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Special Edition 1 | June 2019 Mobility innovations put into action Best practice STRATEGIES The social costs of transport BEST PRACTICE Foster sustainable urban mobility transformation PRODUCTS & SOLUTIONS Passenger needs - advantage and convenience SCIENCE & RESEARCH New trends in transport systems International Transportation International Transportation www.international-transportation.com Special Edition 1 l June 2019 Volume 71 ALL YOU CAN READ Das Archiv der Zeitschrift Internationales Verkehrswesen mit ihren Vorgänger-Titeln reicht bis Ausgabe 1|1949 zurück. Sie haben ein Jahres-Abonnement? Dann steht Ihnen auch dieses Archiv zur Verfügung. Durchsuchen Sie Fach- und Wissenschaftsbeiträge ab Jahrgang 2000 nach Stichworten. Greifen Sie direkt auf die PDFs aller Ausgaben ab Jahrgang 2010 zu. Mehr darüber auf: www.internationales-verkehrswesen.de Trialog Publishers Verlagsgesellschaft | Baiersbronn | service@trialog.de ePaper-EAZ_IV_TranCit.indd 4 11.11.2018 18: 32: 23 International Transportation (71) 1 | 2019 3 Sebastian Belz POINT OF VIEW Eurovision vital - a vital vision of Europe B y the time of writing this editorial the election campaign for the new European Parliament is just about to speed up a little bit. Compared to those campaigns usually accompanying national elections it still remains an also-ran. The people of Europe probably vote for the biggest change to the philosophy of the Continent since decades, but on the surface everybody remains relatively calm and indifferent. Nationalist and anti- European parties are entering the stage, questioning the fundaments of our common living together, but nobody seems very worried. “Brexit? Boring! ” is the usual answer even in intellectual discussions after months of gruelling debates in both, the British and the Continental governments. This attitude is as logical as it is dangerous. It underestimates largely the necessity of finding compromises in a diverse society such as the European Union. The damage to the credibility and reliability of our political institutions already has happened. And if this was not worse enough, our political class lacks any sensitivity to feel the imminent irreversibility of the situation. Everybody tries to follow his personal interest, but the big picture is missing. There is a momentum for Europe today, to change it towards a better, stronger, more open and more sustainable place in the world. Yet, if nobody picks up this chance, we are likely to lose the option for years. It depends on us! What goes along with nationalism today is populism. The massive increase of fake news, alternative facts and public shit storms, encouraged by the new techniques of electronic and mostly anonymous communication is a major threat for fact-based, scientific and objective information. Of course there is nothing wrong in trying to convince people by promoting one’s ideas. Emotional aspects were part of advertising ever since, and this perfectly matches the psychological structure of human beings. But data, information, discourse and dialogue are the cornerstones of every procedure to develop a more and more sophisticated mind and consciousness. The proof, if something is true or false, has to follow strict rules, which were examined by scientists during centuries and chiliads. Denying the human made climate change, the coherence between smoking and lung cancer or between adiposity and fat-rich nutrition are offenses against the human kind - not only against academia -, and have to be resolutely blamed as such! The EPTS Foundation tries to set something against these current distorting developments, at least in the fields of transport and mobility. We are dealing with objective, neutral and independent information in an international, European, sometimes global context. We are covering all transport modes in every European country and wish to publish contributions from scientists of all faculties and disciplines. We are following the principles of sustainability, diversity and pluralism and promote the networking among transport and mobility scientists in Europe. The European Platform of Transport Sciences organises several Congresses and Conferences every year as well as the European Friedrich-List-Award for Young Transport Scientists (see page 38) since almost 20 years. Today the EPTS Foundation covers 25 members, mostly national scientific associations or large university transport institutes, which represent more than 30,000 individuals. Our Executive Board consists of three distinguished scientists from Hungary, the United Kingdom and Switzerland, with the General Secretariat based in Germany. This federal structure ensures the presence of EPTS representatives on both the national as well as the European levels. We believe that Europe is a geographical entity. Especially with regard to transport and mobility there is no possibility or need to re-nationalise traffic flows of goods and people. To acknowledge the historically-based cultural diversity of people in Europe while at the same time having a strong impact in merging processes towards a unified Continent is today´s challenge. We contribute to the irreversibility of this process, but will have to find solutions regarding the right pace. With all its´ heritage and little differences Europe is unique and we regard this diversity as great prototypical chance towards more resilience and stability in an ever-changing world. As your international lobby organisation we are pro transport and mobility sciences, pro Europe and pro humanity. Your contribution to this “Eurovision” is vital. I will be happy to hear from you! Sincerely yours, Sebastian Belz Secretary General European Platform of Transport Sciences - EPTS Foundation e.V. International Transportation (71) 1 | 2019 4 PRODUCTS & SOLUTIONS 16 “On-demand software solutions can help municipalities” Interview with Gunnar Froh, founder and CEO Wunder Mobility BEST PRACTICE 10 Urban development and e-mobility in Malaysia The German Programme “Export Initiative for Green Technologies” Yazmin Stoffer 13 The MobiliseYourCity Partnership An international alliance to foster sustainable urban mobility transformation Markus Delfs Michael Engelskirchen Oliver Lah STRATEGIES 6 Social costs of transport in Switzerland Measuring the impact of transport on the society and quantifying compliance with the polluter pays principle Christian Gigon Alexandra Quandt Photo: Unsplash Photo: Wuppertal Institute PAGE 6 PAGE 13 International Transportation POINT OF VIEW 3 Eurovision vital - a vital vision of Europe Sebastian Belz KNOWLEDGE AT A GLANCE Previously published issues of International Transportation May 2018: Urban Mobility May 2017: Managing Public Transport May 2016: Smarter on the move Oct. 2015: Looking ahead May 2015: Urban transport international-transportation.com International Transportation (71) 1 | 2019 5 CONTENT June 2019 SCIENCE & RESEARCH COLUMNS Photo: iav.de Photo: DLR PAGE 22 PAGE 29 48 Projects in a Nutshell Overview of selected Mobility Research Projects 48 Bringing autonomous driving to-life 48 New breakthroughs in research on super-batteries 49 Particulate matter from aircraft engines affects airways 49 Training data for autonomous driving 50 Traffic prediction system based on neural networks 50 Metallic 3D printing on track for automotive series production 51 FORUM Events 51 Preview: European Transport Conference 51 Preview: InnoRail Budapest 52 Preview: Hypermotion 2019 52 Book launch: Mega Cities - Mega-Challenge 53 IMPRINT | EDITORIAL PANELS 54 REMARK | EVENTS 29 Dominion A realtime middleware for connecting functions in highly automated vehicles Björn Hendriks Christian Harms Michael Kürschner 34 Dwell time forecast in railbound traffic Procedure and first evaluation Johannes Uhl Ullrich Martin 38 New trends in transport systems Random selection of European Friedrich-List-Prize submissions Advanced automation in railway operations Impacts, requirements and potentials Martina Zeiner, Martin Smoliner Challenging assumptions about traveller behaviour The benefits and challenges of using Bluetooth data to examine repeated behaviour Fiona Crawford Risk analysis of dangerous goods transportation Libor Krejčí TSCLab - Traffic Signal Control Laboratory A tool for performance monitoring and evaluation of adaptive traffic signal control in VISSIM Daniel Pavleski 18 Mobile system for road inspection and 3D modelling Introducing novel technology within the project “Digital Roads New Zealand” Sergey Zuev Anko Börner Hongmou Zhang Ines Ernst Martin Knoche Reinhard Klette 22 Secure, helpful, lovable Incorporating user needs in the design of autonomous vehicles systems for public transport Annika Dreßler Jan Grippenkoven Meike Jipp Klas Ihme Uwe Drewitz 26 Products in a nutshell Overview of selected mobility solutions 26 World’s biggest urban ropeway network completed 27 Chances and challenges: Escooters in the City of Portland 27 First cab-less autonomous e-truck in commercial operations on public road 28 Chinese electrical air taxi to be tested in Linz 28 Roadscanners Oy reconcentrates on road business International Transportation (71) 1 | 2019 6 Social costs of transport in-Switzerland Measuring the impact of transport on the society and quantifying compliance with the polluter pays principle Transport economics, External costs of transport, Polluter pays principle, True cost of transport, Unit-costs What is the cost of transport in Switzerland? What are the drivers of transport cost and who pays for it? The “Statistics on the costs and funding of transport” compiled by the Swiss Federal Statistical Office answers these questions for the transport modes road, rail, air and inland waterways. The statistics take into account not only financial expenditures but also the intangible costs of transport-related accidents or damages to health and the environment. Christian Gigon, Alexandra Quandt T he Swiss statistics on the costs and funding of transport (CFT statistics) use a holistic approach to cover all the economic costs caused by transport in the categories infrastructure, means of transport, accidents, and damages to human health and the environment. Adopting the polluter-pays principle, the external costs of transport are also included in the calculations. The chosen full cost approach enables a comparison to be made between the different forms of trans- Zurich tramway . Photo: Bing Hao on Unsplash Total: CHF 89.7 billion Costs of transport by cost category, 2015 © FSO 2019 Source: FSO - Statistics on the costs and funding of transport (CFT) infrastructure means of transport accidents health and environment 59% 12% 17% 13% CHF billion Costs of passenger and goods transport by transport mode, 2015 1 excl. human-powered mobility 2 excl. general aviation © FSO 2019 Source: FSO - Statistics on the costs and funding of transport (CFT) 0 10 20 30 40 50 60 70 80 inland waterways transport air transport 2 rail transport road transport 1 53.4 18.6 8.8 2.2 6.4 passenger transport goods transport 72.0 11.0 6.4 0.3 Total costs including accident, environmental and health costs Figure 1: Costs of transport by cost category, 2015 Figure 2: Total costs of passenger and goods by transport mode, including accident, environmental and health costs, 2015 STRATEGIES External costs External costs STRATEGIES International Transportation (71) 1 | 2019 7 port and the individual cost categories in the interests of costs transparency and thus economic efficiency. The CFT statistics cover the costs of the relevant modes of transport in Switzerland: road, rail, air, and inland waterways. In addition to establishing the total costs of transport and its underlying cost drivers, the statistics also address the matter of its funding. To do this, a distinction is made between direct and final costs bearers (after transfers). Cost bearers are transport users, transport companies, the public sector and the general public. The “general public” is understood as all people who involuntarily bear the burden of negative effects of transport. In contrast to cost-benefit analyses, the CFT statistics look at transport costs in their entirety, comparing the individual cost categories between the different forms of transport. CHF 90 billion for transport in-Switzerland Transport by road, rail, air and inland waterways generated economic costs of a total of CHF 89.7 billion in Switzerland in 2015 (see figure 1). That is just under CHF-11,000 (about EUR 10,000) per capita (EUR 1.00 2015 = CHF 1.07) which was greater, for example, than expenditure on the Swiss health care system. Three quarters of the costs were incurred by passenger transport and a quarter by goods transport. In all areas, motorised road transport costs dominate, accounting alone for four-fifths of total costs. At 12 % and 7 %, rail transport and aviation share a much lower percentage of the costs. The percentage attributable to inland waterways transport in the total transport costs is almost negligible. But in terms of importing goods it occupies an important place, accounting for 11 % of the trading volume. If the individual categories are compared, the majority of costs is due to the acquisition, operation and maintenance of the means of transport (59 %). Transport infrastructure causes 17 % of all transport costs. A Swiss particularity are the high costs for railway infrastructure. They amount to CHF 5.0 billion compared to CHF 8.6 billion for road infrastructure. Costs for transport accidents (12 %) as well as environment and health costs caused by transport (13 %) were much lower (see figure-2). Strong increase in rail and aviation-costs In comparison with 2010, transport costs rose by 4 %. The greatest increases were for rail (+12 %) and aviation (+14 %), while road transport costs stagnated (see figure 3). Factors driving rail transport costs were the substantial investments in infrastructure projects, such as the Gotthard base tunnel in particular but also investments in more frequent trains and new rolling stock. Despite improved efficiency, aviation costs overall continue to rise due to the greatly increased number of flight passengers in Switzerland (+27 %). Highest unit costs in road transport Particularly interesting comparisons can be made by taking transport performance into account. When calculating unit costs (per personor tonne-kilometre), also known as kilometre costs, a distinction should be made by distance group as the level of average costs depends to a large extent on the occupancy rate of the means of transport chosen and the distance travelled (see Index 2010 =100 1 data collected only for 2010 and 2015; excl. general aviation 2 excl. human-powered mobility © FSO 2019 Source: FSO - Statistics on the costs and funding of transport (CFT) road transport 2 rail transport air transport 1 Growth of transport costs by transport mode Total costs including accident, environmental and health costs 2010 2011 2012 2013 2014 2015 95 100 105 110 115 113.6 112.0 102.1 Unit costs of passenger transport, 2015 infrastructure means of transport accidents health and environment Forms of passenger transport with short average distances CHF centimes per person-km public road transport private motorised road transport rail transport air transport 1 Forms of passenger transport with medium average distances CHF centimes per person-km Forms of passenger transport with long average distances CHF centimes per person-km Source: FSO - Statistics on the costs and funding of transport (CFT) © FSO 2019 1 scheduled and charter flights (incl. belly freight), excl. general aviation 100 80 60 40 20 0 100 80 60 40 20 0 100 80 60 40 20 0 12 66 6 4 87 6 29 9 7 51 21 19 3 43 3 3 9 15 Figure 4: Unit costs of passenger transport, 2015 Figure 3: Growth of transport costs by transport mode; total costs including accident, environmental and health costs STRATEGIES External costs International Transportation (71) 1 | 2019 8 figure-4). Forms of transport with long average distances have inherently lower kilometre costs than those covering shorter distances. Each person-kilometre travelled in scheduled and charter flights, for example, costs only 15 centimes. Private motorised road transport cost on average 51 centimes and each person-kilometre travelled by rail cost 43 centimes. In addition to high transport performance in rail transport, the latter is also due to relatively high occupancy rates in trains. 21% of passenger transport in Switzerland is undertaken in public transport by rail and road. Goods transport carried out by heavy road goods vehicles (more than 3.5 t) costs on average 55 centimes, whereas the transport of a tonne of goods by rail costs only 17- centimes. International goods transport on the Rhine is much cheaper at only 7 centimes per tonne transported. In addition to the long distances and the larger tonnage, this is also due to the inherently lower infrastructure costs. CHF 26 billion for passenger cars The largest cost component by far is incurred by the means of transport. For all means of transport together this amounted to CHF 52.6 billion in 2015. Swiss residents spent CHF 26.1 billion alone on the purchase, operation and maintenance of 4.6 million passenger cars. On average, this cor- BACKGROUND Methodological aspects of the Swiss Statistics on the costs and funding of transport (CFT statistics) Coverage The CFT statistics cover the modes of transport road, rail, air, and inland waterways. In principle, the costs caused by transport within Switzerland are measured. However, for aviation and goods shipping on the Rhine, the halfway principle is applied. In international transport half of the costs are charged to the country of origin, the other half to the country of destination. Data sources The CFT statistics are composite statistics that have gradually been developed by federal statisticians since the turn of the millennium. They constitute an extension to an existing survey created in the 1960s to monitor road infrastructure expenditure and the business statistics of the Swiss railways. Findings are based on several complete and partial surveys as well as on a number of model calculations (see figure). Methodology The main guiding principles for the methodology can be summarised as follows: - Utmost priority is given to the comparability of the modes of transport. As far as possible, the same calculation mechanisms are applied to all modes of transport. - Costs are compiled according to the gross cost principle. - For investments (in particular in infrastructure) the perpetual inventory method is used (depreciation and interest costs). - Modelling for the environmental and health costs is done based on a cautious estimate: Costs are only counted if they can be reliably proven using the latest scientific methods. The calculation of the CFT statistics follows a three-stage procedure. 1. Cost levels are calculated for the four cost categories (infrastructure, means of transport, accident, environmental and health costs). The result of this calculation step show how the costs arose. 2. Costs are assigned to the cost bearer that incurred them when they originated (= “direct cost bearer”). The result of this calculation step show who assumed the costs at the time they arose. 3. Due to transfer payments between cost bearers, the absorption of costs may be reassigned. Calculations taking these transfers into account result in the “final cost bearer”. Examples of transfers are: transport-related taxes, transport charges to transport companies and internalisation contributions. The results show who ultimately bore the costs. The three calculation steps show the same costs broken down in three different ways: “cost category”, “direct cost bearer” and “final cost bearer”. The results of the CFT statistics can be expressed either as absolute figures (Swiss francs), as percentages for each breakdown, or as unit costs (per personkilometre or tonne-kilometre). Further information FSO 2019: Statistics on costs and funding of transport, 2015, Federal Statistical Office, Neuchâtel (CH) FSO 2019: Statistics on costs and funding of transport, Methodology report, Version 2.0, Federal Statistical Office, Neuchâtel (CH) www.statistik.ch Main data sources of the CFT statistics Source: FSO 2019 - Statistics on costs and funding of transport (CFT) Private motorised transport (passengers and goods) Public transport Infrastructure Infrastructure Infrastructure costs costs costs Means Means Means of of of transport transport transport costs costs costs Calculation based on import values according to foreign trade statistics Exhaustive survey of transport companies Accident Accident Accident costs costs costs Environmental Environmental Environmental and and and health health health costs costs costs Road transport Rail transport Air transport Inland waterways transport Exhaustive survey of federal, cantonal and communal road infrastructure costs; costs are assigned to vehicle categories on the basis of empirical studies Exhaustive survey of railway companies Partial survey of airlines and airport operators Exhaustive survey of public transport companies, model accounting for freight transport on the River Rhine Model calculation from the Federal Office for Spatial Development External costs STRATEGIES International Transportation (71) 1 | 2019 9 responded to 46 centimes for every vehicle kilometre covered in Switzerland. If the infrastructure costs and accident costs from passenger cars and the transport-related damage to the environment and health are included, the total costs amount to 79 centimes per car kilometre. Environmental damages and health costs up to CHF 12 billion Damage to the environment accounted for 13 % of all transport costs. This is equal to just under CHF 12 billion. The majority of these costs, i.e. 81 %, were caused by motorised road transport. At CHF 3.3 billion, the greatest environmental costs are due to local air pollution in road transport, that is to say from fine particles and carbon monoxide, followed by traffic noise on roads with an estimated CHF 2.1 billion. In absolute terms, road transport is also the greatest polluter. At CHF 1.6 billion the climate costs it causes are twice as high as those caused by Swiss aviation. In relative terms, however, climate costs for aviation are the highest, accounting for 13 % of the total costs of flying. Those costs are not covered by the price of air tickets. With the exception of the Swiss heavy goods vehicle charge (HGVC), no notable internalisation payments are made by transport users in Switzerland. This road charge is the main steering instrument to influence the constitutionally enshrined transfer of goods transport from road to rail. Switzerland is one of the European countries with the highest modal split of rail transport. Nevertheless, road transport rests with a modal split of 63 % the dominant mode of transport. The costs of environmental damage caused by transport are borne almost entirely by the general public. We can thus state that no transport user bears all their own costs. The general public pays too The final cost bearers show how the burden is ultimately shared (see figure 5). In 2015, transport users bore the highest percentage of final costs, bearing 86% of motorised road transport costs. The State bore 2 % in the form of subsidies for public road transport. The remaining 12 % is transferred to the general public in the form of accident, environment and health costs. This 12 % did, however, represent CHF 8.7 billion. That is almost four times the total burden of the other modes of transport together. In aviation, because of the high degree of pollution, the share of user funding was 81 %; for inland waterways transport it was 64 % and for rail transport 46 %. The public sector plays an important part in the funding of rail transport: in 2015 the State assumed around 43 % of the total costs. These costs are generally assumed deliberately by the State, partly due to Switzerland’s legal obligation to provide basic public transport services. Furthermore, in accordance with the road to rail policy, major railway projects such as the Gotthard base tunnel are co-financed by the State. ■ Christian Gigon Scientific Officer, Mobility Section, Federal Statistical Office FSO, Neuchâtel (CH) christian.gigon@bfs.admin.ch Alexandra Quandt, Dr. Scientific Officer, Mobility Section, Federal Statistical Office FSO, Neuchâtel (CH) alexandra.quandt@bfs.admin.ch gi-e-11.02.01-2019 Financing of road infrastructure Costs of human-powered transport How costs are incurred... ... and who foots the bill. Confederation 2 CHF bn 4 6 8 Cantons Communes Source: FSO - Statistics on the costs and funding of transport (CFT) Costs and funding of transport 11 Mobility and transport © FSO 2019 2015 www.statistics.admin.ch CHF 89.7 billion Total costs of motorised transport (+4% since 2010) Infrastructure�10% (CHF 1.1 billion) Means of transport�6% (CHF 0.6 billion) Accidents 83% (CHF 8.7 billion) Environment and health 1% (CHF 0.1 billion) CHF 11.0 bn Passenger transport Goods transport 1 2 3 4 80% 12% 7% 77% 23% 0.4% Motorised road transport Rail transport Rail transport Air transport Inland waterways transport Infrastructure Means of transport Environment and health Accidents Transport users State General public Railway enterprises State revenue from road transport (e.g. petroleum tax) State expenditure on road infrastructure CHF 72.0 bn Motorised road transport 0% 100% 0% 100% 1 2 3 4 72.0 11.0 Figure 5: Costs and funding of Swiss road and rail transport, 2015 @EuTransportConf #etcdublin2019 www.aetransport.org AET European Transport Conference (ETC) 3-Day Booking Discounts: Deadline 28 th June The 47 th European Transport Conference Annual Conference of the Association for European Transport 09-11 October 2019 Dublin Castle, Ireland Delegates are now invited to book their place at the European Transport Conference to benefit from an Early Booking Discount. The Early Booking Discount applies to delegates booking 3-day attendance only, with payment received by Friday 28 th June 2019: Standard Fee* Early Booking Fee* AET or ECTRI Indiv. Member £795 €875 £720 €800 AET or ECTRI Org. Member £760 €840 £690 €765 Non-Members £930 €1125 £850 €945 Single days may be booked from 1st July 2018. In addition, a 50% discount on booking fees is applicable to attendees from new EU Member States (joined since 2004) and for young professionals under the age of 26 or with less than 5 years’ professional experience. Full time students also receive a generous discount. For those involved in transport planning, research and practice, the European Transport Conference is the event to find in-depth presentations on policy issues, best practice and research findings across a broad spectrum of transport modes. To secure your discounted place, please book here: www.aetransport.org or email: sabrina.winter@aetransport.org *All fees shown are subject to 20% VAT. Standard Fees may be subject to alteration due to fluctuating exchange rates. J000237 Early bookings Internationales Verkehrswesen advert 88x126 v2.indd 1 18/ 03/ 2019 13: 13 International Transportation (71) 1 | 2019 10 Images: AHK Malaysia BEST PRACTICE Urban Mobility Urban development and e-mobility in Malaysia The German Programme “Export Initiative for Green Technologies” Urban development, E-mobility, Environment Increasing population and urbanisation of major cities are creating opportunities for development and more sustainable living. However, it exerts significant pressure on infrastructure and resources. Cities need to adapt change to improve air quality, reduce congestion and provide clean energy to their population. Urban planning policies need to develop to make the most of e-mobility and improve the urban ecosystem. Thus, e-mobility can achieve climate goals. The AHK Malaysia held a series of workshops for German speakers to share knowledge with Malaysian players in related fields. Yazmin Stoffer C onsidering the increasing population and urbanisation of major cities such as Georgetown in Penang and Kuala Lumpur, urbanisation is creating significant opportunities for social and economic development and more sustainable living. However, urbanisation is also exerting significant pressure on infrastructure and resources as well as contributing to climate change. Cities need to adapt change in energy, mobility and consumption to improve air quality, reduce congestion and provide clean and reliable energy to their growing population. In Malaysia 93 % of all households own a car. Of these, 54 % own even more than one car. In 2016, the transport sector alone contributed a 42 % share of energy consumption and CO 2 emissions (see figure 1). In response to these problems urban planning policies aim to develop sustainable strategies to adapt an integrated and assertive approach. Electric vehicles (EVS) have a great potential in the context of curtailing greenhouse gas emissions within the transport sector and as a solution to improve the quality of urban ecosystem. Furthermore, electrification of cars is a game changer in achieving climate goals. Malaysia as an automotive/ producing nation with its own National Automotive Policy (NAP), has one of the most ambitious automotive and mobility policies within the region, aiming to increase domestic participation in high value technology within its local industry through the implementation of development programmes in line with Industry 4.0. “The new path of the NAP review will address the elements of the current NAP focusing on various measures required to enhance the competitiveness of the industry with future technological trends”, said YB Dr. Ong Kian Ming, Malaysia’s Deputy Minister of International Trade and Industry. As the Malaysian automotive industry moves towards electric vehicles and new technologies for an eco-friendly future, it offers great potential for German companies. Germany is well-known for its dedication and success in renewable energy, and International Transportation (71) 1 | 2019 11 Urban Mobility BEST PRACTICE could provide insight on the e-mobility ecosystem. “Malaysia aspires to develop a mobility ecosystem with a comprehensive, well planned and intelligent mobility infrastructure, with talent development and business capacities to successfully implement such goals. Germany’s history of sustainable technological innovation is a great example for Malaysia to follow”, said Malaysia Automotive Robotics and IoT Institute CEO, Dato’ Madani Sahari while speaking to the media. In comparison to other countries within ASEAN, Malaysia has the lowest gap between its electricity and petrol & diesel prices (see figure 2). With currently approx. 90% share of fossil energy sources in electricity production, a short-term increase in the share of electrically powered vehicles would have no environmental impact. The combination of mobility (in particular motorised private transport) and the environment is undoubtedly highly relevant in Malaysian cities and is attracting a great deal of attention in the new government and administration as well as in the private sector and civil society. Against this background, under the German Programme “Export Initiative for Green Technologies” the Malaysian-German Chamber of Commerce and Industry (AHK Malaysia) held two workshops on electro-mobility. Since 2016 the German Chambers of Commerce and Industry Abroad (AHKs) have conducted projects under this initiative in 20 countries in the fields of waste and water management and sustainable mobility. They connect stakeholders worldwide, foster discussions of solutions and identify points of contact for mutual projects. The Source: Biennial Update Reports (BURs) 2016, BUR2 2018, NEB 2015 Figure 1: Progress of energy consumption and CO 2 emissions in Malaysia (transport sector: red) Source: Biennial Update Reports (BURs) 2016, BUR2 2018, NEB 2015 $- $0.50 $1.00 $1.50 $2.00 $2.50 Malaysia Thailand Singapore Indonesia Philippine United Kingdom Japan Norway Germany USA South Korea Petrol Diesel Electricity Lowest & smallest gap to electricity price Data sourced from https: / / www.globalpetrolprices.com/ Figure 2: Gap between electricity and prices for petrol and diesel in different countries Data sourced from https: / / www.globalpetrolprices.com/ Figure 3: Workshop on Mobility 4.0, headed by Rauno Fuchs Partners involved - Malaysian-German Chamber of Commerce and Industry (AHK Malaysia), www.malaysia.ahk.de - Ministry of International Trade and Industry (MITI) - Ministry of Energy, Science, Technology, Environment & Climate Change (MESTECC) - Malaysia Automotive Robotics and IoT Institute (MARii) - German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) - Green City Experience GmbH - Chambers for GreenTech, DIHK Service GmbH - eeMobility GmbH - ebusplan GmbH International Transportation (71) 1 | 2019 12 BEST PRACTICE Urban Mobility Association of German Chambers of Commerce (DIHK) coordinates the project line. The Export Initiative for Green Technologies funded by the German Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) is based on the idea, that innovations in environmental protection, climate action and resource efficiency offer growth opportunities and lead to a more sustainable future. It focuses on the dissemination of technology, know-how and integral solutions. The workshops on e-mobility The timing for the workshops in Malaysia was opportune. In 2018 the Malaysian government changed for the first time in more than 60 years. The Malaysian pendant of the German BMU, the Ministry of Energy, Science, Technology, Environment & Climate Change (MESTECC) is led by the young and motivated Minister Yeo Bee Yin: She is ambitious in her pursuit of various goals such as increasing the share of renewable energy, fighting against plastic waste and the reduction of CO 2 emissions in the energy and transport sector. The German speakers corresponded well to the interest of the Malaysian participants. One of the main themes revolving around the workshops were Mobility 4.0: Setting the Scene for the City of Tomorrow, where Rauno Fuchs, CEO of Green City Experience GmbH, presented German concepts as well as case studies and elaborated on possible e-mobility solutions for Malaysian cities (figure 3). During the breakout session concepts for smart mobility solutions were developed as well as steps to creating an action plan for e-mobility to become the preferred mode of transport. Another main focus was set on electrified public transportation - The Connectivity Issue, Moving Low Carbon Transportation Forward. During this session Philipp Sinhuber, cofounder and Managing Director of ebusplan GmbH, shared concepts for the electrification of the local public transport (figure 4). This session was particularly significant for local bus operators to learn about different electric bus concepts that are available on the market alongside the vehicles and battery systems in addition to charging equipment. In depth concepts for smart mobility solutions - steps to creating a shift from conventional bus fleets to e-bus fleets were mapped out. Further the complexity of e-bus systems (vehicle + charging infrastructure) were elaborated according to the battery capacity and charging infrastructure. In smaller working groups, participants were trained to create operation and charging schemes for different fleet sizes taking into consideration 1. the service trip; 2. deadhead trip; 3. charging at depot; 4. break time; 5. delay buffer. The workshop clearly showed that the citizens as well as the bus operators are keen and open to the emobility shift. However, funds from the government are necessary to accommodate the hardware (charging stations, which require a full redesign of the bus depots) as well as software solutions that can collect and analyse driving-profile and the vehicle health-data. In conclusion, the e-mobility market seems to be at a very early stage at the moment. However, Malaysia’s technological openness, its pioneering role in the ASEAN countries and its own history in automotive engineering suggest that the Malaysian e-mobility market will develop rapidly. ■ Yazmin Stoffer Manager - Projects, Malaysian-German Chamber of Commerce and Industry, Kuala Lumpur (MY) yazmin.stoffer@malaysia.ahk.de BY THE WAY Malaysia on the rise A statement from Rauno Fuchs, CEO of Green City Experience GmbH Malaysia is on the rise. You can definitely see and feel that in the vibrant streets of Kuala Lumpur and Penang. What is well desired economically, is quite challenging for the growing cities and their urban development and traffic systems. There is already a lot of pressure on the mobility infrastructure and building new roads alone won’t solve the problems of traffic congestions, a city scenery that is dominated by cars, poor air quality and noise issues. With those challenges in mind, the workshops in Kuala Lumpur and Penang discussed new approaches in sustainable urban mobility planning and the potentials of e-mobility. There was a clear consensus throughout the German and local experts: there is a general need to reduce individual car traffic to further enhance urban sustainability, tackle air quality issues, and to generally improve the quality of life in Malaysian cities. E-mobility will be a big part of the solution, alongside the urgent enhancement of the public private transport system and - though currently not at all represented in the streets - cycling. In both workshops three things were addressed very clearly by the local experts: 1. There is a time-sensitive need for an explicit political will to change towards a sustainable traffic system before problems worsen. 2. Campaigns and education for the general public are needed to promote e-mobility and sustainable lifestyles. Therefore, local NGOs, sustainable mobility planners, associations as well as industry forerunners in the e-mobility field should be strongly supported by local authorities. 3. External support from the AHK Malaysia, industry partners and urban mobility experts is well appreciated to accelerate change towards a sustainable future. Figure 4: Workshop on local public transport, headed by Philipp Sinhuber International Transportation (71) 1 | 2019 13 Mobility Transformation BEST PRACTICE The MobiliseYourCity Partnership An international alliance to foster sustainable urban mobility-transformation Urban development, Sustainable mobility, Transport climate change, SUMP, NUMP Urban mobility is considered a critical success factor with respect to economic efficiency and prosperity of cities; it enables access for people to education, jobs, health facilities etc., and is a key factor for quality of life in a city, both in a positive and negative way. Particularly high urbanization and motorization rates in many emerging and developing countries point at the importance to drastically shift from car-focused development pathways to the promotion of more sustainable mobility solutions, such as mass-rapidtransit, public transport in general, or non-motorized transport. Digitalization and new mobility concepts play an important role in these countries. The MobiliseYourCity Partnership seeks to connect local and national governments, experts and financing institutions from various regions to build networks and jointly work on effective transformation strategies and policies towards a sustainable and climate-friendly future. Markus Delfs, Michael Engelskirchen, Oliver Lah I n 2018, the transport sector emitted more than 8 Gt of CO 2 - approximately 23 % of global energy-related Greenhouse-gas emissions - with a trend to approximately 14 Gt in 2050. However, to slow down warming of the global climate and complying with the internationally agreed target to limit warming to 2 °C, current emissions would have to be cut half of today´s level, namely to approximately 4 Gt p.a. Globally more than 80 % of national transport emissions derive from road transport, a large share of it from urban transport. Forecasts project strong growth of transport emissions, particularly in developing and emerging economies. Due to rapid urbanisation, lack of urban planning and growing motorisation rates, urban transport is becoming one of the main sources of greenhouse gas emissions, but also a key contributor to air pollution and local road safety concerns. Supporting a transition towards a sustainable urban mobility system is a key diver for international cooperation in the transport sector. Investing in sustainable transport infrastructures and technologies will be more cost-effective in the long-run and will also address key policy issues, such as access, health, productivity and safety. However, the economic and political environment in some countries may be challenging and can affect the efficiency and pace of the shift towards sustainable mobility solutions. The planning, implementation and enforcement of a sustainable, low carbon transport system that provides ‘access to safe, affordable, accessible and sustainable transport systems for all’, as outlined in the Sustainable Development Goals, requires effective institutions, a sound and stable flow of financial resources, and administrative capacities. The European Commission, France and Germany have identified that challenge. They have been providing financial, technological, and capacity building assistance through development finance institutions to emerging economies - i.e. by means of grants or loansto support the establishment of sustainable infrastructure and services. Such support is often fuelled through ordinary development assistance commitments, or through international commitments aligned with the New Urban Agenda or UNFCCC´s climate agenda. In many cases, such support has already helped governments to improve urban mobility. However, development finance institutions such as the European Bank for Reconstruction and Development (EBRD), the French Development Bank (AFD), the German Development Bank (KfW), and many others increasingly face difficulties to close financing agreements. Easily “bankable” projects (low-hanging fruits) have already been implemented and become scarce, whilst the remaining challenges - often in countries and cities which face the most pressing challenges - are not as easy to tackle. Project ideas lack robustness with respect to financial viability, technical matureness, institutional anchorage, project management capacity or political will. As a result, a group of institutions embarked in 2015 and launched the MobiliseYourCity Partnership, to install a technical assistance alliance as incubator to facilitate above financial assistance. The approach The MobiliseYourCity Partnership takes an active approach in supporting the sector transition and works on an approach that covers three main pillars: • Raising the ambition level of local governments in sustainable urban mobility through forming coalitions, backed by effective, long-term south-south / northsouth partnerships and cooperation measures; • Linking targets: Breaking down the countries’ climate protection commitments into mobility sector targets and connecting these with so-called “co-benefits”, which are politically most relevant and indispensable for diligent implementation and follow-up particularly at International Transportation (71) 1 | 2019 14 BEST PRACTICE Mobility Transformation sub-national level: i.e. effects related to traffic safety, urban health, jobs, etc.; • Robust linkage of integral policy and planning approaches with finance & investment: Emphasis is crucial to establish integrated, comprehensive policies and development plans for sector transformation with clear linkage to local and national budgeting and third party financing concepts for activating transformation. Against these overarching principles on the technical side three levels of intervention have been defined for MobiliseYour- City: • City Government Level: Sustainable Urban Mobility Plans (SUMPs): SUMPs have been developed within the European Union as essential tool for city governments to roll out their sectoral action planning. MobiliseYourCity’s SUMP methodology narrows down the methodological broadness of SUMPs towards particular needs of its partner countries, and with particular emphasis on action planning, investment & financing, quantitative targeting of GHG-reduction, as well as participatory planning approaches to connect to private-sector stakeholders. • National Government Level: National Urban Mobility Policies and Investment Plans (NUMPs): National Urban Mobility Policy and Investment Programmes are strategic, action-oriented frameworks for urban mobility, developed by national governments, enacted to enhance the capability of cities to plan, finance and implement projects and measures designed to fulfil the mobility needs of people and businesses in cities and their surroundings in a sustainable manner. • Global / regional level: Establishment of a global Community of Practice, which connects Partners & sector stakeholders; provision of related Capacity Development & International Learning offers related to above topics. SUMP or NUMP projects take partner governments approximately 3 years to complete a first round, before an aspired continuous maintenance and advancement stage is reached; each project is supposed to result into actual investment tendering and establishment of related infrastructure and mobility services. Current status As per summer 2019, 51 cities and 12 national governments spread over Africa, Asia, Eastern Europe and Latin America are Partners. 28 cities receive support by the Partnership´s Contributing Partners in drafting SUMPs, E Contact@MobiliseYourCity.net www.MobiliseYourCity.net Twitter: @MobiliseCity Mozambique Madagascar Namibia Kenya Ethiopia Cameroon Togo Burkina Faso Ghana Brazil Uganda Senegal Algeria Tunisia Morocco Ukraine Jordan Albania Bosnia and Herzegovina Macedonia Montenegro Kosovo Serbia China Vietnam Thailand Indonesia Philippines Sri Lanka India Ecuador Peru Colombia Jamaica Guatemala Mexico Dominican Republic Ivory Coast Contributing Partners Validated Beneficiary Partners Partnership process ongoing Current status of partnership Source: MobiliseYourCity AT A GLANCE The MobiliseYourCity Partnership The MobiliseYourCity Partnership is a global and inclusive network of cities and countries as well as an umbrella brand of European development cooperation particularly related to the field of sustainable urban development. The Partnership aims at assisting beneficiary partners - i.e. national and local governments - in their preparation of National Urban Mobility Policies and Investment Programs (NUMPs) and Sustainable Urban Mobility Plans (SUMPs). Furthermore, it is a global alliance for integrated urban mobility planning in emerging, developing and EU neighborhood countries, and an international mobility flagship under the UN Marrakesh Partnership for Global Climate Action. It is a multi-donor action, jointly co-financed by the European Commission’s Directorate-General for International Cooperation and Development (DG DEVCO), the French Ministry of Ecological and Solidarity-based Transition (MTES), the French Facility for Global Environment (FFEM), and the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU). The Partnership is implemented jointly by its implementing partners ADEME, AFD, CEREMA, CODATU, EBRD, GIZ, KfW, and Wuppertal Institute. Besides contribution to the international climate process, it contributes to the UN’s Agenda 2030, specifically Sustainable Development Goal (SDG) 11: Make cities inclusive, safe, resilient and sustainable. More information: www.MobiliseYourCity.net International Transportation (71) 1 | 2019 15 Mobility Transformation BEST PRACTICE and 7 national governments are supported in drafting NUMPs. A glance into the Partnership’s work on the ground: Promotion of e-urban mobility in-Uruguay Thematic focus The national government of Uruguay seeks to develop a national framework which allows the local government to promote electric urban mobility planning that is national in scope and includes the development of technical, regulatory and financial instruments. Together with selected cities the project will elaborate technical guidance to promote e-mobility, raise awareness about the need of define sustainable mobility policies, empower and build capacity among cities actors to steer their own transition to e-mobility. Challenge The competencies of the national level in urban mobility are limited; hence, the involvement of the local governments in the development of the NUMP is crucial. Elections on local and national level could lead to changing priorities. The project involves partners and stakeholders from national and local level as well as public and private sector to put the NUMP on a broad base. Expected completion GIZ together with the Uruguayan counterparts from the Ministry of Industry, Energy and Mining, the Ministry of Housing, Territorial Planning and Environment and the Ministry of Transport and Public Works will develop the NUMP in a 2-year’s process and completed by the end of 2020. Assistance to Uruguay is implemented via the EUROCLIMA+ Program by the MobiliseYourCity Contributing Partners GIZ and AFD (funded by the European Commission together with the German Federal Ministry for Economic Cooperation and Development). Practical example: E-mobility demonstration to support Comprehensive Mobility Plan in the city of Kochi / India Thematic focus The Indian e-mobility sector has seen steady growth in momentum, especially since the central government of India has a target to electrify vehicles more than 30% by 2030. The central Government has various plans and policies addressing e-mobility in India. Kochi Municipal Corporation (KMC) drafted its Comprehensive Mobility Plan (CMP) in 2007 which recommends several shortand long-term sustainable urban transport proposals. MobiliseYour- City partners are working in Kochi to support CMP on the development of local implementation concept on e-mobility. The project plans to replace fossil-fuelled 3-wheelers (Tuk-Tuks) with electric ones and aims at expanding the adoption of e-mobility in Kochi and focuses on improving the city’s first and last-mile connectivity and reducing air and noise pollution. Challenge The operation of E Tuk-Tuks is at the early stage in the city. Therefore, to demonstrate its economic viability, E-Tuk-Tuk manufacturers and drivers will be involved in the demonstration project to carry out E-Tuk- Tuk deployment campaign in the city. E-Tuk-Tuk drivers are provided funds for certain period of time by reducing renting price in order give an overview of cost-benefit of using E-Tuk-Tuks. Expected completion The pilot initiative will start with a small fleet of 10 to 15 E-Tuk-Tuks by 2019 to test operations, user acceptance, costs and benefits. This fleet is planned to be operated in either the Fort Kochi or Mattancherry area, with the local implementation partners are Kochi municipality, Kochi Metro Rail Limited (KMRL), together with Kerala State electricity board (KSeb) and E-Tuk-Tuk manufacturers. Complementary assistance activities in Kochi are implemented by the Mobilise- YourCity Contributing Partners Wuppertal Institute (funded by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety), AFD (funded by the European Commission), and GIZ (funded by the German Federal Ministry for Economic Cooperation and Development). ■ Michael Engelskirchen Head of urban mobility component, Euroclima+ project, which supports climate change mitigation in Latin America, Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ), Eschborn (DE) michael.engelskirchen@giz.de Oliver Lah Coordinator Urban Electric Mobility Initiative (UEMI) and Urban Pathways programme, Head of the Mobility and International Cooperation Unit, Wuppertal Institut für Klima, Umwelt, Energie, Wuppertal (DE) oliver.lah@wupperinst.org Markus Delfs Coordinator MobiliseYourCity Partnership, Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ), Brussels (BE) markus.delfs@giz.de Kochi, India Photo: Wuppertal Institut Montevideo, Uruguay Photo: GiZ International Transportation (71) 1 | 2019 16 PRODUCTS & SOLUTIONS Mobility on demand “On-demand software solutions can help municipalities” Digitalization and automatization are regarded as the royal road to a convenient on-demand mobility. However some solutions still seem to be isolated applications: limited in their range and not really made for the everyday needs of passengers. Other technology solutions are wide in use and it seems that they could fit the wants of both the operators and the customers better. But what is essential? Answers by Gunnar Froh, founder and CEO Wunder Mobility. International Transportation - Mr-Froh, you founded Wunder Mobility as a technology supplier for digital end to end mobility services. What is the approach - what are the specific features? Gunnar Froh - Wunder is the fastest growing mobility-tech company in Europe that provides technology, that enables cities and companies to launch new mobility services and be able to create more efficiency to their businesses. We are the only global company that conceptualizes and scales all variations of new mobility services as part of its technology and aim to offer the entire spectrum of mobility services from a single source. What are the solutions to achieve this aim? There are three options. Wunder Carpool is an innovative technology solution for ridesharing and commuter carpooling that aims to reduce traffic density and improve air quality levels in cities. It is a peer-to-peer platform that allows to share empty seats in your car, e. g. on the way to work. Wunder Shuttle is an integrated software solution for all “shuttle-on-demand services”, which can be integrated seamlessly with existing means of transport. It allows operators to create an on-demand Taxi or ride sharing system. The third product is Wunder Fleet, that delivers the technology to power all sharing services in the mobility ecosystem ranging from bikes to electric scooters and cars. From an operator perspective, we provide all the tools to be able to manage a vehicle sharing business from end-to-end. Let us start with fleet management. Free-floating carsharing pioneers like car2go and others are long experienced in this field. What’s the difference in your conception? Car2go created a really good in-house solution that is tailored for their business and it suits perfect their current needs. We developed Wunder Fleet in close collaboration with over 30 customers and followed a vehicle agnostic approach. We built our fleet technology to add all kinds of vehicles with an unpaired feature set, which is the broadest on the market and is open for new integrations. No application is built perfectly to suit everyone’s unique needs, but it is important for us giving our customers easy access which allows them to use our product in a way that best suits their needs. So they can easily add business tools or their own frontend application … … meaning that the solution is taylorable to any means of transportation - even with future vehicles? We even integrated a mobility hub with multiple vehicles in out back-end which allows an operator to choose from multiple options. As we don’t see the big OEMs in the software business in the long term, we want to become the best software provider for mobility worldwide. In the future, complex mobility services, just like vehicles, will be the result of a value chain in which system suppliers play an important role. The international, entrepreneurial and data-driven culture of our company enables us to build and further develop the software and operating system for this ecosystem faster and better than large OEMs are able to. Our technology is used in more than 100 cities on five continents to power more than 100,000 vehicles - cars, scooters and kickscooter - facilitating over 12 million trips worldwide. In contrast to Wunder Fleet the Carpooling solution addresses the end customer as a peer-to-peer system? Yes, Carpooling is a P2P solution. General operations are needed to run a market - community management, operations support - but overall it is a community from users for users. Payments can be made in cash and electronically through our integrated Wunder Wallet. It is also possible to add fees to both payment solutions. Is the Carpooling technology field-tested and how does it perform in practice? Wunder Carpool has already been launched in 7 different markets and over 5 million Source: Wunder Mobility International Transportation (71) 1 | 2019 17 Mobility on demand PRODUCTS & SOLUTIONS carpools have been processed through it. It makes a huge impact in the markets where we operate at scale. For example in Manila we take up to 50,000 cars of the street through carpooling. Cities that have carpooling solutions can drastically reduce CO 2 emissions and traffic congestion, without any infrastructure investment or adding vehicles on the streets. In New Dehli one of the most polluted cities in the world, we helped reduce CO 2 levels by 20 tons a month. Connecting existing services should be an effective possibility to pull more car drivers from sitting in traffic jams to convenient public transport. How will Wunder Shuttle play a role in this matter? Wunder Shuttle is a demand-oriented mobility solution. The software supports operators to optimize and tailor their existing mobility offerings to the end-customers needs and market demands. Besides many factors that are needed to change people’s behaviour away from their single car usage to using pooled transportation, is the offering of a worthy alternative that is easier and more comfortable to use. Wunder Shuttle is continuously being developed on the basis of experiences gained from the mobility market of pooling passengers and drivers. Integrating a new solution into existing offerings needs the close collaboration of the public operators and digital service providers to combine their knowledge about the end-customers needs. What have passengers to do to get the benefits, how are the procedures? By using the Shuttle app, passengers can plan their trips, find the best possible routes and book these. In addition, the app allows the user to follow the selected journey in live mode in order to identify possible delays ahead of time. And how does it work on the operators side? Operators are looking at a one-time setup fee and monthly vehicle fees for the software which ultimately helps reducing overall operating costs through the savings in efficiency improvement. What are your experiences with the Shuttle solution up to this point? Multiple scenarios have shown overwhelming results. One of them supporting a local shipping company with the first & last-mile solution in the harbour of Hamburg in order to ease the daily commute of their employees to or from the nearest public transportation station to their premisses. Offering an additional service helps increasing employee satisfaction and encourages using public transportation in order to reduce parking space necessity for the employer. By relying on the knowledge and expertise of local operators and enhances their operations, Wunder Shuttle does not increase the number of vehicles running in the streets but optimizes the amount of passengers utilizing the vehicles. Through the technology multiple use-cases can be implemented besides first/ last mile solutions for example corporate & campus transit, employee & school transportation, hotel & airport transfer, tourism & event shuttle and many more. We talked about applications in urban areas with a great variety of public transport offerings. But what about the benefits specifically of Shuttle and Carpooling in the hinterlands with none or hardly any public means of transport? A combination of several offerings is the key for successful mobility to supporting more rural areas in the outskirts of cities. The necessity for on-demand mobility is much higher in areas where public transportation is failing the end-customers’ needs and can be used precisely for this purpose. Ondemand software solutions can help municipalities to evaluated their mobility need and offer alternatives, especially to inhabitants that are no longer able to commute in their own vehicles. www.wundermobility.com ■ Wunder Carpooling app Source: Wunder Mobility Carpooling in Manila Source: Wunder Mobility International Transportation (71) 1 | 2019 18 PRODUCTS & SOLUTIONS Maintenance Mobile system for road inspection and 3D modelling Introducing novel technology within the project “Digital-Roads New Zealand” Road inspection, Sensor systems, Optical navigation, Computer vision, 3D modelling of roadsides Regular inspections and the maintenance of roads support traffic safety. Inspection technologies may benefit from latest developments in sensor systems, camera technology and computer vision. The paper discusses the application of novel mobile technologies, including stereo vision and visual odometry, for modelling and analyzing extensive segments of roads. Applications of the developed system have been evaluated at test sites in New Zealand within an international collaboration project entitled “Digital Roads New Zealand”. Sergey Zuev, Anko Börner, Hongmou Zhang, Ines Ernst, Martin Knoche, Reinhard Klette T he inspection of road infrastructure or of road surfaces requires exact localization of detected road defects on a 2D map or in a 3D model. Developments in advanced driver assistance [1] or autonomous driving [2] depend on accurate 3D road and roadside models, for example for sensor testing or environment-based vehicle guidance. Such models can be generated by different sensor technologies which can be placed on various platforms. Lidar, radar or camera systems mounted on satellites, airplanes, UAV’s or cars gather data which are mapped automatically into environment models. These models can be used for information, assistance and control systems. Model parameters (e.g. online vs. offline generation, spatial resolution, coverage) depend on the given application. The research project “Digital Roads New Zealand” has been defined for meeting and understanding such needs in the context of developing a transport technology test site near Whangarei (New Zealand). This paper reports about current research in this project which is undertaken by three partners. The Institute for Optical Sensor Systems [3] at the German Aerospace Centre (DLR) developed an integrated positioning system (IPS) which was installed on a car for providing precise trajectories of the vehicle [4]. Data were recorded by a stereo vision system and an inertial measurement unit (IMU). Additionally, the data was used for modelling sections of the road from 3D point clouds generated by the IPS. The Centre for Robotics & Vision (CeRV) at Auckland University of Technology contributes to computer vision solutions and dynamic visualization [5]. The Northland Transport Technology Testbed (N3T) is developing road safety solutions for modern trucks [6] on rural roads and is preparing for driver assisting systems. In this paper, these three partners demonstrate the analysis of data collected with the IPS system during a recent measurement campaign under real-world conditions in Northland, New Zealand. Case studies have been defined for urban environments (town basin of Whangarei), a new subdivision (Marsden City) and a rural road (Otaika Valley Road). In this project we were able to perform large-scale 3D roadside modelling (for networks of roads or for 10 to 15 km long road segments) and also to demonstrate opportunities for road inspection. Integrated Positioning System The IPS is a low-cost stereo-vision-aided inertial navigation system (figure 1, left) that has been developed at DLR [7]. The IPS measures seamlessly a motion trajectory in unknown indoor or outdoor environment without any previous knowledge of the environment. Based on the measured trajectory and on computer-vision methods, following applications like 3D scene reconstruction, map-building and object localization can be achieved. The IPS consists of a stereo-camera system (Prosilica GC1380H) with a 45 cm base line and an inertial measurement unit IMU (ADIS-16488). The cameras can record Figure 1: IPS system installed on the car (left). Measurement vehicle (middle). Oncoming truck traffic during measurement campaign on rural road (right). All figures: DLR International Transportation (71) 1 | 2019 19 Maintenance PRODUCTS & SOLUTIONS CCD-progressive 1,360 x 1,024 stereo images at up to 30 frames per second (here used at 10 fps), with a focal length of 8.2 mm. For the IMU, both gyroscope and accelerometer have a bandwidth of 330 Hz, with a bias stability of 6.25 °/ h and 0.1 mg, respectively. The system includes also a low cost GNSS sensor. For more details about the IPS see [8] and [9]. Trajectory estimation The trajectory of the moving IPS is calculated by fusing IMU measurements with visual odometry, i.e. by using the recorded stereo camera data for detecting pose changes over time. The IMU used is a robust, self-contained sensor which means that the measurement does not relay on any (additional) outside information. The high sampling frequency of the IMU allows high spatial resolution when it comes to road defect detection. The IMU outputs six degrees of freedom motion information between two subsequent IMU frames, which includes acceleration and angular velocity. The estimated motion trajectory of the system, based on IMU integration only, is erroneous due to random walk noise in the measurements; this can cause that the error of integrated IMU measurements grows unboundly. In practical applications, an IMU is needed to be combined with other sensors (like cameras) to form a complementary system. A camera system (or visual odometry) also has its challenges. Solely camera-based practical applications are often limited by low resolution, texture issues, and sensitivity with respect to environment illumination. Visual odometry (VO) alone cannot work accurately in highly dynamic working environments (e.g. caused by incorrect feature matching due to motion-blurred images). Therefore, it is common to combine cameras with IMU aiming at a significant improvement of performance by an integrated system. In the IPS system, pose measurements from VO and from the IMU system are fused by using an error-state Kalman filter [10]. The output of the Kalman filter is finally considered as being the motion trajectory of the IPS system, and is then used in our point cloud generation step. In the IPS system, the IMU operates at a very high frequency and is able to reflect the state of the system. Therefore, the IMU measurements (strapdown) are considered as the basic reference of the system state. In addition to the IMU, other sensors synchronously measure the motion of the system but in different coordinates. To fuse the measurements of different sensors, all measurements are transformed into global Earth-centered, Earth-fixed (ECEF) coordinates as show in figure 2. Next, by taking differences between VO and IMU, or GPS and IMU as input, the error state of the system can be updated by the error-state Kalman filter. Finally, by adding the filtered error state to the strapdown output, the optimized system pose is obtained. At the end, the coordinates of the system pose is transformed back into geodetic Latitude, Longitude, Altitude (LLA) coordinates as the final moving trajectory of the system. 3D point cloud generation The IPS with its time-synchronized stereo camera system records two images at exactly the same time. They are used for reliable VO based on sparse sets of 3D points; they also serve for the generation of dense depth maps and resulting local 3D point sets for each recorded image pair. Numerous 3D point sets from image pair sequences can be fused using the calculated IPS attitude data. Subsequently, the point clouds from all image pairs can be merged into a high-density cloud and filtered into a voxel grid of an appropriate resolution and size. A large-scale 3D cloud of points for the entire observed area can be generated; see figure 3. Obviously, this approach strongly depends on a very high accuracy of all contributing modules. A deep understanding of all system components and accurate calibration are essential; we developed a chessboard-based robust calibration approach that is described in detail in [11]. For a fixed camera set-up, the depth resolution and local accuracy of 3D points is mostly determined by the stereo camera parameters, especially the pixel resolution and base length, and the minimum distance of the shown objects to the camera while passing. However, the global point cloud accuracy is directly correlated with the trajectory accuracy. In case of low light conditions with dark and especially very noisy images extracting dense depth maps may require some additional effort, as described in [12]. 3D point cloud generation is usually done in a time-consuming post-processing step with several specialized software packages. The IPS system approach allows 3D point generation in real time. Therefore we adapt the frame rate for point cloud generation dynamically to the camera movement. The computationally most expensive algorithm for dense stereo matching is optimized for the execution on a graphics processor unit (GPU), written in the platform and vendor independent programming language OpenCL. Based on former developments [13] and experiences with various cost functions [14] we use a semi-global matching algorithm with a census cost function. All steps, from the trajectory estimation up to the 3D point filtering to a voxel grid, + - VO IMU Strapdown Error State Filter Pose Estimation Pose in ECEF GPS LLA to ECEF Local to ECEF Local to ECEF - ECEF to LLA Pose in LLA Figure 2: Block diagram for IMU, VO and GPS integration International Transportation (71) 1 | 2019 20 PRODUCTS & SOLUTIONS Maintenance can be carried out on a capable laptop PC in real time. This provides the opportunity to generate and possibly view dense 3D point clouds already during an ongoing measurement and for the entire area of interest, e.g. for very long road paths or large-scale areas. In subsequent steps, e.g. after applying semantic segmentation these 3D points clouds can be used to derive 3D models of observed objects and help answering questions raised in dedicated applications. Using multiple measurements (i.e. repeated by driving in opposite directions or regularly the same road) over extended periods of time, occluded areas can be filled-in and long term changes in state parameters can be detected. Best practice: using of IPS technology in New Zealand Within the project “Digital Roads New Zealand”, several specified case studies with different environments using IPS technology have been applied in test areas in Northland, for the town basin of Whangarei, for the subdivision of Marsden City and for several rural roads. In all areas the measurement runs were from 3 to 10 km long. As a first step, the accurate trajectories have been calculated followed by a generation of largescale 3D point clouds of road environments. Figures 3, 5 and 6 show an overview of 3D clouds for dedicated test areas with a maximum resolution of a 1.5 cm grid. This high resolution of 3D information allowed us a detection of even very small details of road infrastructure. These 3D data are geo-referenced, with a position accuracy of several decimeters over the distance of travelling. The conditions of road infrastructure (e.g. bridges, guard railing, traffic road signs) can be automatically evaluated, recorded and monitored for changes. Generated 3D data allowed us to extract road geometry parameters such as the inclination along and across the road, or the state of barriers or of road markings. The 3D point data can be used for road inspection tasks and also as input for driver-assistance systems by describing the 3D road model at current time and place. The rural test area along Otaika Valley Road (OVR) is 12 km long; this is a hilly and windy rural road with high-frequency traffic of logging trucks. This road is a hot spot of traffic accidents, with about 90 crashes during the last 10 years, most of them involving logging trucks. The application of novel technologies for maintenance, modelling of road surfaces and for supporting drivers with assistance systems are important steps towards safer rural roads. Figure 4 shows calculated trajectory and figure 3 illustrates a resulting large-scale 3D point cloud based on just one run of our measuring car (in just one direction on the OVR). The urban area of town basin of Whangarei (of about 2x2 km) represents a typical city environment with high-frequently traffic; it is suitable for testing of driver-assistance systems. It is challenging to develop computer vision algorithms that filter out moving or otherwise disturbing objects while aiming at an accurate 3D model. Figure 5 shows a resulting overall 3D model and details of road infrastructure in the Whangarei town basin. The Marsden City subdivision area, with 5 km roads in total, is designed for future urban development with full road infrastructure components; it still has low traffic utilization prior to further development. This area is used for testing self-driving vehicle technology. Figure 6 shows a calculated trajectory of our measuring car and the overall 3D model. Road defect detection (e.g. potholes) is a subject for road-surface inspection. The ste- Figure 3: Case study “Otaika Valley Road” - examples of created 3D models Figure 4: Case study “Otaika Valley Road” - IPS measured trajectory in Google map Figure 5: Case study “Town Basin Whangarei” - Details of 3D model (left) and IPS original image-(right) International Transportation (71) 1 | 2019 21 Maintenance PRODUCTS & SOLUTIONS reo image data, provided by the IPS, can be used for efficient detection of road surface distress. IPS-based stereo vision [15] provides multi-frame depth data integration into a digital elevation model [16], supporting robust and efficient detection of potholes, and road surface inspection in general. Conclusions By our IPS measurement campaign, part of “Digital Roads NZ” project, we demonstrated a combination of real time trajectory estimation and 3D environment modelling; results are accurate and reliable over long time periods or trajectory lengths, with real time 3D point cloud generation (at 10 fps). This is a promising technological basis for various applications. The presented costeffective approach for data acquisition enables a high repetition rate. It is therefore not only well-suited for road inspection but also for change detection since time series of point clouds are exactly temporally and spatially referenced. ■ REFERENCES [1] R. Klette, “Vision-Based Driver Assistance”, in Wiley Encyclopedia of Electrical and Electronics Engineering, American Cancer Society, 2015, pp. 1-15 [2] K. Bimbraw, “Autonomous Cars: Past, Present and Future - A Review of the Developments in the Last Century”, in 12th International Conference on Informatics in Control, Automation and Robotic, 2015 [3] DLR Institute of Optical Sensors, 2019. [Online] Available: www.dlr. de/ os/ en [4] A. Börner, D. Baumbach, M. Buder, A. Choinowski, I. Ernst, E. Funk, D. Grießbach, A. Schischmanow, J. Wohlfeil and S. Zuev, “IPS - a vision aided navigation system”, Advanced Optical Technologies, pp. 121-129, 2017 [5] CeRV, Centre for Robotic & Visison, 2019. [Online] Available: www. cerv.aut.ac.nz [6] N3T, The Northland Transport Technology testbed, 2019. [Online] Available: http: / / www.n3t.kiwi [7] D. Grießbach, D. Baumbach and S. Zuev, “Stereo-Vision-Aided Inertial Navigation for Unknown Indoor and Outdoor Environments”, in International Conference on Indoor Positioning and Indoor Navigation, 2014 [8] H. Zhang, J. Wohlfeil, D. Grießbach and A. Börner, “Eligible Features Segregation for Real-time Visual Odometry”, in Conf. 3d-Nordost, Berlin, 2017 [9] H. Zhang, D. Grießbach, J. Wohlfeil and A. Börner, “Uncertainty Model for Template Feature Matching”, in The Pacific-Rim Symposium on Image and Video Technology, 2017 [10] H. Zhang, “Optical Navigation for Mobile Platforms Based on Camera Data”, Ph.D thesis, Technical University of Berlin, 2018 [11] J. Wohlfeil, D. Grießbach, I. Ernst, D. Baumbach and D. Dahlke, “Automatic camera system calibration with a chessboard enabling full image coverage”, in ISPRS Annals, in print, 2019 [12] H. Zhang, I. Ernst, S. Zuev, A. Börner, M. Knoche and R. Klette, “Visual Odometry and 3D Point Clouds Under Low-Light Conditions”, in International Conference on Image and Vision Computing New Zealand, Auckland, New Zealand, 2018 [13] H. Hirschmüller, M. Buder and I. Ernst, “Memory Efficient Semi- Global Matching”, in ISPRS Annals, 2012 [14] H. Hirschmüller and D. Scharstein, “Evaluation of Stereo Matching Costs on Images with Radiometric Differences”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, pp. 1582-1599, 9 2009 [15] A. Dhiman, H.-J. Chien, and R. Klette, “Road surface distress detection in disparity space”, in Proc. Int. Conf. Image Vision Computing New Zealand, IEEE online, Christchurch, 2017 [16] A. Dhiman, H.-J. Chien, and R. Klette, “A multi-frame Stereo Visonbased Road Profiling Technique for Distress Analysis”, in Proc. Int. Symposium on Pervasive Systems, Algorithms and Networks, IEEE online, Yichang, China, 2018 Martin Knoche, Dipl.-Phys., MBA CEO Northland Innovation Centre N3T, Whangarei (NZ) martin@n3t.kiwi Reinhard Klette, Prof. Dr. sc. nat. Director Centre for Robotic and Vision (CeRV), Auckland University of Technology, Auckland (NZ) rklette@aut.ac.nz Ines Ernst, Dipl.-Math. Research associate, DLR Institute of Optical Sensor Systems, Berlin (DE) ines.ernst@dlr.de Anko Börner, Dr.-Ing. Head of Department Real Time Data Processing, DLR Institute of Optical Sensor Systems, Berlin (DE) anko.boerner@dlr.de Hongmou Zhang, Dr.-Ing. Research associate, DLR Institute of Optical Sensor Systems, Berlin (DE) hongmou.zhang@dlr.de Sergey Zuev, Dr.-Ing. Research associate, DLR Institute of Optical Sensor Systems, Berlin (DE) sergey.zuev@dlr.de Figure 6: Case study “Marsden City” - IPS measured trajectory in Google map (left) and 3D-model of roads (right) Trialog Publishers Verlagsgesellschaft | Schliffkopfstrasse 22 | D-72270 Baiersbronn Tel.: +49 7449 91386.36 | Fax: +49 7449 91386.37 | office@trialog.de | www.trialog-publishers.de Let’s keep in touch editorsdesk@international-transportation.com advertising@international-transportation.com International Transportation (71) 1 | 2019 22 PRODUCTS & SOLUTIONS Vehicle design Secure, helpful, lovable Incorporating user needs in the design of autonomous vehicles systems for public transport User-centered design, User experience, Autonomous shuttles, Demand-responsive transport, Mobility as a service Autonomous, shared, and electric - this is the vision for future transport services that enable both efficient and climate-friendly mobility. The success of such services will crucially depend on their actual use by the population, which is in turn determined by perceptions of their usefulness, ease of use, safety, and attractiveness. The new features even entail some new challenges to users. We present methods to identify user needs and potential use barriers early in the process of designing autonomous vehicles systems for public transport, and give examples from our user-centered research. Annika Dreßler, Jan Grippenkoven, Meike Jipp, Klas Ihme, Uwe Drewitz T he public transport of the near future is envisioned to support both efficient and climatefriendly mobility: powered by sustainable drive technology, with autonomous driving allowing flexible, safe, and yet cost-efficient operation, as well as digital technology enabling an intelligent pooling of passenger (and goods) transport, and seamless connections [1]. Passengers share rides and are at minimum as fast to arrive at their destinations as they would be with their individual vehicle. Small shuttle buses are apt to serve even the capillaries of cities and pick up and drop passengers on demand almost right on their doorsteps, as door-to-door services or feeders to and from high capacity transport. A promising vision - however, the success of a transport system with the above features will crucially depend on its acceptance and actual use by the population. For this, the development of these systems must not only focus on the technical and legal aspects, but also offer viable solutions to new challenges that arise from a user perspective. Moreover, the mobility services offered must be as attractive to users as to compete with motorized individual transport. New challenges to users Certain features of flexible autonomous shuttles - e.g. the absence of a driver, the relatively small size of the vehicles, the flexible routing, timing and access points - entail new challenges from a user perspective. Imagine, for example, this rather simple situation: You want to take a bus to a destination that you do not routinely travel to, such as a station in another town or an event location in your city. Have you ever asked the driver if this bus is right for you? We guess you have. In the absence of a driver, the design of the system should still allow getting this information as fast and easy. As our research on user needs revealed, from a passenger perspective, the drivers of public transport vehicles nowadays fulfil a lot more functions than just driving [2]. Apart from the service and information function that is just shortly sketched in the above example, indeed, a lot of passengers perceive the driver as an instance of supervisory control that helps them feel safe. The question follows how to design driverless systems that support the same (or better) personal experience of safety and controllability. Motivating users to prefer the new public transport systems over the individual motor vehicle will moreover require some efforts to enhance service quality - not only with regard to the availability and time dimension, but also looking at user convenience and added value. Systems design must find answers to these and further challenges. How to incorporate user needs in systems design To design new public transport systems that will be the option of choice - not only to those who run short of alternatives, but to all - the satisfaction of user needs has to be of top priority in system development. Therefore, methods to incorporate user needs have to be applied throughout the design process, beginning from the early conceptual stages and continuing through iterations of prototyping until the final product [3, 4]. In the following, we introduce a selection of methods to achieve this Example of a driverless shuttle: Project HEAT in Hamburg, Germany © iav.de International Transportation (71) 1 | 2019 23 Vehicle design PRODUCTS & SOLUTIONS and give examples from our user-centered research. Applying models of user needs and acceptance Relevant models and empirical findings from engineering psychology are a good starting point to approach the assessment of user needs and inspire design for acceptance. There are efforts like that of Venkatesh et al. [5, 6] who integrated ideas from eight models into a Unified Theory of Acceptance and Use of Technology. The model has already been applied to autonomous vehicles and demand-responsiveness in public transport [7-9]. Figure 1 shows our adaption of the model to the context of electric autonomous shuttles in public transport. To dig deeper into the nature of human needs and explore what determines the “expected usefulness”, “expected effort”, and so on, more psychological models are available, as for example the needs pyramid [10] (see figure 2), or self-determination theory that states competence, autonomy and relatedness as basic human needs [11]. Importantly, these models can not only help in understanding how public transport can be made more attractive, but also in recognizing “hard cases” of design features serving certain needs (e.g. the fun in steering a moving vehicle by oneself ). Thus, in the case of certain features, it might not be sufficient to improve the design of public transport systems, but complementary action might be necessary to dampen the attractiveness of individual motorized transport [12]. User, context and task analysis The first thing to do in applying any of the following methods is to think about who typical users are and what they try to achieve [4]. In public transport, there is a broad spectrum of users with regard to age, gender, education, language skills, physical size, physical abilities, previous experience, and so forth. A good approach is to define multiple user groups based on the available statistics. To represent their characteristics in a concrete and tangible way, relevant features can be combined in so-called personas - a description of key prototypical users including not only a name, age, and gender, but also specifics such as goals, work, typical activities, and past experience [13]. Moreover, it is important to think about different contexts of use: For instance, an autonomous shuttle without additional safety systems might be suitable to a person in one context (e.g. by day when lots of other people are around), but not in another (e.g. at night, being alone with only one other person). Finally, the task of using the system to get from A to B needs to be analyzed in the light of the defined users and use contexts [14, 15]. Focus groups Focus group are a suitable method to obtain user requirements regarding future transport systems and let users participate in the early steps of the development. It is good to involve a broad variety of users, as this sheds light on diverse interests and requirements that might sometimes even be conflicting. A focus group typically comprises six to ten participants and is moderated by a subject matter expert or a neutral person. Participants are supposed to work together in a creative, cooperative and solution oriented way. The moderator supports the meeting with an agenda, stimulates the participants’ considerations with key questions, controls the time and ensures documentation. As an example, a focus group on perceived safety of users in flexible and driverless bus shuttles was conducted by Grippenkoven et al. [2]. After an introduction to the objectives, the participants first shared their knowledge about flexible bus shuttles and autonomous driving. The moderator specified the target system, a driverless transport service based on electrified and shared busshuttles that need to be booked through an app because they operate flexibly with regard to route and schedule. The concept was illustrated with explanatory videos. The participants’ first task was to individually elaborate on the question: What would determine my personal safety experience in these shuttles? After participants had introduced their thoughts and ideas to each other, they clustered the documented ideas and identified 17 aversive scenarios (see figure 3). In a subsequent idea phase, measures that enhance the personal experience of safety in the context of the introduced public transportation were collected. These measures serve as a valuable basis for further evaluations, for example in the context of prototypical mobility services in real world environments. Figure 1: A model of user acceptance and use of electric autonomous shuttles. Primary predictors (on the left) are expected to have a direct influence on use intention. User characteristics (bottom) can influence the relation between the predictors and use intention. Figure 2: Maslow’s pyramid of human needs, with examples of the individual levels (adapted from [10]) International Transportation (71) 1 | 2019 24 PRODUCTS & SOLUTIONS Vehicle design Surveys and interviews Users as well as current non-users can also tell us a lot about what they need and why they prefer certain mobility alternatives in surveys and interviews. In the case of advanced public transport systems, which are not yet widespread as objects of interaction experience, there is the specific challenge of enabling the respondents to make valid statements about their appraisal and requirements. Therefore, in the absence of an existing system to be evaluated, some effort should be put in an initial introduction that gives the respondent a vivid impression of the relevant system features. This can be achieved, for example, by narration, illustrated with images or videos, as shown above. In cases where a more “physical” idea of the system is needed (e.g. to assess which seat layout supports the best user experience), prototypes can be used. They can be simple, as a small room with some chairs, or more sophisticated, as an existing vehicle with (some) similar features as the target system. While interviews and focus groups mainly yield qualitative data (answers to open questions on user needs like What? How? etc.), written surveys are often used to obtain quantitative data (How much? How often? etc.), e.g. by asking users to give assessments on scales. The two methods can be usefully combined to first explore relevant aspects and design ideas and then assess the importance and generalizability of these aspects in a more representative sample of users. For instance, expanding on the results of the focus group on perceived safety, Grippenkoven et al. [2] conducted a survey in which potential users first gave ratings of how intimidating they found each of the aversive scenarios that had been identified, and afterwards assessed the effectivity of each of the proposed measures to improve experienced personal safety. Observation Subjective data as it comes from surveys, interviews, and focus groups is an invaluable source of information to explore user needs. However, some aspects of behavior and experience can hardly be to put into words, e.g. because the underlying processes are implicit and unconscious. Therefore, we can learn more about preferences of users and their difficulties in dealing with certain tasks by watching their interactions with transport systems. Examples: In what order do users choose available seats? What activities do passengers perform while traveling? How do they interact with each other in the vehicle? How do they react to disturbances (e.g. bumpy road, stop-and-go, drunk person entering)? Obviously, observation requires a more advanced stage of development of the transport system than just a concept. Thus, it can be applied to evaluate the target system when it is operating. However, depending on the design question, simulations or prototypes (see above) might also be used in earlier design phases. Scientific observation is structured and systematic by defining a priori a small range of target behaviors or events to be focused on and how to document the results (e.g. by counting how often events occur or measuring their duration [16]). Therefore, to enable Figure 3: Aversive scenarios identified in a focus group on personal safety experience in shared autonomous shuttles with flexible routing and timing [4] Figure 4. Real-time user state monitoring for assessment of user needs in autonomous vehicles for public transport. Based on different sensors (e.g. cameras or physiological sensors) the user state is identified using machine learning methods and integrated with information from the context. Based on this, the current need of the user can be determined. International Transportation (71) 1 | 2019 25 Vehicle design PRODUCTS & SOLUTIONS the scoring of the same episode for different target events, video recordings are of great help if they can be attained in accordance with data protection. Some important pillars of data protection are to work with low resolution that does not allow the identification of faces, to protect the raw data from access by third parties and to delete them right after the analysis to deduct the results. In the development of new transport systems, there might also be the possibility to do a test with dedicated users who can give informed consent. User-State monitoring In addition to the human eye, sensors can help to determine user needs during the design process by evaluating the users’ expression of inner states through behavioral channels such as posture, gestures, gaze behavior, vocal and facial expressions, or physiology [17-19]. Moreover, real-time user state monitoring can be used to develop systems that recognize user experience and can therefore react adaptively to negative episodes, such as discomfort, frustration, fear, stress or uncertainty. For instance, if a passenger of an autonomous shuttle feels uncertain about whether the automation can handle a complex situation or not, this could be sensed by the system. In case of detected uncertainty, information about the current situation representation of the automation could then be displayed on an invehicle screen or via a smartphone app to satisfy the user’s need for system transparency and safety. The sensing in such systems (see figure 4) could be implemented, for example, through camera-based methods or peripheral physiology [20-23]. It must be complemented with information about the context (traffic, environment, weather, etc.) or history of use, because similar negative experiences may have different causes - e.g. too fast driving style or rude behavior of another passenger - and thus require different measures - e.g. more defensive driving or a contact person for help. The use of multiple sensor views and methods based on wearables (e.g. fitness bands) could help to take into account the specifics of public transport to develop such user-oriented automated systems. Early user focus spares trouble These are only a few examples how usercentered methods can be used to incorporate user needs in the development of advanced systems for public transport. The choice of the best methods is made based on the specific system and research question and, not least, the available budget. Methods can be scaled to the available resources [24] by various parameters, such as low-cost prototyping, smaller sample sizes, or the application of expert evaluation (Heuristic Evaluation, Cognitive Walkthroughs) where user studies appear impracticable [3]. The early focus on user needs is most important, as it is much more practicable and much cheaper to remedy design shortcomings in the development process than after system implementation. Experience shows that it is worthwhile to invest in exploring user requirements, or, as Wickens [4] put it: “Being first, being best, and even being right do not matter; what matters is understanding what your customers want and need.” ■ REFERENCES [1] Fulton, L. M. (2018). Three Revolutions in Urban Passenger Travel. Joule, 2(4), 575-578 [2] Grippenkoven, J., Fassina, Z., König, A. & Dreßler, A. (2019). Perceived Safety: a necessary precondition for successful autonomous mobility services. In D. de Waard, K. Brookhuis, D. Coelho, S. Fairclough, D. Manzey, A. Naumann, L. Onnasch, S. Röttger, A. Toffetti, and R. Wiczorek (Ed.), Proceedings of the Human Factors and Ergonomics Society Europe Chapter 2018 Annual Conference [3] Nielsen, J. (2009). Usability engineering. Amsterdam: Morgan Kaufmann [4] Wickens, C. D., Gordon, S. E. & Liu, Y. (2004). An introduction to human factors engineering. Upper Saddle River, N.J: Prentice Hall [5] Venkatesh V., Morris, M., Davis, G. & Davis, F. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425 [6] Venkatesh, V., Thong, James Y. L. & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. Management information systems : MIS Quarterly, 36(1), 157-178 [7] König, A., Grippenkoven, J., & Jipp, M. (in prep.). The Actual Demand Behind Demand-Responsive Transport: Applying the Unified Theory of Acceptance and Use of Technology to Explain Usage Intentions of Demand-Responsive Bus Services [8] Madigan, R., Louw, T., Dziennus, M., Graindorge, T., Ortega, E., Graindorge, M., & Merat, N. (2016). Acceptance of Automated Road Transport Systems (ARTS): An Adaptation of the UTAUT Model. Transport Research Arena TRA2016, 14(Supplement C), 2217-2226 [9] Nordhoff, S., van Arem, B., Merat, N., Madigan, R., Ruhrort, L., Knie, A., & Happee, R. (2017). User Acceptance of Driverless Shuttles Running in an Open and Mixed Traffic Environment. In Proceedings of the 12th ITS European Congress. Strasbourg, France, 19-22 June 2017 [10] Maslow, A. (1943). A theory of human motivation. Psychological Review, 50, 370-396 [11] Ryan, R., & Deci, E. (2017). Self-Determination Theory: Basic Psychological Needs in Motivation, Development, and Wellness. New York: Guilford [12] Graham-Rowe, E., Skippon, S., Gardner, B., & Abraham, C. (2011). Can we reduce car use and, if so, how? A review of available evidence. Transportation Research Part A: Policy and Practice, 45(5), 401-418 [13] Cooper, A. (2004). The inmates are running the asylum. Indianapolis: Sams [14] Annett, J. (2005). Hierarchical Task Analysis. In N. Stanton, A. Hedge, K. Brookhuis, E. Salas, & H. Hendrick (Eds.), The handbook of human factors and ergonomics methods . New York, NY: Taylor & Francis [15] Naikar, N., Moylan, A., & Pearce, B. (2006). Analysing activity in complex systems with cognitive work analysis: concepts, guidelines and case study for control task analysis. Theoretical Issues in Ergonomics Science, 7(4), 371-394 [16] García-López, M. D. C. (2016). Systematic Observation of Behaviors and Environmental Events Using the Lag Method. Perceptual and Motor Skills, 67(1), 255-262 [17] Crivelli, C., & Fridlund, A. J. (2018). Facial Displays Are Tools for Social Influence. Trends in Cognitive Sciences, 22(5), 388-399 [18] Erickson, K., & Schulkin, J. (2003). Facial expressions of emotion: A cognitive neuroscience perspective. Brain and Cognition, 52(1), 52-60 [19] Scherer, K. R. (2016). What are emotions? And how can they be measured? Social Science Information, 44(4), 695-729 [20] Beggiato, M., Hartwich, F., & Krems, J. (2018). Using Smartbands, Pupillometry and Body Motion to Detect Discomfort in Automated Driving. Frontiers in Human Neuroscience, 12, 3138 [21] Bořil, H., Boyraz, P., & Hansen, J. H. L. (2012). Towards Multimodal Driver’s Stress Detection. In J. H. Hansen, P. Boyraz, K. Takeda, & H. Abut (Eds.), Digital Signal Processing for In-Vehicle Systems and Safety (pp. 3-19). New York, NY: Springer New York [22] Ihme, K., Unni, A., Zhang, M., Rieger, J. W., & Jipp, M. (2018). Recognizing Frustration of Drivers From Face Video Recordings and Brain Activation Measurements With Functional Near-Infrared Spectroscopy. Frontiers in Human Neuroscience, 12, 669 [23] Zhang, M., Ihme, K., & Drewitz, U. (2019). Discriminating drivers’ emotions through the dimension of power: Evidence from facial infrared thermography and peripheral physiological measurements. Transportation Research Part F: Traffic Psychology and Behaviour, 63, 135-143 [24] Nielsen, J. (1994). Guerrilla HCI: Using Discount Usability Engineering to Penetrate the Intimidation Barrier. In R. G. Bias (Ed.), Costjustifying usability (pp. 245-272). San Diego: Academic Press Jan Grippenkoven, M.Sc. Dept. Human Factors, DLR Institute of Transportation Systems, Braunschweig (DE) jan.grippenkoven@dlr.de Meike Jipp, PD Dr. Dept. Human Factors, DLR Institute of Transportation Systems, Braunschweig (DE) meike.jipp@dlr.de Annika Dreßler, Dr. Dept. Human Factors, DLR Institute of Transportation Systems, Braunschweig (DE) annika.dressler@dlr.de Klas Ihme, Dr. Dept. Human Factors, DLR Institute of Transportation Systems, Braunschweig (DE) klas.ihme@dlr.de Uwe Drewitz Dept. Human Factors, DLR Institute of Transportation Systems, Braunschweig (DE) uwe.drewitz@dlr.de International Transportation (71) 1 | 2019 26 PRODUCTS & SOLUTIONS Business line Projects in a nutshell Overview of selected mobility solutions World’s biggest urban ropeway network completed T he tenth line belonging to the world’s biggest urban ropeway network is in service. The Línea Plateada (silver line) was the last to be opened as part of the 33-kilometer network. This network is comprised of detachable gondola lifts and constitutes the principal mode of transport for Bolivia’s adjacent metropolises La Paz and El Alto. The Línea Plateada closes the loop, which means that all lines are now interconnected. The Línea Plateada is an important element in the urban infrastructure of El Alto and La Paz: By connecting the red, blue, purple and yellow lines, it completes the last segment of the ropeway circuit. This means that all the lines are now interconnected. Modern ten-passenger cabins enable up to 3,000 passengers an hour to glide in each direction between the stations 16 de Julio and Mirador. The new line runs via the intermediate station Faro Murillo - the biggest station building in the network covering a total surface area of 10,000 m 2 . Here, passengers can change to the Línea Morada (purple line), which opened in September 2018. Since 2014, ropeways from Doppelmayr/ Garaventa have been part of everyday life for the residents of La Paz and El Alto. These installations make it easier for people to get from A to B, reduce traveling time and guarantee that they will get where they want to go on time. The ropeway network encompassing the two South American metropolises consists of ten lines with an overall length of approximately 33 kilometers. Since the first ropeway opened in May 2014, almost 200 million passengers have been carried to date. Every day, Mi Teleférico is used by some 300,000 people - as a means of getting to work, going shopping, going to school or accessing leisure time activities. The ropeways are also very popular with tourists. www.doppelmayr.com Línea Plateada at Faro Murillo station Source: Doppelmayr/ Garaventa International Transportation (71) 1 | 2019 27 Business line PRODUCTS & SOLUTIONS Chances and challenges: E-scooters in the City of Portland E -scooters have the potential to advance transportation goals. This is one of the key findings from the “2018 E-Scooter Findings Report” published in Portland, Oregon. The report demonstrates that as the city of Portland grows and traffic congestion gets worse, e-scooters can move more people safely and efficiently in the same amount of space. This helps reduce reliance on automobiles and shift trips to an efficient, potentially less-polluting travel option. The publishers - the „Portland Bureau Of Transportation (PBOT)” - believe there is a preliminary indication that e-scooters are a less-polluting travel option. However, they need more data especially regarding e-scooter operations and lifecycle costs before they can definitively say how much or even whether e-scooters directly contribute to a reduction in greenhouse gasses. During a pilot in 2018, riders took more than 700,000 e-scooter trips on various types of streets. Throughout the city, sidewalk riding was lower along streets with lower speeds or designated bikeways. This clearly demonstrates how important it is to have protected facilities that minimize conflicts between pedestrians, e-scooters, and cars. For all of the positives about scooters that emerged during the pilot, PBOT also learned valuable lessons about the challenges related to making scooters a permanent part of Portland’s transportation ecosystem. Given the scale and scope of these challenges, they planned a second pilot in 2019. This pilot will be longer to give more time to test innovative solutions to the challenges that emerged this past summer and fall - specifically to focus the efforts on improving equitable access across the city and ensuring safe and legal riding and parking. E-scooter use impacted other park users and presented a significant management challenge for Portland Parks & Recreation staff. In addition, PBOT planned to conduct additional public and stakeholder engagement. www.portlandoregon.gov/ transportation/ e-scooter First cab-less autonomous e-truck in commercial operations on public road I t was a historic moment on 15 May this year, when the first cab-less, electric truck - Einride’s T-pod - drove on a public road. The world premiere and inaugural run took place at DB Schenker’s facility in Jönköping, Sweden. The T-pod will transport goods between a warehouse and terminal at the facility, as part of a commercial flow. The T-pod is self-driving, but will be supervised by an operator which, if needed, can steer the vehicle through remote control - one operator can control several vehicles. The vehicle can load up to 20 tonnes and is capable of driving approximately 20 miles on a charge. The Swedish startup Einride and leading logistics firm DB Schenker initiated their partnership in April 2018. The agreement includes the pilot in Jönköping and an option for additional pilots internationally. Ericsson and Telia provides the installation with high performance, 5G-based connectivity. In November 2018, Einride DB Schenker initiated the first installation of an autonomous, allelectric truck or “T-pod” at the facility in Jönköping, Sweden. It was the first commercial installation of its kind in the world. On 7 March 2019 the Swedish Transport Agency concluded that the T-pod is able to operate in accordance with Swedish traffic regulations. March 11, the agency approved Einride’s application to expand the pilot to a public road. The permit applies to a public road within an industrial area - between a warehouse and a terminal. The permit is valid until 31 December 2020. www.einride.tech Photo: Einride International Transportation (71) 1 | 2019 28 PRODUCTS & SOLUTIONS Business line Roadscanners Oy reconcentrates on road business F inland-based Roadscanners Oy sold its Rail Division business, including its Rail Doctor® software and related intellectual property rights, to Loram Finland Oy, owned by US-based Loram Maintenance of Way, Inc. - located in Hamel, Minnesota, and established in 1954. Loram Maintenance of Way is one of the world’s foremost railway maintenance companies, specialized in maintenance equipment, contracting and consulting services. The acquisition will reinforce Loram’s know-how and service portfolio. After the sale, Roadscanners Oy will continue its business in other sectors, including its Road Division and Software and Hardware Division, in the field of road, street, bridge and airport condition surveys, diagnostics and asset management. Road Doctor Survey Van (RDSV), for example, offers a complete non-destructive survey system designed for road condition data collection and analysis. RDSV unites the Road Doctor survey packages in a single affordable easy to use plug and play high tech system. The Road Doctor software for simultaneous surface and sub-surface data analysis is able to synchronize and visualize data from several sources enabling multidata analysis. In addition, Roadscanners will continue investing in ITS-related research and development work. www.roadscanners.com Latvia Road Doctor survey van Source: Roadscanners Photo: Linz AG / fotokerschi Chinese electrical air taxi to be tested in Linz T he Chinese EHang Air Mobility Group and the Austrian FACC aviation company will take their air taxi to series production in Austria by 2020. A “Innovation Partnership” between EHang, FAAC and the public service provider Linz AG is decided to develop strategies and operations routines for the unmanned EHang 216 Autonomous Aerial Vehicles (AAV) in urban regions. The population of the world’s cities is growing rapidly. By the year 2030, 5.2 billion people will live in urban regions, about one billion more than today. The proportion of urban dwellers will then be 60 % of the world’s population, which will mean that people will be affected by traffic jams on the roads on a daily basis. Air taxis are expected to help solve traffic problems in cities by using airspace as a “third dimension”. Newly developed, environmentally friendly electric drives, high-performance batteries with short charging times, minimal space requirements for take-off and landing areas, fast computers and big data create the necessary conditions for tackling urban air- mobility concepts within and between cities. In addition to transporting people or goods, autonomous aircraft offer numerous other applications such as flights to transport urgent emergency goods or high-risk operations from the air in the event of environmental catastrophes. The market potential is very promising: According to a study by Roland Berger, there is a demand for air taxis worth EUR 32 billion over the next decade. The EHang AAVs are essentially ready for series production; certification procedures are under way. As soon as they are completed, the only obstacles to real flight operation are regulatory ones. Air taxis will be a component of future mobility concepts that is very different from what we are familiar with today. www.ehang.com Software SCIENCE & RESEARCH International Transportation (71) 1 | 2019 29 Dominion A realtime middleware for connecting functions in-highly automated vehicles Middleware, Traffic, Automotive, Vehicle simulator, Automated vehicle The Institute for Transportation Systems (TS) at the German Aerospace Center (DLR) develops Dominion as the connecting software for all its automotive and some other research platforms. Dominion’s development began more than ten years ago. Since then, changing research topics required to increase Dominion’s flexibility to meet current and future projects’ demands. This article describes Dominion’s basic features and how we updated and extended them to keep Dominion a usable research tool for the future. Björn Hendriks, Christian Harms, Michael Kürschner T he Institute of Transportation Systems (TS) at German Aerospace Center (DLR) researches a broad range of topics for intelligent mobility. For automotive-based research the institute runs several research platforms to conduct studies with real or simulated vehicles (see figure 1 and figure 2) in different traffic scenarios. Currently, the simulation platforms are extended to include testands riding a bicycle or participating as pedestrian. The simulators as well as the real vehicles contain a broad range of software modules to read the data of sensors, control the actuators, or interact with the testands. Additionally, there are software modules for data processing, for example, to detect relevant objects in camera views or laser scanners, to create the output for advanced driver-assistance systems, or to implement vehicle automation. Many of these software modules were developed by researchers according to the requirements of their respective project demands. To connect all these software modules TS has developed the middleware Dominion as an abstract platform for the data exchange of these software modules [1, 2]. Its main objectives are • real-time capability, • distributable over multiple hosts with different operating systems (Linux, Windows, ...), • encapsulating research platform complexity, • easy to adapt to project-specific requirements. In Dominion’s terminology software modules running on Dominion are called Dominion Application. Examples for Dominion Applications are • read steering wheel or pedal positions, • display outputs to the driver (speedometer, signal lamps, …), • compute driving dynamics, • read driver state sensors (heart rate, eye tracker, …), • set acc-/ deceleration of an automated vehicle, • compute assistance system results, • read state of traffic lights, or • compute state of simulated traffic lights. The last two examples illustrate the separation of abstract functionality from the concrete research platform executing it. The abstract function “get traffic light state” is independent of the source of the traffic light state on the concrete platform. There is one Dominion Application implementing the functionality in a driving simulator and another Dominion Application implementing it on a real vehicle. All of these Dominion Applications produce the same output that is then provided to other Dominion Applications regardless of its concrete source, thus all Dominion Applications consuming the traffic light state can be the same on all platforms. Often driving studies need to alter specific aspects of the research platform while the remaining aspects of driving should work as usual. For example, the investigation of a new advanced driver-assistance system in a driving study may require to process already existing vehicle data and display the result on the dashboard. Another example is a study to investigate a new physiological sensor to detect driver tiredness. Because Dominion Applications usually control only a single aspect of the research platform the adaptations required for such studies can often be limited to add or adapt only a few Figure 1: Dynamic simulator at TS © DLR SCIENCE & RESEARCH Software International Transportation (71) 1 | 2019 30 Dominion Applications. That modularity enables researchers to develop project-specific functions as a Dominion Application without the need to know much about the technical details of the rest of the research platform. Instead, they only need to know how to produce or consume their project-specific data. This is an important precondition for an efficient research with our complex platforms. Dominion’s first major version was finished more than ten years ago, but since then, changes in research topics constantly created new requirements for Dominion. The next section describes Dominion’s basic architecture while the following sections explain how some of the new demands were met. Dominion’s architecture Dominion is split into a general part called Dominion Environment and an installation-specific part called Dominion User. Dominion User contains the Dominion Applications and all data structures they exchange. Dominion Environment contains the general libraries to execute the data exchange as well as functions and classes for typical tasks of Dominion Applications and several supplementary tools. All Dominion software (except for some supplementary tools) is implemented in C++, which allows to develop efficient code. The separation of Dominion Environment and Dominion User allows to apply Dominion Environment as a general middleware in other domains as well. At TS it is used for railway research too. Other DLR institutes apply Dominion Environment on their non-traffic research platforms to achieve a high-performance realtime data exchange. Dominion Data Core At the heart of Dominion User is a central data model called Dominion Data Core. Dominion Data Core contains a nested tree of data structures with all data eventually required by one of the Dominion Applications. At TS we use a joint Dominion Data Core for all automotive Dominion Applications. Having the same Dominion Data Core in the automotive simulators and the real vehicles makes transitions of Dominion Applications between these platforms easy. Dominion installations in other domains have their own Dominion User space and thus their own Dominion Data Core. The central part of the Dominion Data Core is a tree of data to pass between the Dominion Applications. The leaves of the tree are numbers of all kinds, characters, or enumerations. These are aggregated into a hierarchy of nested structures. In later versions of Dominion we added unions and ringbuffers beside the structures. A- structure contains all of its elements simultaneously, while unions only contain one element at a time. The Dominion Applications exchanging union data must provide a mechanism to select the active union element at runtime, because this selection cannot be defined generically. Ringbuffers are for cases where the amount of data produced by a Dominion Application per call cycle is not fixed. They provide a buffer which becomes automatically overwritten when it is full. All levels of the tree can have a multiplicity such that they are arrays of numbers, structures, etc. Besides the data tree, the Dominion Data Core contains meta info for all Dominion Applications and other components, particularly which part of the data tree a Dominion Application reads as input or writes as output. Dominion Applications only have access to their specified inputs and outputs. Code generation A main feature of Dominion Environment is the code generation for the Dominion Applications, which is based on the Eclipse Modeling Framework (EMF) [3]. EMF is an implementation of the Meta Object Facility (MOF) of the Object Management Group (OMG) specification consortium (see [4] and its referenced special MOF standards). The Dominion Data Core is implemented as an EMF Ecore model. A model-to-model transformation implemented in QVTo (Query/ View/ Transformation Operational Mapping, part of the MOF standards) transforms it to another Ecore model (application model), which has a structure oriented towards code generation demands. The application model is used as input to many code generation templates implemented in Acceleo [5]. Acceleo is an implementation of the OMG MTL standard [6] with some extensions. The Acceleo templates generate C++ code for each selected Dominion Application. Besides other functionality, the generated code contains a few empty interface functions to be implemented for the specific Dominion Application. In addition, it contains the trees of the Dominion Application’s input and output as nested C++ structs, such that input and output elements can be accessed as C++ variables. The generated code takes care to call the interface functions, particularly it calls the so-called run function regularly within a configured time frame. Figure 2: Research vehicles at TS © DLR Software SCIENCE & RESEARCH International Transportation (71) 1 | 2019 31 Data exchange All Dominion Applications running on the same host exchange their data by using a shared memory. To organize the shared memory each Dominion Application registers on initialization its specific inputs and outputs by sending their structure to the so-called Dominion Server. The Dominion Server merges the structures received from all Dominion Applications, maps them to addresses in the shared memory, and returns the respective addresses to the Dominion Applications. The generated code of each Dominion Application takes care to copy the relevant parts of the shared memory to its internal input C++ struct, call the Dominion Application-specific code, and finally copy the output back to the assigned shared memory addresses. The access of the shared memory is protected by a global mutex such that only one Dominion Application at a time can access the shared memory. Note, that the Dominion Server only organizes the data exchange in the initialization phase; once all Dominion Applications are initialized they communicate directly without the Dominion Server. If Dominion Applications run on different hosts the generated code takes care to synchronize the relevant parts of the shared memories on the hosts by sending UDP or TCP broadcasts over LAN. With a growing number of hosts the overhead of broadcasts sending Dominion Data Core updates unconditionally to every host limits the data throughput. To improve Dominion’s scalability, we are currently adding unicasts and multicasts to send data only to hosts which need it. Figure 3 shows an example of a possible data flow through Dominion based on an AGLOSA functionality [7]. A sensor, in this example a camera viewing a traffic light, is read by a special Dominion Application (Camera App). The Dominion Application extracts the traffic light state from the view and writes it into the shared memory of the host it runs on. Alternatively, the traffic light could be equipped with a V2X (vehicle-to-everything) sender read by a matching V2X Dominion Application [8]. Next, a Dominion Application (Processing App) running on the same host reads the data from the shared memory to compute a speed recommendation depending on the traffic light state. It stores the result in the shared memory and simultaneously sends it to the shared memory of Host 2, because it knows from the Dominion Server that on Host 2 another Dominion Application runs (Speed-Ctrl App) consuming the speed recommendation. On Host 1 the Dashboard Dominion Application reads the recommended speed from the shared memory and sends it to the vehicle’s dashboard. At the same time on Host 2 the Speed-Ctrl Dominion Application reads the recommended speed from the shared memory of Host 2 and sends it to the speed control of an automated vehicle. The described example shows that even a simple detection and distribution of traffic light states creates complex data dissemination requests, for which Dominion is optimized considering the complexity of different types of research platforms. Additionally, Dominion’s architecture based on shared memory access achieves a high-performance data exchange within a host, which is especially a requirement for highly automated vehicles. Tests on a workstation have shown that two Dominion Applications could exchange data via shared memory at a rate of 10 kHz. Connecting Dominion to other middlewares Automotive research evolves more and more to technical cooperation among vehicles and infrastructure, particularly for automated vehicles. Often cooperation experiments in this field are conducted together with external research groups who have their own research platforms. These need to cooperate with our research platforms running Dominion, such that data will be exchanged Figure 3: Example for a possible Dominion data flow. The numbers denote the order of data flow. © DLR SCIENCE & RESEARCH Software International Transportation (71) 1 | 2019 32 between Dominion and the middlewares deployed on the platforms run by the partners. Another reason to connect Dominion to other middlewares are software modules available for those. To integrate such an external software module it is often simpler to exchange Dominion’s data with the middleware belonging to the requested module than to integrate that module into Dominion. Examples for middlewares Dominion needs to connect to are Robot Operating System (ROS, see www.ros. org), AUTomotive Open System ARchitecture (AUTO- SAR, see www.autosar.org), MQ Telemetry Transport (MQTT, see www.mqtt.org), and Scalable service-Oriented MiddlewarE over IP (SOME/ IP, see www.some-ip. com). Until now, in such cases we have developed a specific communication Dominion Application for each individual project’s data-exchange requirements. But that required to adapt each of those specific Dominion Applications when there was a change of a concerned part of the data tree in the Dominion Data Core. To avoid that in the future, we are currently adding a new level of code generation. Each Dominion Application can have individual text generation templates (appspecific templates) implemented in Acceleo. There are two categories of such templates either for source files or for non-source files. Source file templates must generate valid C++ code, which will be automatically compiled and linked to its Dominion Application by Dominion Environment. Generated non-source files can contain arbitrary text, for example configuration data for an external tool or generated documentation. They will not be further processed by Dominion Environment. App-specific templates have access to the whole Dominion Data Core including the whole data tree as well as to the meta data of all Dominion Applications. To make navigation through the Dominion Data Core model easier, a QVTo mapping transforms the Dominion Data Core model to a special code-generation Ecore model (see figure 4). Opposed to the Dominion Data Core model the code generation model contains back references, for example, from the data entries to the Dominion Applications producing or consuming the data, so it can be navigated directly from each model element to all related model elements. The app-specific code generation enables the development of generic Dominion Applications to exchange data between Dominion and another middleware. The code to synchronize the Dominion Data Core tree with the other middleware can be generated as well as all necessary configuration files. Build system During the long development of Dominion its build system has evolved to a mixture of different build tools. The code generation (including model to model transformation) was handled by Java’s build tool Ant with the extensions Ant-Contrib and Antelope and some self-developed Dominion DataElement Application Library Entry Structure RingBuffer ModelElement Container CodeElement EntryInteger EntryFloat Union [0..1] parent [0..*] dataElements [0..*] outputs [0..*] producers [0..*] inputs [0..*] consumers [0..*] dataElements [0..*] codeElements [0..*] dependencies [0..*] dependencies Figure 4: Simplified code generation Ecore model © DLR Software SCIENCE & RESEARCH International Transportation (71) 1 | 2019 33 Ant tasks to call the code generation tools. On Linux Ant additionally handled the compilation of the C++ code with the Ant extension cpptasks. On Windows the code generation created Visual Studio project files which included a hierarchy of Visual Studio property files. On both platforms a Dominion Application developer had to build the Dominion Environment first and then each Dominion Application that was needed. Eventually additional libraries had to be built in between. This build system had several difficulties: • A Dominion Application developer needed to build the Dominion Environment, the Dominion Applications, and all other required components individually in the correct order. • Ant is mainly designed to compile Java code. Its extension cpptasks to compile C++ has some limitations. • Many build configurations have to be made in Ant and Visual Studio files in parallel with the risk to implement them slightly different or to fix OS-independent bugs only for either Linux or Windows. To address all these difficulties and refactor the build system we have reimplemented the build process with the modern build tool CMake (see www.cmake.org). CMake natively supports C++ but is flexible enough to integrate other compilers as well. CMake is a meta build system generating native build files for a variety of platforms and compilers, including GNU Makefiles for GCC on Linux as well as Visual Studio project and solution files on Windows. Thus the build code in CMake is in general the same for all platforms and compilers, although it is not always avoidable to have CMake code sections for a special compiler or platform (for example there are some special compiler flags set for Visual C++ or some code to install required DLLs on Windows next to the executables). To mitigate potential problems arising from the special sections we keep them as short as possible by applying CMake’s platformand compileragnostic commands and variables wherever possible. As a side benefit we can now compile Dominion on new platforms, like, for example, the ARM-based Raspberry Pi, without the need to do any additional adaptations. This setup reduces the compilation of Dominion Environment and all required Dominion Applications to a single call of CMake followed by a call of the installation-specific compiler. This also includes the building of the integrated documentation. So we do not have to train each Dominion Application developer to know the details of the build process any more. Currently, we are working on the automatic integration of commonly used external libraries by implementing CMake modules to build and link those generically. Summary and outlook Changing research interests over time create changing demands for the features of Dominion as the middleware of the research platforms at TS. We have shown how we met the demand to interact with other middlewares. Next, we have shown how refactoring the build system solved many difficulties accumulated in Dominion’s long-term development and made it fit for the future by increasing usability and manageability as well as integrating new platforms, compilers, and development environments. Besides these, we constantly improve Dominion in many more aspects. Currently we plan to compare Dominion’s abilities to other available middlewares to find out more about its strengths and weaknesses. Based on the results we will evaluate the effort of further Dominion development compared to replacing it with another middleware. But we are confident that at least Dominion’s shared memory communication is doing well compared to the communication methods of other middlewares, particlularly that Dominion is the right tool for the high real-time demands of automated driving. Besides, replacing Dominion would cause a lot of extra work to adapt all the Dominion Applications currently in use, so that an alternative middleware must at least compensate for that with its benefits. Overall, keeping Dominion is currently the most promising alternative and the comparison with other middlewares will guide Dominion’s future development direction towards a more beneficial research tool. ■ REFERENCES [1] Jan Gacnik, Oliver Häger, and Marco Hannibal. A service-oriented system architecture for the human centered design of intelligent transportation systems. In: European conference on human centred design for intelligent transport systems. Apr. 2008 [2] Jan Gacnik, Oliver Häger, Marco Hannibal, and Frank Köster. Service-oriented architecture for future driver assistance systems. In: Fisita 2008 automotive world congress. Sept.-2008 [3] D. Steinberg, F. Budinsky, and E. Merks. EMF: Eclipse Modeling Framework. Eclipse (Addison-Wesley). Addison-Wesley, 2009. ISBN: 9780321331885 [4] Object Management Group (OMG). Meta-Object Facility (MOF) Specification. https: / / www. omg.org/ spec/ MOF [5] Obeo. Acceleo. https: / / www.eclipse.org/ acceleo [6] Object Management Group (OMG). MOF Model to Text Transformation Language Specification. https: / / www.omg.org/ spec/ MOFM2T [7] Jakob Erdmann. Combining adaptive junction control with simultaneous green-lightoptimal-speed-advisory. In: Wivec 2013. June 2013 [8] Tobias Frankiewicz and Alexander Burmeister. AIM reference track— test site for V2X communication systems and cooperative ITS services. In: Journal of large-scale research facilities JLSRF 3.A106 (Apr. 2017) Christian Harms Institute of Transportation Systems, German Aerospace Center (DLR), Braunschweig (DE) christian.harms@dlr.de Michael Kürschner Institute of Transportation Systems, German Aerospace Center (DLR), Braunschweig (DE) michael.kuerschner@dlr.de Björn Hendriks, Dr Institute of Transportation Systems, German Aerospace Center (DLR), Braunschweig (DE) bjoern.hendriks@dlr.de SCIENCE & RESEARCH Rail operations International Transportation (71) 1 | 2019 34 Dwell time forecast in-railbound traffic Procedure and first evaluation Dwell time, Timetable planning, Passenger service time, Quality of service, Dispatching, Highly stressed passenger transport systems Due to their extend and their variability the dwell times at scheduled stops remain a challenge to operational planning and controlling in railbound traffic. For this purpose an approach will be presented, which allows a prediction of the expected dwell times as well as their variations for the individual stops in the course of a whole train run, based on input parameters describing the infrastructure, the vehicle and the traffic volume. Finally, a first validation will be discussed. Johannes Uhl, Ullrich Martin T ransport operators all over the world are faced with various challenges concerning dwell times at scheduled stops in railbound traffic. While the times required for scheduled stops are increasing due to the rising number of passengers and safety requirements, the growing occupancy rate of the infrastructure and the resulting shorter train headways prevent an expansion of the dwell times. This and the increasing passenger and operational requirements on operating quality require a reliable forecast of the expected dwell times and their variations [1]. At the Institute of Railway and Transportation Engineering of the University of Stuttgart, a model for course-related forecast of dwell times at scheduled stops in railbound transport systems is being developed. Below the basic functions and relationships of this model will be introduced and furthermore some model results will be compared with measured values from practice. State of research Various modeling proposals already supply forecasts of dwell times. One example is the dwell time model based on regression analysis of Weston [2]. The investigations on station stops and passenger exchange processes carried out by Weidmann [3] and Heinz [4] as well as their models inspired by fluid mechanics are also significant. Furthermore, several researchers approached the topic by pedestrian flow simulations (e. g., [4, 5]). Currently investigations on dwell time modeling shows high dynamic in Asia, while the postulated models are often regression models for specific transportation systems (e. g., [6]). The model presented below predicts the time required for the individual stops in the course of a train run. It builds on a model forecasting the passenger exchange times which was developed at the same institute (compare [7, 8]). By using queueing-theoretical approaches to calculate the time required for boarding and alighting, the dwell time model takes special account of the stochastic properties of passenger exchange processes. This analytic approach allows an estimation of the dwell time variations without needing extended computation time as required e.g. for pedestrian flow simulations. Thereby the model not only predicts the expected mean of the required dwell times but also the associated distribution functions. The approach is applicable to all railbound transport systems and to ensure the models practicability its data requirements are limited to data typically available in transport companies. As a prototype, the proposed model was implemented in Matlab [9]. Course-related dwell time modeling As can be seen in figure 1, the dwell time modeling for a course starts with the input of the required data by the user. Hereby, infrastructure data (e. g. properties and facilities of the platforms, stop positions), vehicle data (e. g. length, door and capacity distribution, door closing times) and traffic data (e. g. passenger volume) are queried for the investigated train run. The required infrastructure data can be obtained from station plans or from online aerial photographs and the needed vehicle data from the vehicle type sheets. The traffic data cover mainly the numbers of boarders and alighters at the individual stops of the investigated train run which can be received form traffic models or passenger counts. If available, the user can furthermore input the origindestination matrix of the train run. Otherwise, the model itself estimates this matrix based on the numbers of boarders and alighters. In addition, the operational program of parallel running lines can be indicated. PEER REVIEW Received: : 1 Apr 2019 Accepted: 13 May 2019 Rail operations SCIENCE & RESEARCH International Transportation (71) 1 | 2019 35 The calculation process consists of three sub steps - namely the modeling of the expected number of boarding passengers, the distribution of the boarding and alighting passengers among the vehicle doors and finally the time required for the passenger exchange and the other processes of a stop. These three sub-steps are calculated consecutively for every station in the course of a train run, building up on the results of the previous scheduled stops. The procedure within the sub-steps will be explained in more detail below. Figure 1 also elucidates that this procedure is embedded in two repetitive loops. The superior loop takes into account the stochastic daily variations of factors such as delays, passenger volume, and behavior of the passengers. Therefore, random numbers of these variables are generated for about 100 to 200 days of operation. Furthermore, the use of queueing-theoretical approaches also allows the utilization of a probability of non-exceedance. Among others, this concerns the determination of the amount of boarding passengers arriving at the platform non-timetable-oriented. Therefore, about 5 to 20-probability steps are calculated for each day of operation. The results of the individual of both loops are aggregated by averaging. Finally, the model provides the distribution functions of the dwell times expected for each scheduled stop as well as additional statistical information such as mean values and standard deviations. Furthermore, there are additional outputs that support the understanding of the results and especially allow identifying potentials for optimizations concerning the dwell times. Expected amount of boarding passengers As can be seen from equation (1), the modeling of the amount of boarding passengers at a scheduled stop is carried out separately for each destination reachable from this stop on the considered line. Thereby the passengers arriving at the platform are distinguished between passengers arriving randomly and timetableoriented. Their ratio A is inter alia depending on the scheduled headway on the respective origin-destinationrelation [10]. First of all the average arrival rate AR m,p of boarders on the current origin-destination relation is calculated from the amount of passengers on this relation. Then, the current headway CHW m,p occurring in the considered operating situation is calculated for each relation. Therefore, the randomly generated delays of the evaluated line and parallel running lines are considered. Further delays due to exceeding the planned dwell times at previous stops are also taken into account. The number of passengers arriving randomly BR m,p depends on the duration since the last travel opportunity on the considered relation and is modeled as a queueingtheoretical birth process, whereby the current headway and the respective probability are taken into account. The number of timetable-oriented passengers BT m,p is determined directly from the general number of boarders at the stop. Only if the current headway is longer than 1.5 times the scheduled headway, it is taken into account that there are already passengers arriving for the next trip. Finally, the expected amount of arriving passengers of the individual relations are summed up to get the total amount of boarding passengers B m at the considered stop. B A SHW BR SHW BT AR AR CHW PS A m m p m p m p m p m p m p m = ( ) ⋅ ( ) ( ) + − ( ) ⋅ , , , , , , ; ; 1 " , , ; ; p m p m p p m M SHW CHW ( )   = + ∑ 1 B A SHW BR SHW BT AR AR CHW PS A m m p m p m p m p m p m p m = ( ) ⋅ ( ) ( ) + − ( ) ⋅ , , , , , , ; ; 1 " , , ; ; p m p m p p m M SHW CHW ( )   = + ∑ 1 (1) B m Number of boarders at stop m [-] BR m,p Number of randomly arriving boarders at stop m with destination p [-] BT m,p Number of timetable-oriented arriving boarders at stop m with destination p [-] A Ratio of randomly arriving boarders [-] AR m,p Average arrival rate of boarders at stop m with destination p [Pass/ h] SHW m,p Scheduled headway on the origin-destinationrelation from m to p [sec] CHW m,p Current headway on the origin-destinationrelation from m to p [sec] PS Probability step [-] M Total number of stops [-] Ini tial Query input data from the user Determine random numbers for the variables delays, passenger volume, passenger behavior parameters, stopping accuracy for current day of operation Determine the number of boarding passengeres at the current scheduled stop Determine the distribution of boarding passengers among the vehicle doors at the current scheduled stop Determine the cumulative distribution function of the dwell time at the current scheduled stop Calculate the weighted mean of the CDFs of all probability steps per scheduled stop Calculate the mean of the CDFs of all days of operation per scheduled stop Provide results to user Final Done for al l days of operati on? Done for al l scheduled stop? Done for al l probabi lity steps? Choose next scheduled stop Choose next probability step Choose next day of operation yes n o yes yes n o n o Figure 1: Procedure of the presented dwell time model Source: Authors SCIENCE & RESEARCH Rail operations International Transportation (71) 1 | 2019 36 This procedure allows the modelling of busy line sections with short headways (mainly in the city centers), where passengers predominant arrive randomly and thus almost low delays result in a significant increase of the amount of boarders. Hence, modelling of the building up of delays can be done. The use of the parameter A also enables the modelling of line sections with long headways (mainly outside the city center), where this effect is less important. Passenger distribution among the vehicle doors For the passenger exchange time at a stop, the distribution of the passengers among the vehicle doors is of particular importance, whereby a uniform distribution minimizes the time required [3, 4]. Since, however, it cannot be assumed that the users of the model know the distribution of waiting passengers along the platform length at each stop, this is also modeled. In order to determine the distribution of passengers, it is assumed that passengers, when positioning on the platform of their departure point, orient either towards the circumstances of their departure (platform accesses, weather protection, usual vehicle stop position) or their destination point (platform exits). Using the existing knowledge (e. g., [11, 12]) as well as results of further investigations about the factors influencing passengers’ distribution along the length of a platform carried out at our institute [13], an analytic model was developed, which allows to predict the passengers´ distribution for a certain platform situation. Thereby based on the infrastructure data entered by the user, a density function of the probability of the position chosen by a waiting passenger among the platform can be derived for the respective stop. Taking into account the vehicles door arrangement and stop position, this can be used to distribute the boarding passengers at a stop among the doors. Hence, the vehicle occupancy and the numbers of alighting passengers are determined based on the results of previous stops. Three redistribution steps then adjust the calculated numbers of boarders and alighters as well as the vehicle occupancy. These redistribution steps are simulating the reactions of passengers to the different occupancy rates of the individual platform areas before the arrival of the vehicle, in the case of overfilling of individual vehicle entry sections as well as to the different occupancy rate after boarding in the various areas of the vehicle. Therefore, the situation is modeled as a classical study of transport theory, whereby the number of supernumerary or missing passengers in the individual areas is regarded as supply or demand quantity and the distances between the sections as the transportation costs. Cumulative distribution function of-the-dwell-time To determine the cumulative distribution function (CDF) of the dwell time at a scheduled stop, the CDFs of the time required for the partial processes are first determined and then summarized by convolution. First the method used for door opening is determined considering the technical possibilities of the vehicle and the specific operating situation. Hence the CDF of the vehicle-specific time requirement before the start of the passenger exchange (inter alia door release) is determined. Subsequently, the CDF of the period up to the end of the regular boarding process is calculated separately for each door of the vehicle. This is determined by convoluting the CDFs of the time required for the doorspecific processes before the passenger exchange (inter alia door opening), the alighting process and the boarding process. The CDF of the alighting as well as the boarding process are modeled as a queueing-theoretical pure death process. This assumes that there are initially a definite amount of jobs (in this case boarders or alighters) which have to be processed (in this case pass the door) one after the other with a specific rate. This so called death-rate as well as whose variation are adjusted by the model to fit the death-process matching with varying situations at a platform. Thereby the effects of geometric constraints (inter alia door width and height difference) and interactions between passengers (inter alia congestion at high occupancy, interactions between boarders and alighters) on the boarding or alighting rate are considered by varying the death rate [7]. In practice, however, not all boarders are already waiting on the platform when the vehicle arrives, but there also late runners, who arrive only at the platform when the boarding already has begun. The amount of Figure 2: Comparison of the model calculated mean values with at least 20 measured values per stop for the center part of a suburban railway line (one direction) at seven stops Source: Authors Figure 3: Comparison of the model calculated CDF with 54 measured values for a busy station of a suburban railway line (one direction) Source: Authors Rail operations SCIENCE & RESEARCH International Transportation (71) 1 | 2019 37 those late runners at a certain stop is calculated inter alia considering the scheduled headway and then distributed among the vehicle doors depending on the location of the platform accesses. At doors where late runners are expected, the CDF of the boarding process is not modeled as a pure death process but instead as a queueing-theoretical birth-death process with focus on the duration of the busy period. Thereby the death rate is specified as the boarding rate described above, while the birth rate is specified as the arrival rate of late runners at a vehicle door. This allows the calculation of the duration until the door first time has the possibility to be closed completely. Hence, the door closing procedure is also taken into account, which is determined considering the technical possibilities of the vehicle and the platform, the number of late runners and the current delay of the train. Overall, it is also possible to model the recurrent interruption of the door closing process by late runners. Finally, the CDFs of the individual doors are aggregated by maximum formation and then, considering the CDF of the time required for dispatch, the CDF of the dwell time at the scheduled stop is determined. Comparison of the model results with measured data Using the example of one direction of a suburban railway line in Stuttgart, the results of the model are compared with dwell times measured in real operation. The considered line is a cross-city route and shows a high passenger volume, especially in the extended city center. Figure 2 shows the mean values of the measured and modeled dwell times for the stops around the city center. Although the model is not fully calibrated yet, the comparison elucidates that the dwell times calculated by the model correspond well to the measured values. The average deviation of the measured and calculated mean values over all stops of the considered line is 2.7 seconds or 9.8 %. Figure 3 shows the CDFs based on the calculated and measured values for station 6, which is one of the stops located in the city center. A close match can be seen as it is the case for the other evaluated stations. Also first validations for light rail and commuter train courses let expect a high degree of forecasting quality. Conclusion and outlook The presented model enables a precise forecast of the distribution functions of dwell times in railbound systems. The model results can be used for timetable planning as well as due to the consideration of the relationship between delay and the dwell time extension for dispatching and performance investigation [14]. Because of the additional outputs, optimization potentials regarding vehicle, infrastructure and operating program can also be derived. The remaining discrepancies between the measured and the predicted values indicate further need for research regarding the arrival of passengers at the scheduled stops, the distribution of passengers along the platform and the behavior of late runners. Therefore as well as for model calibrations based on extensive data sets of light rail systems, suburban railways trains and commuter train systems further investigations are already running. ■ LITERATURE [1] Lin, T. (1990): Dwell time relationships for urban rail systems, Massachusetts, Masterthesis [2] Weston, J. (1989): Train service model - technical guide, London [3] Weidmann, U. (1994): Der Fahrgastwechsel im öffentlichen Personenverkehr, Zürich [4] Heinz, W. (2003): Passenger service times on trains, Stockholm [5] Buchmüller, S. (2005): Planung von Umsteigeanlagen, Zürich, Diplomarbeit [6] Lam, W.; Cheung, C.; Poon, Y. (1998): A study of train dwelling time at the Hong Kong mass transit railway system. In: Journal of advanced transportation, Jg. 32, H. 3, S. 285-296 [7] Uhl, J.; Martin, U.; Hantsch, F. (2018): Entwicklung eines bedienungstheoretischen Modells zur Bestimmung von Fahrgastwechselzeiten im spurgeführten Verkehr. In: Schönberger, J.; Nerlich, S. (Hrsg.): Tagungsband der 26. Verkehrswissenschaftliche Tage, Dresden, S. 625-640 [8] Uhl, J. (2018): Entwicklung eines bedienungstheoretischen Modells zur Bestimmung von Fahrgastwechselzeiten im spurgeführten Verkehr, Stuttgart, Masterthesis [9] Mathworks (2018): MATLAB - Matrix Laboratory, Version 2018a [10] Lüthi, M.; Weidmann, U.; Nash, A. (2007): Passenger Arrival Rates at Public Transport Stations. ETH Zürich - Research Collection. Zürich [11] Kim, H. et al. (2014): Why do passengers choose a specific car of a metro train during the morning peak hours? In: Transportation Research Part, Jg. 61, S. 249-258 [12] Rüger, B. (2017): Influence of Passenger Behaviour on Railway-Station Infrastructure. In: Fraszczyk, A.; Marinov, M. (Hrsg): Sustainable Rail Transport, S. 127-160 [13] Klose, M. (2019): Untersuchung der Einflussfaktoren auf die Verteilung der wartenden Fahrgäste über die Längsausdehnung eines Bahnsteigs. Stuttgart, Bachelorthesis [14] Steiner, J. (2019): Untersuchung der Zusammenhänge zwischen der Haltezeitcharakteristik und der Betriebsqualität auf einem Streckenabschnitt des spurgeführten Verkehrs. Stuttgart, Bachelorthesis Ullrich Martin, Prof. Dr.-Ing. Director, Institute of Railway and Transportation Engineering (IEV), University of Stuttgart, Stuttgart (DE) ullrich.martin@ievvwi.uni-stuttgart.de Johannes Uhl, M.Sc. Doctoral student, Institute of Railway and Transportation Engineering (IEV), University of Stuttgart, Stuttgart (DE) johannes.uhl@ievvwi.uni-stuttgart.de AUF EINEN BLICK Das vorgestellte Modell ermöglicht auf Basis von Eingabeparametern zur Infrastruktur, dem Fahrzeug sowie dem Fahrgastaufkommen eine linienbezogene Prognose der Verteilungsfunktionen von Haltezeiten spurgeführter Verkehrssysteme. Hierzu wird für jede Station zunächst das zu erwartende Einsteigeraufkommen sowie die Verteilung der Einsteiger auf die Fahrzeugtüren modelliert. Mit diesen Ergebnissen werden anschließend unter Verwendung bedienungstheoretischer Zusammenhänge die Zeitbedarfe für den Fahrgastwechsel sowie die weiteren Haltezeitprozesse ermittelt. Neben stochastischen Einflüssen werden auch Zusammenhänge zwischen den planmäßigen Halten sowie das Unterbrechen des Türschließprozesses durch Einsteigernachzügler berücksichtig. Erste Validierungen der Modellergebnisse am Beispiel einer S-Bahnlinie lassen auf eine hohe Prognosegüte schließen. International Transportation (71) 1 | 2019 38 Advanced automation in railway operations Impacts, requirements and potentials Automation, Energy consumption, Railway, Simulation Automation is already present in many areas of the railway sector. However, to achieve set climate goals, increase capacity, reduce costs and offer an attractive transport service, it is essential to systematically apply ATO (Automatic Train Operation) or higher Grades of Automation (GoA). This paper summarises the findings of a study regarding the impacts, requirements and potentials of higher automation in the railway sector. The analysis distinguishes between (i) mainlines and branch lines as well as (ii) passenger transport, freight and mixed traffic. Furthermore, results based on a model simulating energy consumption highlight the importance of energy-efficient driving. Martina Zeiner, Martin Smoliner A TO is considered a subsystem with different functions depending on the GoA (as defined by the UITP) and must be combined with ATP (Automatic Train Protection) to ensure safety. ATP together with DAS (Driver Advisory Systems) are classified as GoA1 and are already used by railways. GoA2 combines ATP and ATO; where ATO executes traction and brake commands. Much effort is currently put in field trials for GoA2, albeit one can also find existing examples of GoA2, such as the Thameslink project in London. GoA4 corre- New trends in transport systems For the 14 th consecutive time the European Platform of Transport Sciences - EPTS - awards a prize dedicated to young transport researchers. The prize is named “European Friedrich-List-Prize” to honour the extraordinary contributions of Friedrich List, the visionary of transport in Europe of the 19 th century, being a distinguished economist and respected transport scientist committed to the European idea. The European Friedrich-List-Prize is awarded for out-standing scientific papers in each of the categories Doctorate paper and Diploma paper. The submitted papers address topics in the transport field within a European context and from a European perspective. In 2019 around 150 scientific works have been nominated and evaluated. The award will be conferred during the 17th European Transport Congress in Bratislava (Slovakia) on 13th June 2019, and the results will be introduced on the website www.international-transportation.com. In the following you can find a small random selection of this year’s submissions summarized in drafts. SCIENCE & RESEARCH European Friedrich List Award Bratislava Photo: Džoko Stach on Pixabay European Friedrich List Award SCIENCE & RESEARCH International Transportation (71) 1 | 2019 39 sponds to fully automatically run vehicles and until now has only been applied in urban metro lines. Requirements for higher automation in the railway sector are listed in figure 1. Since ATP is a safety requirement as of GoA2 and to ensure interoperability, many institutions and suppliers support the idea of ATO over ETCS. Efforts are currently underway to incorporate new specifications for GoA1 and 2 in the TSI [2]. Furthermore, adaptions in national legislation, liability issues (of trial runs), certification issues and harmonised authorization processes have to be considered and solved. To ensure the safe guidance of a train a continuous ATP must be implemented and continuous information, usually known to the driver, needs to be submitted to the ATO. In Europe ETCS Level 2 is regarded as the basis for ATO. However, the current infrastructure and slow migration process of ETCS makes the use of a harmonised, sophisticated ATP very unrealistic. ATP solutions based on satellites should thus be examined together with migration concepts in case of ATP other than ETCS-[3]. In order to increase energy efficiency and punctuality ATO has to be combined with DAS (providing an optimised speed profile for one train). To optimise train movements in a whole network, ATO must be connected to a cross-network Traffic Management System (TMS). This would mean adapting trajectories to the current state of the traffic continuously to avoid unnecessary stops, reactionary delays or conflicts. One approach is known as dynamic capacity optimisation; it is based on an automatically computed timetable in real-time combined with ATO and can reduce headways (90 to 100-sec.)-[4]. Technical equipment on wayside and trainborne level will need to be adjusted depending on the GoA. As of GoA3, the train has to take over the driver’s visual functions. For wayside obstacle detection, solutions stem from drone-based cameras to fibre optic sensing. The installation of laser or radar sensors combined with image processing at level crossings or fences at platforms are conceivable solutions [5]. As for on board obstacle detection the combined installation of radar, infrared, laser or cameras is suggested because of different characteristics in reach and dependence of weather-[6]. Different systems can benefit from increasing automation according to their boundary conditions. Capacity problems are particularly prevalent in passenger transport, especially on mainlines. Solutions as of GoA2 in connection with TMS show great potential in passenger transport and mixed traffic for coping with peak demand in hubs [4]. The need for additional infrastructure (as of GoA3) could therefore be replaced by means of a dispositive level. Comfort can already be achieved as of GoA2, since ATO can balance e.g. aggressive styles of driving. The use of TMS reduces waiting time, increases reliability and punctuality which has added value for freight and passenger transport. In a first step this can already be achieved to a certain degree with DAS. Introducing TMS plus fully automatically run vehicles on branch lines could bring about a cost-effective and demand-based transport service. There is a common understanding that safety increases by taking out the human factor. However, risks caused by new technologies as of GoA3 must be less in character than the human-risk factor in terms of cyber security, failures of providers, manufacturers or systems. Recently developed “intelligent” vehicles (equipped with a centre buffer coupling and being able to perform an automatic brake test) could replace the remaining manual work of coupling processes [7]. Safety in shunting could thus be increased considering the high risk of accidents. A useful way to save energy is to exploit the acceleration, cruising, coasting and braking phase more energyefficiently. An energy calculation program [8] was elaborated to depict different driving behaviours and estimate the respective energy consumption. The model is based on the total train resistance which occurs along a train journey on a random route and can be expressed in energy needed for that section. Energy consumed by auxiliary functions is also considered. Different driving styles were computed for a section on one Austrian mainline taking into account five train types (passenger and freight); energy recovery remained unconsidered. Figure 2 displays three speed profiles for a local regional train. Case maximum top speed demonstrates a tight speed profile which might counteract a reactionary delay. Energy-efficient driving is shown by limiting the top speed and yet arriving on time (considering 5 % Fully automatic Train-Operation Operational- requirements Operational-rules and regulation Traffic-Management-(Dispatching,-TMS) Operation-according to demand Safety &-Security Certification Authorisation Tests-&-Validation Liability Vehicle / -Infrastructure Localisation / -Positioning Communication Obstacle Detection ATO-over ETCS Interoperability Legal-and normative- framework Technical- requirements Figure 1: Requirements for automation according to [1] International Transportation (71) 1 | 2019 40 buffer time). Case 3 represents the same train without any stops. The ratio of the train resistances shows that the major part of consumed energy can be led back to acceleration resistance. A top speed reduction can save up to around 50 % of energy compared to a tight speed profile. The decrease in energy consumption generates a 35 % higher running time which highlights that the degree of energy reduction is not wedded to the degree of increase in travel time. To a certain extent the results must be treated with caution, since in reality a train will not accelerate up to the permitted top speed every time before a stop. It can be assumed that the energy savings will be lower; better corresponding to what can be found in the literature, e.g. [9]. Furthermore, the energy-efficient driving profiles only concern the optimisation of a single train. The optimisation of several trains might lead to less reduction for the single train. An appropriate TMS is therefore necessary to save energy on a networkwide level. Investigations on freight trains confirm the need of an improved management of traffic. Around 35 % of energy could be saved on that route without putting freight trains aside. Energy-efficient driving could reduce energy costs by 10 % on average for one train in one year [10]. This could in particular increase the competitiveness of freight traffic. Whereas cost cuts by replacing drivers is a doublesided issue, the economic benefit in shunting is certain due to the possible decrease in manual labour. The outcome of the study underlines the importance of elaborating automation on a dispositive level (at best cross-network TMS) if energy and capacity improvement are to be achieved. Hence, punctuality along with cost-effective offers bring added value to the customer and boost the railway sector. In case of passenger and mixed traffic, energy savings and capacity increases can already be achieved with GoA1 and 2. The results of different driving behaviours underpin the benefits of DAS and ATO. One potential in freight transport can be exploited with “intelligent” freight trains improving safety and reducing costs. Nevertheless, one must consider side effects of higher levels of automation in terms of technical requirements as well as in the context of legal aspects. Moreover, there are limits to operational optimisation; results of the energy calculation show that auxiliary functions consume one sixth of the total energy consumption. Opportunities for more environmentallyfriendly vehicles should thus be contemplated. Finally, it should be noted that the study only focuses on electrified railways. On non-electrified lines the focus should also be placed on alternative traction technologies. ■ REFERENCES [1] M. Meyer zu Hörste, Fully automatic railway operation: Concept and Conditions, Vienna, 28.11.2017 [2] R. Treydel, Development of the harmonised European specifications for mainline ATO, Vienna, 28.11.2017 [3] J. Trinckauf, “Der Bahnbetrieb auf dem Weg zur Digitalisierung und Automatisierung,” Deine Bahn, no. 09, pp. 7-9, 2017 [4] U. Weidmann et al., “Dynamische Kapazitätsoptimierung durch Automatisierung des Bahnbetriebs,” Eisenbahn-Revue, no. 12, 606-611, 2014 [5] J. Pachl, “Betriebliche Randbedingungen für autonomes Fahren auf der Schiene,” Deine Bahn, no. 09, pp. 11-19, 2017 [6] O. Gebauer et al., “Autonomously driving trains on open tracks— concepts, system architecture and implementation aspects,” it - Information Technology, vol. 54, no. 06, pp. 266-279, 2012 [7] B. Müller-Hildebrand, “Automatisierung und Digitalisierung im Schienengüterverkehr,” ZEVrail, no. 08, pp. 301-303, 2017 [8] M. Messner, Berechnung des Energieverbrauchs für Triebfahrzeuge, TU Graz, 17.05.2014 [9] M. Marinov, ed., Sustainable Rail Transport: Proceedings of RailNewcastle Talks 2016, Springer International Publishing, 2018 [10] J. Winter et al., “Fahrerassistenz-System,” Signal + Draht, no. 10, pp. 6-14, 2009 Martina Zeiner, Dipl.-Ing. BSc University Assistant, Institute of Railway Engineering and Transport Economy, Graz University of Technology (TU Graz), Graz (AT) martina.zeiner@tugraz.at Martin Smoliner, Dipl.-Ing. BA BSc MA University Assistant, Institute of Railway Engineering and Transport Economy, Graz University of Technology (TU Graz), Graz (AT) martin.smoliner@tugraz.at 0 50 100 150 200 250 300 350 400 450 500 0 50 100 150 200 250 300 350 400 450 500 max.-top speed top-speed reduction no-stops 0 time-[min] Energy-consumption-[kWh] Energy-consumption-due-to-train-resistance-and- auxiliary-functions-for-different-driving-styles Auxiliary-Functions Starting-Resistance Acceleration-Resistance Resistance-according-to-alignment Running-Resistance calculated-running-time max.-allowed-running-time max. top---------top-speed-------no-stops speed------------reduction 0 160 0 50 Velocity--[km/ h] Distance-[km] Speed-profiles-for-different-driving-styles permitted-top-speed max.-top-speed top-speed-reduction no-stops Figure 2: Results of the simulation model SCIENCE & RESEARCH European Friedrich-List-Prize European Friedrich List Award SCIENCE & RESEARCH International Transportation (71) 1 | 2019 41 Challenging assumptions about traveller behaviour The benefits and challenges of using Bluetooth data to examine repeated behaviour Big data, Travel behaviour, Variability, Bluetooth data Emerging data sources provide new opportunities to test how well long held assumptions in transportation reflect reality. This article presents a case study which uses one year of data from 23 fixed Bluetooth detectors to examine the regularity of individual travel behaviour over time. New insights were obtained into the relationship between spatial and time of day variability and the proportion of travellers with very regular travel patterns. This type of research is challenging, however, due to the large amounts of data involved and the need to develop new methods to analyse the data. Fiona Crawford S implifying assumptions are a necessary part of the transport modelling process. Without such assumptions we would be overwhelmed by the complexity of people’s travel behaviour and the countless types of variability present. In some cases, long standing assumptions which were originally devised when data was scarce have become so ingrained that they often go unspoken and unchallenged. As the amount of data available about Figure 1: Map of Bluetooth detector locations in the Wigan area (from-[1]) SCIENCE & RESEARCH European Friedrich List Award International Transportation (71) 1 | 2019 42 User class Subclass Number of devices Number of trips Average trip frequency Spatial variability Time of day variability Frequent travellers F1 16,634 1,144,115 1-2 per week More variable More variable F2 8,163 820,221 1-2 per week Less variable Less variable F3 3,089 815,504 5 per week Less variable Less variable F4 5,809 1,590,437 5 per week More variable More variable Very frequent travellers V1 1,901 1,302,874 2 per day Highly variable Average of F3 and F4 V2 195 349,002 5 per day Highly variable Average of F3 and F4 Table 1: Summary table of frequent and very frequent travellers in the Bluetooth case study both the transport network and traveller behaviour grows rapidly, so do the opportunities to examine the validity of different assumptions. Individuals’ travel behaviour is often assumed to be highly repetitive, particularly during the peak period, with the same people being observed on the roads each day. Such assumptions are increasingly questionable given changes in working hours and the locations in which work is undertaken. Newly emerging types of data open up new opportunities to examine people’s repeated trip behaviour to determine how repetitive their travel choices are. This article describes research focusing on the regularity of individuals’ behaviour using data from fixed Bluetooth sensors, which are relatively cheap to purchase and can be permanently installed to collect data for large numbers of travellers over long periods of time. A case study using Bluetooth data Bluetooth is a wireless technology which is used in a vast array of personal devices, including mobile phones and fitness trackers. The technology is also commonly used in vehicles, for example to enable hands-free calls or to connect devices to in-vehicle sound systems. A key aspect which makes the technology useful for transport planners is that each device has a unique identifier, known as a MAC address, which can be captured by fixed Bluetooth detectors. A number of cities around the world have successfully used the technology for measuring travel times by matching devices between detector locations using their MAC addresses. The potential to match MAC addresses between days has not been explored previously, however. This research examined the different groups of road users (in motorised vehicles) which exist in a case study area, based on their repeated trip behaviour. The objective was to assess the assumption that most traffic is the result of people travelling to the same place at the same time each day. The case study is within Greater Manchester in the north of England, where over 500 fixed Bluetooth sensors have been installed alongside the road network for the purpose of monitoring travel times. This research focuses on 23 detectors in and around the town of Wigan, as shown in figure 1. Data from 2015-01-01 to 2015-12-31 was analysed. Processing and analysing the data was challenging. Firstly, consecutive observations for the same device needed to be identified and then filtered so that only observations which were likely to relate to a direct trip between the two Bluetooth detectors in a motorised vehicle were retained. This process involved examining the free flow travel times between each pair of sensors, but also the travel times recorded by surrounding Bluetooth devices (see [1] for more details). Determining the best way to examine repeated trip behaviour using the data was also challenging. The data is relatively similar to the data obtained from Automatic Number Plate Recognition (ANPR) systems, but the scale of data (including the time period over which unique identifiers could be matched and the number of detectors) was relatively large. Bluetooth data also has the added complication that not all Bluetooth-enabled devices will be detected when passing a sensor and therefore one missing detection within a trip through the town is more common than it would be for ANPR data. It was straightforward to calculate a lower bound for the number of trips made by each Bluetooth device during the year. The number of trips is an underestimate of the trips made by the device as not all Bluetooth enabled devices passing a sensor will be detected, and devices can only be detected if they are switched on and the Bluetooth functionality is enabled. It may also be an underestimate of the number of trips made by the owner of the device, as the device may not be taken on all trips. Measuring the spatial and time of day variability for each device, however, was much more challenging. In both cases, new methodologies were required. For spatial variability, the distribution of each traveller’s trips across different ‘spatial sets’ was measured. These ‘spatial sets’ were defined by grouping together similar trips (denoted by the list of sensor locations at which a device was detected). The grouping used a method called Sequence Alignment to compare trips based on their components (the sensors where detections were made) and the order in which they appear. Sequence Alignment is used in bioinformatics to compare protein sequences and has also been used in the social sciences [2]. Time of day variability was measured for each traveller by identifying clusters of the times of day they are observed at their most common sensor location and then examining the number of clusters and the average variance of the clusters. After producing measures of trip frequency, spatial variability and time of day variability for each traveller, different user classes could be identified within the data. Of the 7,480,204 trips which were recorded by Bluetooth detectors during the year, 81 % were made by devices European Friedrich List Award SCIENCE & RESEARCH International Transportation (71) 1 | 2019 43 which were classified as frequent or very frequent travellers during the analysis process. The six subclasses of Bluetooth devices in these categories are shown in table-1. The frequent travellers can be separated into two groups based on trip frequency (F1 and F2 versus F3 and F4). Each of these groups contains one subclass which is larger and has higher levels of both spatial and time of day variability for individual travellers. Even when considering just 23 sensors around a relatively compact town centre, there appears to be many travellers who are not highly regular in their trip making. The very frequent travellers are also of interest as 22 % of the trips observed in the Bluetooth data are made by this group. These travellers have similar time of day variability to F3 and F4, but they have more spatial variability. These travellers make trips in more of the different ‘spatial sets’ than frequent travellers, but as they make so many more trips during the year, these very frequent travellers also repeat trips more often. Given that the estimated number of trips is a lower bound on the actual number of trips made, it is possible that these devices are not recording personal travel, but travel related to business activities. Bluetooth data therefore provides a different perspective from travel diaries which typically collect data on personal travel only. Challenges and opportunities This small case study has demonstrated that new insights, in this case into repeated trip behaviour, can be obtained from newly emerging data sources. In this example, Bluetooth data was used. The substantial amount of research which has been undertaken to examine Bluetooth data’s usefulness for measuring travel times (including [3] and [4]) provides some confidence in the data, but different kinds of research are required if the data is being considered for a different type of application. For example, more research is required into how people use the wide range of Bluetooth-enabled devices now available, such as fitness trackers and in-vehicle audio systems. Trust in the data is only part of the challenge, however. Processing large quantities of data can be time consuming and may require large amounts of computing power. New analytical techniques may be required to gain insights from the data, perhaps from fields such as data science. Therefore, whilst more and more opportunities are opening up to allow us to challenge long held assumptions about travel behaviour, it is essential that we ensure, as a community, that we are developing the necessary skills and methods and ensuring that we have access to the resources we need to make the most of the opportunities ahead. ■ The data used in this research was provided by Transport for Greater Manchester REFERENCES [1] Crawford, F., Watling, D.P. and Connors, R.D. (2018), Identifying road user classes based on repeated trip behaviour using Bluetooth data. Transportation Research Part A: Policy and Practice. 113 pp. 55-74. doi: 10.1016/ j.tra.2018.03.027 [2] Crawford, F. (2017), Methods for analysing emerging data sources to understand variability in traveller behaviour on the road network. University of Leeds. Available at: http: / / etheses.whiterose.ac.uk/ 18758/ 1/ Crawford_F_ITS_PhD_2017.pdf [3] Quayle, S.M., Koonce, P., Depencier, D. and Bullock, D.M. (2010), Arterial Performance Measures with Media Access Control Readers: Portland, Oregon, Pilot Study. Transportation Research Record. 2192 (1), pp. 185-193 [4] Araghi, B.N., Olesen, J.H., Krishnan, R., Christensen, L.T. and Lahrmann, H. (2015), Reliability of Bluetooth Technology for Travel Time Estimation. Journal of Intelligent Transportation Systems. 19 (3), pp. 240-255 Fiona Crawford Research Fellow in Transport Studies, Centre for Transport and Society, University of the West of England, Bristol (GB) fiona.crawford@uwe.ac.uk Risk analysis of dangerous goods transportation Risk analysis, Dangerous goods, Human resources, Risk mitigation The paper deals with transport of dangerous goods by road (ADR). Main contribution is the development of the algorithm for evaluation and management of human factor risks in the field of dangerous goods transport. There is presented a systematized approach and unambiguously structured the gradual use of qualitative, quantitative and semiquantitative methods for the risk assessment. Following methods are used: Check-list; What, if; Failure Modes Effects and Causes Analysis (FMECA); Human Reliability Assessment (HRA), Fault Tree Analysis (FTA). Libor Krejčí T ransportation of dangerous goods takes place as an important part of European economy. Due to the nature of dangerous goods (e.g. chemicals, petroleum products, explosives), such substances are causing significant threat to road or railway users, inhabitants, infrastructure and the environment. A certain degree of risk is at any stage of dangerous goods manipulation, during its production, storage, han- SCIENCE & RESEARCH European Friedrich List Award International Transportation (71) 1 | 2019 44 Figure 1: Flow chart for the risk analysis of dangerous goods transportation dling, transportation and consumption. Therefore the international legislation standards set a legal framework for dangerous goods transportation in Europe. Dangerous goods transportation by road is regulated by the Agreement concerning the international carriage of Dangerous Goods by Road (ADR) [1]. Even though there are the regulations in this area focused on training of drivers, dangerous goods safety advisors and other professionals in the supply chain, still the human factor plays a major role, causing the most accidents in this transport sector. Qualitative, quantitative and semi-quantitative methods for the risk analysis and assessment had been employed to address an unacceptable risk. Author determined the technological process for the application of the individual methods in gradual steps, structured the evaluated areas, set the evaluation criteria and set the unacceptable risk limits for the individual quantitative methods. Methods used Check-list - the method was used to identify all the major causes of previous accidents related to human factor in the dangerous goods transportation. All the identified hypothetical causes of accidents related to handling of dangerous goods were included into a risk register and then compared with relevant international databases of past accidents Major Accident Reporting System (eMARS) [2] and National Transportation Safety Board (NTSB) [3]. Risk areas with a reference to any accident in the databases were further analysed with more systematised follow up methods. What, if - the method enables to identify risks across the broader spectrum of the dangerous goods transportation processes. A group element of the method was used to stimulate the cooperation and creation of various scenarios. A multidisciplinary risk analysis team was set up for this purpose, consisting of risk manager, driver transporting dangerous goods in packages, driver transporting dangerous goods in tank vehicle, warehouse worker handling dangerous goods, dangerous goods safety advisor, policeman and fireman. Failure Modes Effects and Causes Analysis (FMECA) - the group inductive method for assessing probability of occurrence, impact and detection of undesired events. For the purposes of the analysis, the system was defined as a transportation process of dangerous goods by road and the failure a road traffic accident, which could lead to the leakage of dangerous goods. The FMECA analysis extends the (Failure Mode and Effects Analysis) FMEA analysis by classification each type of failure. Human Reliability Assessment (HRA) - the method was used to target the impact of human activity on the functionality of the dangerous goods transportation system. The qualitative and quantitative forms of the method were used. The qualitative form to identify the possibility of human errors and their causes in order to reduce the probability of errors. The quantitative form of the method provided input data on human failure into the follow-up analysis. Fault Tree Analysis (FTA) - the method was used primarily for a highly systematic approach, allowing sufficient flexibility for the analysis of different factors at the same time. The undesirable peak event was a road traffic accident, which could lead to the leakage of dangerous goods. Causal factors were partly utilized from the previous methods applied. Other factors induced by this method were included into the follow-up analysis as well. At the final stage of the risk assessment, the calculation of the individual and social risk for the population during the transportation of dangerous goods was done using the F/ N curves (frequency “F” where a certain number of “N” consequences may occur). Finally, determination of the acceptability of individual and social risk caused by dangerous goods transportation, during handling and along the road was provided. In order to asses risk mitigating measures the determination of the ALARP (As Low As Reasonably Practicable) zone was utilised. Gradual implementation of the methods for risk identification, impact analysis and risk assessment is illustrated in figure 1. Conclusions All the aforementioned methods had been applied in a number of fields of human activities (engineering, petrochemical, industrial), where the continuous risk control is important. However, utilisation of these methods has Start risk analylis of dangerous goods transportation 2 Current scienti c approach 3 Accident databases 4 Scienti c methods 1 State of the art 5 Check-list 6 What, if 7 FMECA 8 HRA 9 FTA 10 Acceptable risk? 11 Risk mitigating measures 12 Prevention of accidents 13 Mitigation of accident consequences End - acceptable risk + - European Friedrich List Award SCIENCE & RESEARCH International Transportation (71) 1 | 2019 45 been rather rare and isolated in the area of dangerous goods transportation. The developed algorithm for evaluation and management of human factor risks in the area of dangerous goods transportation will help to reduce the risk caused by the most risky human factor. Implementation by the carriers, consignors (loader, packer, filler, tank-container/ portable tank operator) and consignees (unloader) of dangerous goods, minimizes the potential negative impacts and contributes to the overall safety improvement in Pan-European region. It is always necessary to implement the algorithm in full extent as designed by the author. It is not appropriate to implement the individual methods individually or independently of each other, in order to obtain only certain partial knowledge. There are mutual relationships among the methods included into the algorithm. These methods complement each other and allow approaching the risk area from an aggregated viewpoint to individual sub-processes. Only with the systematic gradual implementation of individual methods, it is possible to gain the full advantage of their distinctions. The author prepared the set of measures to address the risk of dangerous goods transportation by road. Even though these measures are beyond the scope of this article, all the measures are aimed at prevention of accidents or mitigation of their potential negative impacts on population, infrastructure and the environment. Exploitation of the common developed algorithm addressing the risks in the whole dangerous goods supply chain is going to be facilitated especially by the dangerous goods safety advisors, who are the main drivers of safety and security in target companies. As a consequence risk mitigating measures shall be adopted by other professionals in the dangerous goods transportation. ■ REFERENCES [1] United Nations Economic Commission for Europe (2019), European Agreement concerning the International Carriage of Dangerous Goods by Road (ADR), https: / / www.unece. org/ trans/ danger/ publi/ adr/ adr2019/ 19contentse.html [2] European Commission (2019), eMARS (the Major Accident Reporting System), https: / / emars.jrc.ec.europa.eu/ [3] National Transportation Safety Board (2019), Hazardous Materials Accidents Reports, http: / / www.ntsb.gov/ investigations/ AccidentReports/ Pages/ hazardous.aspx Libor Krejčí, Ph.D. CDV - Transport Research Centre, Brno (CZ) libor.krejci@cdv.cz TSCLab - Traffic Signal Control Laboratory A tool for performance monitoring and evaluation of adaptive traffic signal control in VISSIM TSCLab, VISSIM, Signal control, Measures of effectiveness Adaptive Traffic Control Systems (ATCS) have been widely implemented for urban traffic control due to their capability to alleviate congestion. The evaluation of the effectiveness of complex ATCS is challenging and presents an open problem. The most important issue is to identify whether the ATCS fulfills the goals envisioned to be achieved. In this paper, development of TSCLab (Traffic Signal Control Laboratory), a MATLAB based tool for evaluation of ATCS is presented. To proof the capabilities of TSCLab, the effectiveness of the UTOPIA/ SPOT ATCS as the use case has been evaluated. Daniel Pavleski P rior implementation of an ATCS in an urban environment, it has to be evaluated using realistic traffic scenarios. This is important in order to assess the possible improvement of LoS and to analyze the cost-benefit ratio before a costly upgrade of the transport infrastructure [1]. Mostly used approach for this is software-in-the-loop where a microscopic traffic simulator in combination with an ATCS is applied [2]. Many researchers address this problem in order to implement an appropriate framework, ensure realistic traffic scenarios from different world regions, enable in-depth behavior analysis of the managed urban transport network and define appropriate MoEs. This paper also tackles the problem of evaluating ATCS using the software-in-the-loop approach. SCIENCE & RESEARCH European Friedrich List Award International Transportation (71) 1 | 2019 46 Figure 1: Diagram for monitoring of the signal time changes and vehicle arrivals [6] Figure 2: Table with results related to progression [6] ATCS performance selection In the absence of information for the performance of adaptive traffic signal control, the quality of signal operations cannot be determined, and the functionality of the control strategies cannot be validated [3]. Therefore, the monitoring of these so-called “live” systems i.e. the monitoring of their real-time “responses” to certain traffic state and a specific objective function is crucial. Due to the complexity of ATCS, the process of evaluating their effectiveness requires using of measures of effectiveness with in-depth insights into the traffic situations of the controlled signalized intersection. The available literature describes a variety of MoEs that can be used for ATCS performance analysis [3, 4, 5]. In the report [3], are outlined candidate Measures of Effectiveness (MoE) for each operational objective and each MoE is denoted as a candidate since it is not necessary to calculate or compare all of the measures to validate the functionality of a system. The report [4] documented an extensive portfolio of performance measures for evaluating traffic signal systems with emphasis on performance measures obtained from high-resolution data and from external travel time measurements. The report [5] presents the next step toward integrating traffic signal performances measures in traffic signal systems facilitated by high-resolution controller event data. The mentioned reports have been used as a starting point and base for selection of MoEs for ATCS performance evaluation. Selected measures that have been used for ATCS performance evaluation in this paper are: • Cycle length and Green time duration; • Maximum green time utilization ratio; • Arrived vehicles per cycle; • Served vehicles per green signal; • Green/ Red occupancy ratio; • Queue length; • Delay; • Stops; • Percent of arrived vehicles on green signal; • Platoon ration & Arrival type. Development of TSCLab - Traffic Signal Control Laboratory In order to apply the selected measures for performance evaluation of ATSC which are not featured in VISSIM, a MATLAB based tool named TSCLab (Traffic Signal Control Laboratory) with graphical user interface was developed and connected to VISSIM trough COM interface of VISSIM. This tool can access, gather and visualize relevant data generated by VISSIM, needed for calculation of selected measures for performance evaluation of ATSC in VISSIM based microscopic simulation environment [6]. The main graphical user interface of the TSCLab tool is consists of three main sections. The first section refers to measurements assignment where objects defined in the VISSIM model such as Nodes, Data collection Measurements, and Queue Counters, can be specified. The second section refers to traffic signal control assignment where objects defined in VISSIM model such as Traffic Light Controller, Signal Groups, Detector ports, and Detectors, can be specified. The third section is related to simulation where parameters for simulation in a VIS- SIM model such as Start/ End of Time Period and Resolution can be specified. TSCLab enables diagrams for real time monitoring of: • Signal Times/ Detector occupancy; • Signal Times/ Vehicle arrivals; • Max green time utilization ratio; • Vehicle arrivals per cycle; • Served vehicles per green time; • Green/ Red occupancy ratio; • Vehicle queue link profile; • Percent of vehicle arrivals on green time; and • Vehicle Platoon ratio & Arrival type. An example of diagram for real time monitoring is shown in figure 1. In addition, TSCLab enables tables with outputs that refer to: (i) Signal timing; (ii) Throughput; (iii) Capacity and (iv) Progression. An example of output data related to signal timing is shown in figure 2. Evaluation of ATSC - a case study To proof the capabilities of the developed TSCLab tool it has been applied to evaluate the effectiveness of the UTOPIA/ SPOT ATCS using an isolated signalized urban intersection as case study. A VISSIM model for urban network of seven signalized intersections located in wider central area of Skopje was developed and the Intersection denoted as I2, was selected for testing. All signalized intersections in the study area are managed by the ATCS UTOPIA. Therefore, UTOPIA as a ”black box” was connected to the developed VISSIM model through the UTOPIA VISSIM Adapter (UVA). With this connection, UTOPIA manages the traffic signals for the simulated road network and VISSIM provides the needed European Friedrich List Award SCIENCE & RESEARCH International Transportation (71) 1 | 2019 47 traffic data from the sensors. Both, traffic signal commands and sensor measurements are refreshed every second [6]. In order to evaluate the performances of ATCS UTO- PIA, the morning peak hour (7: 15 to 8: 15) in a typical working day was chosen for analysis. To create realistic simulation model in VISSIM the following information have been obtained: 1) Network layout; 2) Familiarity with site operation and driver behaviour; 3) Traffic flows and turning proportions; 4) Traffic flow compositions; 5) Bus frequencies; 6) Bus stop locations; 7) Bus stop dwell times; 8) Signal timings and controller logic; 9) Saturation flow; 10) Queue lengths; and 11) Mandatory speed limits. A simulation period of 5,400 seconds divided into six time intervals of 900 seconds was defined in VISSIM. The first interval represents the warm-up period, the second, third, fourth and fifth periods represent the peak hour and the last interval represents the cool down period. The vehicle inputs for each time interval are determined on the basis of processed automatic vehicle counting data obtained from the Traffic Management and Control Centre (TMCC) in Skopje. The calibration procedure described in [7] was applied for the calibration of the VISSIM model. The saturation flow was selected as the parameter for validation of the model. Because the saturation flows appear to be modeled incorrectly uniformly across the network, the parameters of the global ”driver behavior” model: average standstill distance, additive part of safety distance and multiplicative part of safety distance were adjusted to comply with the validation criteria. Obtained results with average values per approach for the selected intersection I2 in the chosen analysis period are shown in table 1 [6]. Conclusion and future work The aim of this research paper is to provide a performance evaluation of ATCS applied in today’s UTC centers. For this, a simulation framework was developed to enable a software in the loop simulation of the adaptive traffic control UTOPIA using the microscopic simulator VISSIM. It contains a new MATLAB based tool named TSCLab which can access, gather and visualize relevant data generated by VISSIM, needed for calculation of selected measures for performance evaluation of ATSC in VISSIM based microscopic simulation environment using different traffic scenarios. TSCLab provides the operators in the traffic management and control center a possibility to test the control strategy and if it is necessary, to change the algorithm parameters prior to being implemented on the field. Future work on this topic will be related to the augmentation of TSCLab to enable evaluation using a simulation model containing more controlled signalized intersections in an urban network and comparison of different control strategies. Additionally, new measures for performance evaluation especially those related to sustainable modes of transport (public transport, cycling and walking) will be considered also. ■ REFERENCES [1] I. Dakic, M. Mladenović, A. Stevanović, and M. Zlatkovic (2018), Upgrade Evaluation of Traffic Signal Assets: High-resolution Performance Measurement Framework, PROMET - Traffic&Transportation, 30(3): 323-332 [2] D. Pavleski, D. Nechoska Koltovska, and E. Ivanjko (2017), Development of TSCLab: A tool for evaluation of the effectiveness of adaptive traffic signal control systems, In: Proceedings of 5th International conference NT-2019: 386-394 [3] D. Gettman, et al. (2013), Measures of Effectiveness and Validation Guidance for Adaptive Signal Control Technologies, US Department of Transportation, Federal Highway Administration [4] C. Day, et al. (2014), Performance Measures for Traffic Signal Systems: An Outcome-Oriented Approach, Purdue University, West Lafayette, Indiana, USA [5] C. Day, D.M. Bullock, H. Li, S.M. Lavrenz, W.B. Smith, and J.R. Sturdevant (2015), Integrating Traffic Signal Performance Measures into Agency Business Processes, Purdue University, West Lafayette, Indiana, USA [6] D. Pavleski (2018), Performances Evaluation of Adaptive Traffic Signal Control in Microsimulation Environment, Master thesis, Faculty of Technical Sciences, Bitola, (in Macedonian) [7] Mayor of London (2010), Traffic modelling guidelines, TfL Traffic manager and network performance best practices (J. Smith and R. Blewitt Eds.), Transport for London Daniel Pavleski, M.Sc. Head of Unit, Department for Transport, City of Skopje, Skopje (MK) daniel.pavleski@outlook.com Performance measure Link 1 Link 2 Link 3 Link 4 Average max green time utilization ration 0.47 0.40 0.60 0.54 Average arrived vehicles per cycle 28.20 9.79 33.51 16.18 Average served vehicles per cycle 19.25 6.35 29.50 12.35 Average green occupancy ratio 0.91 0.86 1.00 0.95 Average red occupancy ratio 0.80 0.68 0.98 0.84 Average percent of arrived vehicles on green signal 18.57 11.02 22.83 10.59 Average platoon ration 0.81 0.95 0.88 0.85 Table 1: Results from TSCLab International Transportation (71) 1 | 2019 48 SCIENCE & RESEARCH Academics Projects in a nutshell Overview of selected mobility research projects Bringing autonomous driving to life I n a newly founded innovation network, researchers from the Fraunhofer Institute for Industrial Engineering IAO have joined forces with McKinsey & Company and a range of industry project partners to collaborate on the mobility solutions of tomorrow - from conceptual idea to finished prototype. The researchers of the Mobility, Experience and Technology Lab, or MXT for short, look at the various ideas that are emerging, and determine as quickly as possible which technologies, services and business models could catch on and which of them are unlikely to succeed in the marketplace. One solution, for instance, envisages voice-assisted services that draw on artificial intelligence, with car windshields turning into multifunctional displays. At the heart of this initiative to pursue the most promising ideas is Fraunhofer IAO’s Mobility Innovation Lab in Stuttgart. This modern research facility for prototyping and creative workshops already provides an insight into the mobility of tomorrow, featuring, for instance, a converted vehicle that interacts with pedestrians; an electric three-wheel scooter that hints at the future of sustainable inner-city mobility; and a futuristic car cockpit complete with modular dashboard, windows made of switchable glass, reclining seats, foldout tables and a pull-out monitor. One of the key roles of the MXT Lab is to carry out user studies, providing a first indicator of the viability of potential innovation opportunities. In one of the first such studies, the partners investigated whether the time freed up by autonomous driving might be suited to language learning. This information allows the researchers to draw conclusions that can then be fed into the automation of the driving experience in the future and the way that these vehicles are designed. www.iao.fraunhofer.de A converted car interacts with a pedestrian. Photo: Fraunhofer IAO New breakthroughs in research on super-batteries R esearchers at Graz University of Technology (TU Graz) in Austria have discovered a means of suppressing singlet oxygen formation in lithium-oxygen batteries in order to extend their useful lives. Stefan Freunberger of the Institute for Chemistry and Technology of Materials at TU Graz has been working on development of a new generation of batteries with enhanced performance and longer useful lives, and which are also cheaper to produce than current models. He believes that lithium-oxygen batteries have significant potential. In 2017 he uncovered parallels between cell ageing in living organisms and in batteries. In both cases, highly reactive singlet oxygen is responsible for the ageing process. This form of oxygen is produced when lithium-oxygen batteries are charged or discharged. The researcher has now found ways to minimise the negative effects of singlet oxygen, and his findings have been published. In his paper in Nature Communications, Freunberger describes the effect of singlet oxygen on what are called redox mediators, which can be reversibly reduced or oxidised. The work was carried out in collaboration with researchers from South Korea and the USA. Redox mediators play a vital role in the flow of electrons between the exterior circuit and the charge storage material in oxygen batteries, and also have a considerable impact on their performance. The principle behind mediators is borrowed from nature, where they are responsible for a host of different functions in living cells, including transmitting nerve impulses and producing energy. The researchers used density functional theory calculations to demonstrate why certain classes of mediators are more resistant to singlet oxygen than others. They also identified its most likely avenues of attack. These insights are driving forward the development of new, more stable redox mediators. “The more stable the mediators, the more efficient, reversible and long-lasting the batteries become,” Freunberger explaines. Besides deactivating redox mediators, singlet oxygen also triggers parasitic reactions, which compromise battery life and rechargeability. Freunberger identified a suitable quencher that transforms the singlet oxygen produced into harmless triplet oxygen, which occurs in air - an enzyme called superoxide dismutase blocks the formation of singlet oxygen in living cells. www.tugraz.at International Transportation (71) 1 | 2019 49 Academics SCIENCE & RESEARCH Training data for autonomous driving A utonomous cars must perceive their environment true to reality. The corresponding algorithms are trained using a large number of image and video recordings. For the algorithm to recognize single image elements, such as a tree, a pedestrian or a road sign, these are labeled. Labeling is improved and accelerated by understand.ai, a startup established by computer scientist Philip Kessler, who studied at Karlsruhe Institute of Technology (KIT), and his cofounder Marc Mengler. An algorithm learns by examples and the more examples exist, the better it learns. That is why automotive industry needs a large amount of video and image material in machine learning for autonomous driving. So far, objects on the images have been labeled manually by human staff. “Big companies, such as Tesla, employ thousands of workers in Nigeria or India for this purpose. The process is troublesome and time-consuming,” Kessler explains. “We at understand.ai use artificial intelligence to make labeling up to ten times quicker and more precise.” Although image processing is highly automated in large parts, final quality control is made by humans. Combination of technology and human care is particularly important for safety-critical activities, such as autonomous driving,” the founder of understand.ai says. The labelings, also called annotations, in the image and video files have to agree with the real environment with pixel accuracy. The better the quality of the processed image data, the better is the algorithm that uses these data for training. More information: www.kcist.kit.edu | www.understand.ai Using processed images, algorithms learn to recognize the real environment for autonomous driving. Graphics: understand.ai Particulate matter from aircraft engines affects airways R esearchers under the leadership of the University of Bern have investigated the effect of exhaust particles from aircraft turbine engines on human lung cells. The cells reacted most strongly to particles emitted during ground idling. It was also shown that the cytotoxic effect is only to some extent comparable to that of particles from gasoline and diesel engines. According to the World Health Organization (WHO), seven million people worldwide die as a consequence of air pollution every year. For around 20 years, studies have shown that airborne particulate matter negatively affects human health. However, the toxicity of the solid particles from aircraft turbine engines is still widely unresearched. Now a multidisciplinary team, led by lung researcher Marianne Geiser of the Institute of Anatomy at the University of Bern, together with colleagues from Empa Dübendorf and the University of Applied Sciences and Arts Northwestern Switzerland (FHNW), has shown that primary soot particles from kerosene combustion in aircraft turbine engines also cause direct damage to lung cells and can trigger an inflammatory reaction if the solid particles - as simulated in the experiment - are inhaled in the direct vicinity of the engine. The researchers demonstrated for the first time that the damaging effects also depend on the operating conditions of the turbine engine, the composition of the fuel, and the structure of the generated particles. The present study was published in the journal “Nature Communications Biology”. Particles emitted from aircraft turbine engines are generally ultrafine, i.e. smaller than 100 nm. By way of comparison, a human hair has a diameter of about 80,000-nm. When inhaled, these nanoparticles - like those from other combustion sources - efficiently deposit in the airways. In healthy people, the well-developed defense mechanisms in the lungs normally take care of rendering the deposited particles ineffective and removing them from the lungs as quickly as possible. However, if the inhaled particles manage to overcome these defense mechanisms, due to their structure or physico-chemical properties, there is a danger for irreparable damage to the lung tissue. The particles turned out to cause different degrees of damage depending on the turbine thrust level and type of fuel: the highest values were recorded for conventional fuel at ground idling, and for biofuel in climb mode. Bibliographic information: Jonsdottir HR, Delaval M, Leni Z, Keller A, Brem BT, Siegerist F, Schönenberger D, Durdina L, Elser M, Burtscher H, Liati A, Geiser M.: Non-volatile particle emissions from aircraft turbine engines at ground-idle induce oxidative stress in bronchial cells. Nature Communications Biology. 2: 90 (2019), DOI: 10.1038/ s42003-019-0332-7 Close-up of the turbine engine at the testing facility with the aerosol sampling probe in place Photo: University of Bern / SR Technics Switzerland AG International Transportation (71) 1 | 2019 50 SCIENCE & RESEARCH Academics Metallic 3D printing on track for automotive series production E nd of March 2019, the joint project “Industrialization and Digitization of Additive Manufacturing (AM) for Automobile Series Processes - IDAM” held its kickoff meeting in Munich, which was intended to pave the way for Additive Manufacturing to enter automotive series production. Specifically, the project partners - consisting of SMEs, large companies and research institutions - will transfer metallic 3D printing into an industrialized and highly automated series process in the automotive industry for the first time. In this project, twelve project partners are laying an important cornerstone to sustainably strengthen Germany’s technological pioneering role and the country itself as a manufacturing location. By integrating metallic 3D printing into the conventional production lines of the automotive industry, IDAM will enable them to replace cost and time consuming processes, such as the production of molds, and to meet the desire for product customization at no extra cost. Metallic 3D printing is being implemented at two locations: the BMW Group’s Additive Manufacturing Center in Munich and automotive supplier GKN Powder Metallurgy’s factory of in Bonn. There, the IDAM team is qualifying the AM technology for the specific requirements to produce identical parts as well as individual and spare parts on the basis of specific components. The targeted quantities speak for the signal character of the joint project: In the future, it should be possible to produce at least 50,000 components per year in mass production and over 10,000 individual and spare parts - at the highest quality and under extreme cost pressure - with the AM production lines. Individual modules can be adapted to the different production requirements thanks to the modular construction of the line and, if necessary, replaced. http: / / ilt.fraunhofer.de Structural optimized differential housing, jointly developed by GKN Powder Metallurgy and Porsche Engineering. Photo: GKN Powder Metallurgy Picture: Sander Weeteling/ Unsplash Traffic prediction system based on neural networks R esearchers of the Miguel Hernández University (UMH) of Elche have developed artificial intelligence solutions based on deep neural networks to predict traffic conditions using data from fixed sensors (such as loops) and connected vehicles. This new system makes it possible to predict traffic 15 minutes ahead of time. To carry out this study, researchers of the UWICORE laboratory, which belongs to the I3E Centre for Engineering Research of the UMH, have digitised and implemented on the SUMO traffic simulation platform, a real traffic setting corresponding to a 97-kilometre stretch from Spain’s A-7 motorway between the cities of Alicante and Murcia. They have worked with the collaboration of the Levante Traffic Management Centre, which provided data on all their traffic sensors from the chosen stretch over a 12-year period. This stretch has been chosen due to the high influx of traffic (Daily Average Intensity of 100,000 cars on some spots) and due to the high number of traffic sensors on the stretch (99 in total), which make it possible to accurately measure traffic with a frequency of one minute. With a selection of this data, researchers have developed a digital simulation setting which makes it possible to very accurately generate the traffic endured by the A-7 stretch for 10 days. With the digital traffic platform created at the UMH, researchers have developed techniques based on deep neural networks to predict traffic conditions 15 minutes in the future, using data from connected vehicles. Researchers have analysed how the insertion of the connected vehicle affects the accuracy of the traffic intensity, density and speed predictions. Their investigations have allowed them to prove that traffic prediction levels can be improved with data from just 4% of the vehicles, compared to when the prediction is done with data from the traffic sensors that are currently deployed in the relevant A-7 stretch. They also have shown that the merger of the data provided by the current traffic sensors with data from connected vehicles allows for an improvement of traffic prediction accuracy. According to Strategy Analytics data, in 2019, more than half of all manufactured vehicles worldwide will be connected vehicles. With their data, it is possible to learn the state of traffic and even predict it, without having to deploy and maintain traffic sensors as is done today. However, access to this data will have a cost, which means it is important for administrations and managers to know how many pieces of data they need to conduct their functions. The research of the UMH not only offers tools based on artificial intelligence for the characterisation and prediction of traffic conditions, but also make it possible to quantify the data necessary to be able to accurately predict traffic conditions. For example, the percentage of vehicles from which data is needed. www.umh.es International Transportation (71) 1 | 2019 51 Events FORUM European Transport Conference Preview: The 47 th ETC conference will take place from 9 to 11 October 2019 in the Dublin Castle, Dublin (IE) T he Association for European Transport (AET) is the leading European organisation for transport professionals and academics, and is committed to the development of young professionals in the transport industry and in academic fields. AET promotes networking and exchange of ideas, information and opportunities amongst members. It organises Europe’s oldest multi-disciplinary, multi-stream annual transport conference, the European Transport Conference (ETC). In general members are encouraged to play an active role in the Association by joining the Programme Committees which put together the programme for ETC, acting as Session Chairs during the Conference, mentoring young and first-time presenters and joining the panel of experts to peer review a selection of papers presented at the Conference. Members of the Association are currently drawn from more than 35 countries and about 200 Organisation and Individual Members. The full 3-day conference programme covers major themes including: • Autonomous vehicles - looking beyond the technology. What are the implications for accessibility, equity, traffic management, business models? • Climate change • Aviation • Big data - its use in, and implications for, network resilience, control centres, cities, climate change, emissions, mobility as a service, smart cities • System dynamics representation of complex systems in modelling without detailed data Themes covered by the Programme committees can be found in detail at aetransport.org/ html_82. More details about ETC are available online: https: / / aetransport.org Tradition and Progress - Railways in the Digital Age Preview: The 4th International Conference InnoRail Budapest takes place from 12 to 14 November 2019, Budapest (HU) I nnoRail Budapest, a series of professional conferences for the railway industry is organized in Budapest once every second year. The conference was called into being by Hungarian professionals committed to rail transport with the objective of thinking together about the present of rail transport, in order to foster its future development. This tradition-creating event achieved its objective and created a veritable forum for an exchange of views between different professional fields, for the discussion of practical issues and for presenting novel innovative developments, tools and methods. Creation of a safe, modern and integrated railway network that can make rail transport more competitive remains one of the highest priorities of the European Union. Development of railway infrastructure is also progressing in Hungary at a rate not seen for a very long while before. One can witness a veritable generation change in track, train control systems and vehicles alike. As increasing the utilization of railways entails additional tasks for rail professionals throughout Europe, further competitive development efforts are needed in our industry, an industry that is environment friendly, decreases the burden on public roads and offers the backbone of community transport. The professional program of the conference The first day of the conference will feature plenary sessions while the other two days will be organized into parallel section meetings. The topics: • Transport policy directions; strategies, regulatory environment and financial issues in rail infrastructure development • Structure, construction, maintenance and operation of track networks • Intelligent solutions in rail infrastructure • Risks, safety and reliability in the operation of rail infrastructure • Intelligent solutions in passenger services • Environmental awareness in traction: up-to-date energy supply and drive systems Infrastructure and vehicle operators from Hungary and abroad, industrial partners, researchers, designers and theoretical and practical experts are most welcome to the conference. Official conference languages are English, Hungarian and German. More information: http: / / innorail2019.hu International Transportation (71) 1 | 2019 52 FORUM Media Hypermotion 2019: Pioneering Mobility &-Logistics Preview: The Hypermotion takes place from 26 to 28 November 2019, Frankfurt am Main (DE) H ypermotion brings together providers and users who set new standards for tomorrow‘s mobility and logistics. Hypermotion is the first independent platform to show smart, intermodal mobility and logistics solutions. At the exhibition, large corporations, small to medium sized companies and start-ups meet with representatives of science, politics and the associations. In the Hypermotion Lab, young mobility and logistics companies present their business ideas and projects. Start-ups, a new generation of mobility and logistics specialists, demonstrate their conceptual abilities with innovative products, services and apps. enowned conference partners offer inspiration with an exclusive programme for the exchange of knowledge and networking. Focus themes are: mobility, logistics and big data • Smart & digital regions - how can tomorrow’s cities and regions be developed intelligently to meet both global and individual expectations? • Digital & urban logistics - how can a synchronous, customer-oriented value chain be made intermodal and efficient from the first to the last mile? • Data analytics & security - how can integrated mobility and logistics processes be made transparent and secure in the future? In addition Hypermotion participants can look forward to an extensive conference programme on all three days of the event. The Stuva Conference, the international forum for tunnels and infrastructure, will take place parallel to Hypermotion from 26-28 November 2019 in hall 5 at the fair ground of Messe Frankfurt. This year also marks the first time that the German Mobility Congress (DMK) will be taking place as part of Hypermotion. The theme of this congress is ‘Mobility in conurbations - opportunities and challenges’. Other conferences on the programme include ‘EXCHAiNGE’, a prestigious international event focusing on supply chain management, and the ‘Logistics Digital Conference’ (LDC), an event that covers topics such as the future of freight transport. At the ‘smart mobility conference’ (smc), on the other hand, it is sustainable urban mobility concepts and digital networking that are in the spotlight. Detailled information: www.hypermotion-frankfurt.com Photo: Messe Frankfurt / Marc Jacquemin Mega Cities - Mega Challenge Informal Dynamics of Global Change. Insights from Dhaka, Bangladesh and Pearl River Delta, China Frauke Kraas; Kirsten Hackenbroch; Harald Sterly; Jost Heintzenberg; Peter Herrle; Volker Kreibich 2019. 227 pages, 43 figures, 17x24 cm, English ISBN 978-3-443-01103-1, bound EUR 29.90 Megacities are gaining importance as hubs of globalisation processes in a world increasingly dominated by cities. A deeper understanding of the multiple causes and drivers of their development is needed in order to shape the global urban transformation towards sustainability. The interdisciplinary research programme “Megacities - Megachallenge: Informal Dynamics of Global Change” aimed at understanding the connections between informal mega-urban processes and global change by investigating the situation in the two mega-urban areas of the Pearl River Delta/ China and Dhaka/ Bangladesh. The research focused on processes and interactions in four fields with high development dynamics and social relevance: new forms of governance and self-organisation, differentiation of urban economies, dynamics of matter and resource flows, and informal settlement development. International Transportation (71) 1 | 2019 53 International Transportation is a special edition of Internationales Verkehrswesen | vol. 71 Imprint Editorial board Prof. Dr. Kay W. Axhausen Prof. Dr. Hartmut Fricke Prof. Dr. Hans Dietrich Haasis Prof. Dr. Sebastian Kummer Prof. Dr. Barbara Lenz Prof. Knut Ringat Publishing house TRIALOG: PUBLISHERS Verlagsgesellschaft Eberhard Buhl | Christine Ziegler Schliffkopfstr. 22, D-72270 Baiersbronn Phone +49 7449 91386.36 office@trialog.de www.trialog.de Publishing Director Dipl.-Ing. Christine Ziegler VDI Phone +49 7449 91386.43 christine.ziegler@trialog.de Editorial office Managing Editor Phone +49 7449 91386.44 eberhard.buhl@trialog.de iv-redaktion@t-online.de Advertising Phone +49 7449 91386.46 Fax +49 7449 91386.37 anzeigen@trialog.de For advertisement prices, please see price list no. 56 of 01 Jan. 2019 Sales Phone +49 7449 91386.39 Fax +49 7449 91386.37 service@trialog.de Publishing intervals Quarterly, plus International Transportation Terms of subscription Subscriptions run for a minimum of 1 year and may be terminated at the end of any subscription period. 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The publisher accepts no liability for any unsolicited manuscripts. Trialog Publishers Verlagsgesellschaft Baiersbronn-Buhlbach ISSN 0020-9511 IMPRINT | EDITORIAL PANELS Editorial Board Editorial Advisory Board Matthias Krämer Director Mobility and Logistics, Federation of German Industries (Bundesverband der Deutschen Industrie e.V./ BDI), Berlin (DE) Gerd Aberle Dr. rer. pol. Dr. h.c., Emeritus professor of Gießen University, and honorary member of the Editorial Advisory Board (DE) Ben Möbius Dr., Executive Director of the German Federation of Rail Industries (Verband der Bahnindustrie in Deutschland), Berlin (DE) Uwe Clausen Univ.-Prof. Dr.-Ing., Director of the Institute for Transport Logistics at Technical University (TU) Dortmund & Fraunhofer Institute for Material Flow and Logistics (IML), (DE) Florian Eck Dr., Deputy Managing Director of the German Transport Forum (Deutsches Verkehrsforum e.V./ DVF), Berlin (DE) Michael Engel Dr., Managing Director of the German Airline Association (Bundesverband der Deutschen Fluggesellschaften e. V./ BDF), Berlin (DE) Alexander Eisenkopf Prof. Dr. rer. pol., ZEPPELIN Chair of Economic & Transport Policy, Zeppelin University, Friedrichshafen (DE) Tom Reinhold Dr.-Ing., CEO, traffiQ, Frankfurt (DE) Ottmar Gast Dr., Chairman of the Executive Board of Hamburg-Süd KG, Hamburg (DE) Barbara Lenz Prof. Dr., Director of the Institute of Transport Research, German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt e.V./ DLR), Berlin (DE) Knut Ringat Prof., Speaker of the Executive Board of the-Rhine-Main Regional Transport Association (Rhein-Main-Verkehrsverbund GmbH/ RMV), Hofheim am Taunus (DE) Erich Staake Dipl.-Kfm., CEO and President of Duisburger Hafen AG, Duisburg (DE) Wolfgang Stölzle Prof. Dr., Professor of Logistics Management, Research Institute for Logistics Management, University of St. Gallen (CH) Ute Jasper Dr. jur., lawyer, law firm of Heuking Kühn Lüer Wojtek, Düsseldorf (DE) Johannes Max-Theurer Executive Director, Plasser & Theurer, Linz (AT) Matthias von Randow Executive Director of the German Aviation Association (Bundesverband der Deutschen Luftverkehrswirtschaft/ BDL), Berlin (DE) Kay W. Axhausen Prof. Dr.-Ing., Institute for Transport Planning and Systems (IVT), Swiss Federal Institute of Technology (ETH), Zurich (CH) Hartmut Fricke Prof. Dr.-Ing. habil., Chair of Air Transport Technology and Logistics, Technical University (TU) Dresden (DE) Hans-Dietrich Haasis Prof. Dr., Chair of Business Studies and Economics, Maritime Business and Logistics, University of Bremen (DE) Sebastian Kummer Prof. Dr., Head of the Institute for Transport and Logistics Management, Vienna University of Economics and Business (AT) Peer Witten Prof. Dr., Chairman of the Logistics Initiative Hamburg (LHH); Member of the Supervisory Board of Otto Group, Hamburg (DE) Oliver Wolff Executive Director of the Association of German Transport Companies (Verband Deutscher Verkehrsunternehmen/ VDV), Cologne (DE) Oliver Kraft Geschäftsführer, VoestAlpine BWG GmbH, Butzbach (DE) Martin Hauschild Chairman VDI Committee Traffic & Context; Head of Mobility Technologies, BMW Group, Munich (DE) Ralf Nagel CEO of the German Shipowners’ Association (Verband Deutscher Reeder/ VDR), Hamburg (DE) Detlev K. Suchanek Executive Partner, PMC Media House GmbH, Hamburg (DE) Detlef Zukunft Dr., Transport Program Department, German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt e.V./ DLR), Cologne (DE) Jan Ninnemann Prof. Dr., Course head for Logistics Management, Department Maritime & Logistics, HSBA Hamburg School of Business Administration, Hamburg (DE) Sebastian Belz Dipl.-Ing., Secretary General of EPTS Foundation, CEO econex verkehrsconsult, Wuppertal (DE) International Transportation (71) 1 | 2019 54 Dear Readers, this issue of International Transportation shows the wide range of challenges we are currently faced with in the transportation sector. That embraces the evolution of adequate mobility services in the most diverse settings, the strategies and tools to achieve that aim, and both the social and economical costs. Again our authors provide insights into innovative solutions and report on sucessfully realised projects. It must be clear: There is no royal way to meet everybody’s requests. Automatisation, electrification, or mobility services in particular can not solve urban congestion problems or environmental issues. Essential is a spanning strategy so that these technologies can take effect - held not only by entrepreneurial will but also by political fostering. Addressing these issue becomes even more imperative, because increasingly shorter cycles of innovation generate new, often cross-sectoral technologies, and innovative products. That is why we at International Transportation keep it rolling. If you would like to contribute, please contact us. Authors‘ guidelines and a submission form can be found at our website: www.internationaltransportation.com. Or contact us directly by e-mail: editorsdesk@internationaltransportation.com I look forward to hearing from you! Sincerely Eberhard Buhl, Managing Editor 04-07 June 2019 Munich (DE) Air Cargo Europe Exhibition and conference Organization: Messe München GmbH www.aircargoeurope.com 04-07 June 2019 Munich (DE) transport logistic Passion for logistics Organization: Messe München GmbH Contact: +49 89 949-11368, info@transportlogistic.de www.transportlogistic.de 13-14 June 2019 Bratislava (SK) 17 th European Transport Congress (ETC) New Trends in Transport Systems Organization: The European Platform of Transport Sciences - EPTS Foundation Contact: Eva Schmidt, schmidt@epts.eu www.17etc.uniza.sk 19-20 June 2019 Johannesburg (ZA) Africa Rail 2019 22. Exhibition & Conference Organization: Terrapinn Ltd, Bryanston 2021, South Africa Contact: Athena Maharaj, +27 11516-4075, athena.maharaj@terrapinn.com www.terrapinn.com/ exhibition/ africa-rail/ index.stm 11-15 Sept 2019 Frankfurt am Main (DE) New Mobility World Everything on future mobility - at IAA Organization: NMW project office, +49 30 7262 199 71, nmw-expo@evenson.de https: / / newmobility.world/ en/ 12-22 Sept 2019 Frankfurt am Main (DE) 47 th European Transport Conference Annual conference of the Association for European Transport Organization: Association for European Transport (AET), Henley-in-Arden (UK) Program and Informations: https: / / aetransport.org 15-17 Oct 2019 Munich (DE) eMove360° Europe 4 th International Trade Fair for Mobility 4.0: electric - connected - autonomous Organization: MunichExpo Veranstaltungs GmbH Contact: robert.metzger@emove360.com.de http: / / www.emove360.com 12-14 Nov 2019 Budapest (HU) 4 th International Conference InnoRail Budapest Tradition and Progress - Railways in the Digital Age Organization: Innorail Kiadó és Konferencia Kft., Budapest Contact: CongressLine Kft., office@congressline.hu http: / / innorail2019.hu/ de 26-28 Nov 2019 Frankfurt am Main (DE) Hypermotion 2019 Pioneering Mobility & Logistics Additional conferences during Hypermotion - The Stuva Conference: International forum for tunnels and infrastructure - German Mobility Congress (DMK): “Mobility in conurbations - opportunities and challenges” - EXCHAiNGE: Supply chain management - Logistics Digital Conference (LDC): The future of freight transport. - smart mobility conference (smc): sustainable urban mobility - Connected Technologies Conference: intelligent transport systems and services (ITS) Organization: Messe Frankfurt Exhibition GmbH Contact: info@messefrankfurt.com https: / / hypermotion-frankfurt.messefrankfurt.com CALENDAR OF EVENTS 04 June 2019 to 28 November 2019 For information on additional events go to www.international-transportation.com REMARK | EVENTS Meine/ Unsere Daten:  Herr  Frau  Firma/ ... 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