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www.international-transportation.com MOBILITY Acceptance of autonomous shuttle busses in Berlin The Paradigm Shift in Transportation: Progress for Science, but a Revolution for Practice LOGISTICS Intelligent Logistics: Analyzing the Dissemination of AI Applications Collection | March 2026 Volume 77 MOBILITY 11 User Acceptance of Autonomous Shuttle Busses Tests in Berlin’s KISM project First results of test drives, experts and focus groups related to the introduction of level 4 automated shuttle busses as first and last mile service in Berlin, Germany in 2024 Wulf-Holger Arndt, Jakob Busch, Robert Linke-Wittich, Katharina Lange, Christoph Schäper 18 The Paradigm Shift in Transportation: Progress for Science, but a Revolution for Practice. A summary Hermann Knoflacher LOGISTICS 2 Intelligent Logistics: Analyzing the Dissemination of AI Applications Eugen Truschkin, Emin Huseynov, Remmon Sarka COLUMNS 23 Impressum Photo credit: © iStock.com/ ilbusca Photo credit: © iStock.com/ imaginima PAGE 2 PAGE 18 CONTENT Collection 2025 Just read on: Specialist and scientific articles from International Transportation online from the year 2000 onwards in the article overview on the archive page on the web. www.internationalesverkehrswesen.de/ archiv TOPICS, KEYWORDS, AUTHORS ... Current topics, dates and the extensive archive can be found at www.internationales-verkehrswesen.de Internationales Verkehrswesen (77) 1 ǀ 2025 1 MEDIENTIPP Sierk A. Horn Intercultural Leadership Humanistic Perspectives 1. Auflage 2024, 552 Seiten ISBN print 978-3-8252-6186-3 ISBN eBook 978-3-8385-6186-8 DOI 10.36198/ 9783838561868 Ladenpreis print €[D] 44,90 Ladenpreis eBook €[D] 43,99 + offers humanistic perspectives on leadership across cultures + introduces an easy-to-understand way of thinking about cultural diversity + invites self-reflection on communication across cultures For many of us, connecting with people across the world is now easy and commonplace. But coming into contact with different ways of doing things means losing our superpower of giving meaning to what is happening around us, interacting skilfully and building rapport. In the first part of this book, Sierk Horn shows how intercultural interactions set in motion psychological processes. The second part deals with the behavioural determinants of intercultural communication. The third part examines our social environment and how we deal with cultural differences. The book wants to make you curious about intercultural leadership. It invites you to explore humanistic perspectives in everyday communication. A wealth of exercises will accompany you on your learning journey. An extensive range of online resources and learning and teaching materials accompany the textbook. Narr Francke Attempto Verlag GmbH + Co. KG Dischingerweg 5 \ 72070 Tübingen \ Germany \ Tel. +49 (0)7071 97 97 0 \ info@narr.de \ www.narr.de The benefits of AI adoption in logistics are manifold. Applications range from real-time route optimization and predictive demand forecasting to robotic warehouse automation and intelligent customer service systems. These solutions offer measurable improvements in cost efficiency, service reliability, and speed of delivery. The current surge in AI adoption is not only being driven by technological improvement, but also by broader strategic recognition. According to a recent PwC survey, 56% of executives report more efficient use of working time 1. Introduction Artificial intelligence (AI) has emerged as one of the most transformative technologies of the 21st century. Its rapid development has brought AI to the forefront of industrial and service applications. Logistics, as the backbone of global supply chains, is no exception. In times of high volatility on the global markets and increasing demand for efficiency, resilience, and transparency, AI is now considered not an optional add-on, but an essential driver of competitiveness and innovation in logistics. due to generative AI, while 32% have experienced revenue growth and 34% have seen increased profitability (PwC, 2025). This paper aims to provide a structured overview of how AI technologies are being applied across core logistics processes along three established planning horizons: strategic, tactical, and operational. ƒ Strategic planning (years) encompasses long-term supply chain design, network structuring, and investment decisions. ƒ Tactical planning (months) addresses medium-term tasks such as aggregate Intelligent Logistics: Analyzing the Dissemination of AI Applications Artificial Intelligence, Supply Chain Management, Planning Horizons, Predictive Analytics Analyzing the dissemination of AI in logistics, this paper structures applications along strategic, tactical, and operational horizons. It concludes that while AI offers decision support for long-term strategy, its adoption is most mature in operational areas like route optimization and warehousing, delivering immediate efficiency gains. Eugen Truschkin, Emin Huseynov, Remmon Sarka DOI: 10.24053/ IV-2025-0073 International Transportation (77) Collection ǀ 2025 3 demand forecasting, capacity allocation, and inventory management. ƒ Operational planning (days to weeks) focuses on the day-to-day execution of logistics activities, including shift scheduling, vehicle dispatching, and load optimization. Differentiating the use of AI based on these planning horizons enables a clearer understanding of the current adoption patterns. Strategic applications generally offer substantial benefits, but require longer lead times. Operational AI solutions, on the other hand, quickly demonstrate value through immediate process improvements. This paper is based on desk research and the synthesis of evidence from academic research, industry reports, and real-world business cases. It leverages these findings to map out current AI use along the three planning horizons described above, identify where tangible benefits are already being realized, and explore future opportunities. This paper is structured as follows. Section 2 discusses AI applications identified at the strategic logistics level, and Section 3 explores AI applications at the tactical level. In Section 4, we provide an overview of AI applications at the operational logistics level. Section 5 discusses observed trends, and Section 6 concludes with our view on the prospects of AI applications in logistics. 2. AI applications at the strategic planning level Artificial intelligence is evolving into a major instrument supporting strategic decisions in logistics—choices that shape assets, partnerships, and capital over multi-year horizons. At the strategic level, supply chain network design and its three sub-tasks (transport corridor/ route configuration, prioritization of capital allocation, supplier and partner relationship management) are of specific practical interest. Across these topics, AI (which is often embedded in digital twins) does two main things: It puts messy internal and external data on a common footing, and it generates comparable scenarios that make trade-offs visible in terms of services, costs, risks, and emissions. The goal is not to replace engineering and feasibility work, but to identify and narrow down options, improve data quality, streamline routine activities, and generally support human experts in decision-making. Supply chain network design (SCND) and optimization Supply chain network design (SCND) involves establishing the footprint and roles of suppliers, plants, warehouses, and distribution centers to configure material and information flows. Traditionally, this has been treated as an occasional, static exercise. Today’s volatility in geopolitics, trade frictions, and shifting customer expectations, however, are imposing new requirements and demanding a more dynamic approach. AI is shifting SCND from episodic, spreadsheet-driven studies toward a continuous, evidence-based cycle that links strategic targets with day-to-day decisions. This shift can be described as an iterative “align-create-implement-validate” loop (Tengler Consulting, 2025): align business objectives and KPIs; create scenarios with AI optimization; implement short-term structural changes; and validate performance with continuous monitoring. The argument is that volatile demand, supply shocks, and service expectations now require SCND to operate as an ongoing capability rather than a one-time project. At the same time, tasks like capacity allocation through the placement of warehouses, terminals, cross-docks, and even new railway lines and roads still remain long-term strategic decisions. The described design and optimization cycle (“align-create-implement-validate”) can be efficiently executed inside a supply-chain digital twin that mirrors end-toend behavior and supports ongoing scenario testing and updates. In this context, shortterm actions (lane rerouting, inventory re-allocation) coexist with medium-/ longterm structural moves (opening/ closing facilities, dual sourcing), making network design a living, data-driven process rather than a one-time plan and providing a possibility for rapid course corrections as conditions change. In other words, digital twins are virtual replicas that ingest operational and financial data to simulate and optimize decisions from forecasting and sourcing to distribution and returns. Paired with predictive and prescriptive AI, they enable “self-healing” supply chains and various benefits, such as up to a 20% improvement in fulfilling delivery promises, a ~10% reduction in labor costs, and a ~5% rise in revenue in representative use cases (McKinsey, 2024a). In real-life scenarios, there are companies that specialize in network design and optimization and also actively integrate AI elements into their solutions to streamline processes. For instance, River Logic focuses on prescriptive, finance-aware decisioning through its Digital Planning Twin - a constraint-based digital representation of a value chain. At the same time, the platform’s Network Design solution addresses fundamental decisions (e.g. number and location of facilities) and scenario planning with a focus on formulating feasible plans under capacity, contractual, and regulatory constraints (River Logic, 2025). Another real-life example of a supply-chain design platform with a slightly different focus is anyLogistix, which combines analytical network optimization with dynamic simulation to evaluate end-to-end logistics networks, inventory, and production capacity. Its major functions include Greenfield Analysis (to identify possible facility locations based on demand and geography) and Network Optimization. While ALX is not an “AI-first” tool, it integrates machine-learning models so users can directly embed external ML - for example, demand forecasts - in simulations/ digital experiments (anyLogistix, 2025). The sections that follow examine three key SCND sub-tasks in greater detail: transport corridor/ route configuration, prioritization of capital allocation, and supplier and partner relationship management. Transport corridor/ route configuration As a part of broader SCND development, transport corridor/ route configuration and development is a long-term strategic process (from the point of view of an investor, producer or/ and infrastructure owner wishing to establish an intermodal logistics route or corridor, for instance). It often involves large design spaces (e.g. multi-national logistics corridors) and implies complex planning processes. Here, studies once again emphasize how AI-supported digital twins can offer benefits in terms of process streamlining, reduction of complexity, and improvement of efficiency by consolidating demand models and finance logic in order to evaluate long-term options (Wu et al., 2025; Nag et al., 2025). What AI adds at the strategic scale is the improvement of fundamental elements like data inputs (e.g. entity matching, data cleaning) and models (OD-demand and transport mode share forecasting). Guizzardi et al. (2025) show how digital twin frameworks for transport and logistics can combine simulation with machine learning to test corridor policies and facility allocations, as well as how resilience KPIs can be set in advance so that various options can be ranked under disruption scenarios. Recent studies also apply deep reinforcement learning to corridor capacity planning, indicating that AI can search wide policy/ design spaces, formulate phasing plans (with human governance), and convert simulated outcomes into KPIs and metrics for financial and operational decision-making (Farahani et al., 2025). In practice, a digital twin encodes various options for the infrastructure at hand (railway lines, terminal locations) and formulates several corridor development options; AI then tests each plan against numerous what-ifs - demand shifts, envi- LOGISTICS  Intelligent Logistics DOI: 10.24053/ IV-2025-0073 International Transportation (77) Collection ǀ 2025 4 ternative-sourcing analysis (AlMahri et al., 2024). Complementary research focuses on trustworthy link prediction in supplier KGs (e.g. by demonstrating possible company relationships and dependencies) to improve transparency, which is important for governance and escalation decisions in SRM (Kosasih & Brintrup, 2024/ 2025). Together, these techniques turn scattered, incomplete inputs into a continuously updated “network view” that enables selection and risk monitoring. In real life, numerous companies provide supplier-data platforms with AI integration. For instance, TealBook uses AI to unify and enrich fragmented supplier records and refresh profiles across systems. Knowledge-graph approaches are also already deployed at scale: Companies like Interos and Sourcemap use KGs to map millions of companies and complex multi-tier networks. AI’s second contribution is to support the design of the commercial architecture that underpins long-term partnerships: term structures, risk-sharing and price indexation, service targets, penalties/ bonuses, and governance of partnerships across multi-year frameworks. Models trained on contract portfolios and market data help benchmark clauses, simulate total cost under various price adjustments or service terms, and highlight the most important points to negotiate before going to market; the results are aligned with enterprise KPIs and rolled up to financial and service metrics for decision-making (GEP, 2024). CLM vendors such as Icertis and Agiloft now ship generative-AI copilots that specialize in drafting/ amending contract clauses. 3. AI applications at the tactical planning level Artificial intelligence is quietly transforming the tactical pillar of logistics, altering how service providers adopt a mid-term strategy and make choices. AI is also increasingly gaining traction in the critical middle ground, guiding demand forecasting, inventory management, supplier evaluation, workforce and shift planning, and asset management. A more detailed overview of these AI-driven tasks is provided below. Demand forecasting and capacity planning Demand forecasting is a staple of logistics, but with the emergence of artificial intelligence, it is changing its roots. No longer dependent on stagnant historical data and seasonality, forecasting is now informed by sophisticated algorithms, live analytics, and AI-based services in order to create much more precise and effective forecasts. Using AI to monitor customer trends, price movement, delivery schedules, and market ronmental instabilities, new policies, and other external factors. It then highlights the options that still perform well under those shocks. This approach is also relevant for separate logistics chain/ corridor elements like transport infrastructure and logistics facilities in general. For instance, a real-life business case in the Port of Corpus Christi (TX, USA) followed a similar logic, using a digital twin of the port to test various development options before actually committing to capex (Business Insider, 2025). Prioritization of capital allocation The same engine that steers daily flows and short-term decisions can also rank long-term investments and plan the timing of their rollout, strengthening the link between short-term performance and strategic capital allocation. In effect, AI-enabled digital twins can serve as a central decision layer for ranking and phasing logistics-infrastructure investments. A McKinsey study of public mega-projects shows that twins that bring together engineering, demand, and cost data can boost capitaland operating efficiency by 20-30% (McKinsey, 2025). A recent review of digital twin technology for infrastructure finds that “digital twin plus AI” toolchains cut facility-allocation modelling time by ~40% and raise net-present value through rapid scenario iteration (Moshood et al., 2024). Supplier and partner relationship management (SRM) SRM addresses how firms discover, evaluate, contract with, and develop suppliers to meet cost, quality, service, resilience, and sustainability objectives. Historically, SRM has been less structured and more fragmented; recent literature positions it as an ongoing capability that combines internal master data with external signals (news, disclosures, operational events). AI supports the SRM process by turning unstructured or inconsistent inputs into analyzable supplier profiles and aligning selection and development with enterprise KPIs (Mohammed, 2023). At a strategic scale, the first contribution of AI is to industrialize SRM data: entity matching across systems, automated entity classification (e.g. category, region, risk flags), and extraction of supplier facts from text. Recent work proposes knowledge-graph (KG) pipelines (a map of companies and how they are linked) that use large language models (LLMs) to research public sources, fill in the KG, and infer relationships beyond tier-1/ -2 suppliers (showing the supplier and the supplier’s supplier), thereby expanding visibility. This KG+LLM approach supports discovery, mapping of critical dependencies, and alindicators in real-time is helping logistics companies predict demand more accurately. This change involves much more than enhanced precision; more intelligent predictions result in more intelligent inventory control, more efficient transportation scheduling, and improved warehouse control. The eventual outcomes include reduced costs, less waste, and supply chains that keep up with (and even anticipate) the latest developments. AI-driven demand forecasting is now a strategic requirement in this new landscape. Organizations such as Blue Yonder are applying machine learning to forecast demand and optimize entire supply chains. Relied upon by the largest companies in the world (like Walgreens and Bayer), their planning solutions use real-time data to predict demand fluctuations, reduce waste levels, and drive increased turnover. They enable organizations to remain agile, efficient, and responsive to customer demands through their sustained capability to react to variability in the market (Blue Yonder, 2025). In addition, Amazon has recently introduced an AI-powered demand forecasting model to support the customer experience. This new foundational model is designed to predict what customers will want, as well as where and when, for hundreds of millions of products per day (Amazon, 2025). While the previous systems Amazon used relied on sales histories to guide inventory planning decisions, its AI-powered forecasting model adds time-bound data like weather patterns and holiday schedules to place the right products in the right locations more accurately (Amazon, 2025). Amazon has reported that these forecasts have contributed to a 10% improvement in long-term national forecasts for deal events and a 20% improvement in the regional forecasts for millions of popular items (Amazon, 2025). The forecasting method has already been implemented in the U.S., Canada, Mexico, and Brazil, and the benefits can already be seen. Packages arrive faster than they previously did and delivery partners travel fewer miles, which in turn reduces traffic and carbon emissions (Amazon, 2025). AI-powered data forecasting tools used by Amazon and Blue Yonder are among the many solutions that are currently reshaping the logistics landscape. They help companies become more efficient, reduce waste, and improve their adaptability. Inventory management and stocking policies As AI handles more and more aspects of demand forecasting, the advantages automatically carry over to inventory management and stocking policy. By analyzing large DOI: 10.24053/ IV-2025-0073 International Transportation (77) Collection ǀ 2025 5 Intelligent Logistics  LOGISTICS amounts of data in real time, AI applications calculate the most effective reorder points and quantities in order to replenish inventory in the most efficient manner, thereby minimizing carrying costs and releasing capital that is held up in excess stock. This degree of precision allows companies to maintain their service levels as they minimize waste and make their supply chains more agile. The adoption of AI-based inventory optimization by small and medium-sized businesses across different markets is demonstrated by platforms like Netstock. These systems use smart product classification to rank products based on sales velocity and performance and customize replenishment cycles, fill rates, and safety stock levels. They also help in identifying obsolete or stock-out items, enabling companies to adopt a leaner stocking policy that will respond directly to demand signals. Inventory policies can be fixed on a national level or can vary by product, supplier, or location, with manual overrides that facilitate faster decision-making under turbulent conditions (Netstock, 2025). Similarly, ToolsGroup uses AI and probabilistic forecasting to control inventory with complex, multi-level supply chains. Its methodology combines sets of demand scenarios and probabilities to help deal with uncertainty and volatility, which traditional techniques fail to handle. Stock levels can be aligned with real demands by using multi-echelon optimization and service-oriented planning capabilities. This adaptability is taken to an even greater extent with machine learning, which uses internal and external signals to enhance predictions and inventory targets in a continuous process (ToolsGroup, 2025). Collectively, these technologies represent a move toward more adaptive, data-driven inventory management that balances financial, operational, and customer service goals with constant, smart adaptation. Procurement and supplier evaluation Artificial intelligence is also transforming the procurement process in logistics by facilitating smarter sourcing decisions, the ongoing evaluation of suppliers, and more responsive contract management. Current AI technology makes it possible to keep supplier performance on track in real time by converting performance factors such as delivery reliability, quality, and contract compliance into numerical scores. Machine learning can be used to detect risks in a more efficient way by indicating potential warning signs such as financial strain, employee conflicts, or ESG infringement based on sources in news, social media, and financial platforms. Sustainability is also considered, with AI assessing suppliers on the basis of emissions, ethical sourcing, and rule adherence. In contract management, AI can scan large sets of contract portfolios to find pricing anomalies, opportunities to exploit contract loopholes, and vulnerabilities to legal issues. Automated negotiation systems are also able to process routine negotiations, simplifying the procedures involved and freeing up human resources for more strategic activities. The use of artificial intelligence in end-to-end procurement and supplier management is evident on AI-based platforms like JAGGAER and Scoutbee. JAGGAER, which is used at companies such as Lenovo and Rolls-Royce, uses machine learning for autonomous sourcing, supplier risk and performance analysis, and predictive demand and spend forecasting (JAG- GAER, 2025). In a similar fashion, Scoutbee uses AI to improve supplier discovery and analytics on a platform that includes capabilities, ESG compliance, intelligent supplier scouting, automated supplier matching, and data enrichment. Siemens, Unilever, and Audi are some of the corporations that utilize these tools to digitalize procurement and get a better view of suppliers in the international market (Scoutbee, 2025). Asset management Asset management can be seen as one of the AI applications that has been adopted more broadly in logistics. One of the fields in this domain that can be associated with the tactical level is predictive maintenance. AI-driven predictive maintenance platforms analyze real-time sensor data such as engine temperature, vibration, and pressure to anticipate vehicle failures and schedule maintenance proactively. This reduces unplanned downtime, lowers maintenance costs, and extends asset lifespans. Here, the case of Hitachi can be cited as an example of AI-driven fleet management solutions that enable fleet operators to streamline their operations, maintenance, and repair processes, leading to reduced vehicle downtime and better management of both fleets and risks (Hitachi, 2025). 4. AI applications at the operational planning level Nowadays, established supply chain structures compete in a highly volatile environment with increased customer expectations with regard to service level. AI tools support companies in coping with this increased complexity by optimizing ongoing operations, saving costs, improving their service level, and contributing to their sustainability agenda. Our research reveals a wide dissemination of AI applications, specifically at the operational level of logistics (fulfillment). As an example, respondents in a recent survey of 130 companies (mostly from Germany) consider optimized routing, improved resource utilization, reduction of transport costs, and an improved CO2 footprint among the major gains afforded by AI (Inform, 2024). The following section provides an overview of AI applications at the operational logistics level. Warehousing (picking and sorting) AI-powered tools can unlock 7-15% more capacity in warehouse networks by identifying daily spare capacity, understanding variability in resource availability, and evaluating opportunities to improve efficiency (McKinsey, 2024). The examples below demonstrate how current AI applications are used in warehouse operations. Amazon has invested in new robotic inventory management systems like Sequoia. Sequoia makes it possible to identify and store inventory at Amazon fulfillment centers up to 75% faster, which results in items being listed for sale on Amazon.com more quickly - a benefit for both sellers and customers. It also reduces the time it takes to process an order through a fulfillment center by up to 25%, which improves shipping predictability and increases the number of goods Amazon can offer for same-day or next-day shipping (Amazon, 2023). Digit (see Figure 1), a mobile robot from Agility Robotics, is being tested by Amazon to help employees with tote recycling - a highly repetitive process of picking up and moving empty totes once inventory has been completely picked out of them (Amazon, 2023). Meanwhile, a new agentic AI team within Amazon Robotics is working on the next step in the robotics evolution, adding the ability for robots to hear and understand natural language, process it in a rational manner, and act autonomously (Amazon, 2025). At DHL, AI-powered sorting robots dubbed DHLBots are increasing sorting capacity by 40% or more. These robots are capable of sorting over 1,000 small parcels per hour with 99% accuracy (DHL, 2025a). DHL expects the industry to make use of warehouse management assistants in optimizing inventory placement, automating picking and packing processes, monitoring equipment maintenance schedules, and enhancing overall operational efficiency within warehouses and distribution centers (Dohrmann et al., 2024). Vision picking can be considered one of the further applications of AI use cases in warehousing. With TeamViewer Frontline Pick technology, DHL warehouse employees can utilize smart glasses equipped with vision picking software that provides visual LOGISTICS  Intelligent Logistics DOI: 10.24053/ IV-2025-0073 International Transportation (77) Collection ǀ 2025 6 pacity and improve its network capacity management, resulting in a 7-9% total cost reduction (Transmetrics, 2022). ƒ Vehicle inspection and condition monitoring: Focalx uses computer vision AI to automate vehicle inspections and damage detection. Its platform reduces manual inspection time by up to 80% while minimizing disputes and accelerating fleet readiness and turnover (Focalx, 2025). Customs clearance Customs clearance is often cited as a slow, complicated, bureaucratic affair due to complex regulations and the volume of data to be processed. AI-powered customs clearance solutions support customers in streamlining the process, resulting in time savings, increased accuracy, and less human error. KlearNow and eClear are examples of digital AI-powered customs brokers that offer customers the benefits of process automation. KlearNow, a U.S.-based company founded in 2018, and its KlearCustoms platform automatically converts and organizes all customs documents from customers’ email chains, ensuring the accuracy of customs declarations along with their compliance with the local regulations. In addition, real-time information on shipments is provided. An integrated ROI calculator for importers reports cost savings of 28% when using the default settings of KlearNow.ai (Klear- Now.ai, 2025). eClear, a Germany-based company founded in 2016, offers CustomsAI, which focuses on customs clearance in the EU market. It automates tasks like document classification, tariff code determination, and risk assessment (where it identifies shipments that may warrant closer inspection). Through AI-assisted analysis of customers’ product information (e.g. EAN, GTIN, ASIN, descripareas such as warehouses, delivery centers, and transportation terminals. To estimate the required number of workers, AI analyses previous workload information, seasonal patterns, real-time order volumes, and forecast models. This helps reduce costs, as it can identify where to anticipate overtime and offer alternatives, such as changing schedules to available personnel. AI also increases employee satisfaction by taking into account each individual’s preferences and free time, which enhances work-life balance while reducing absenteeism and churn. In addition, AI makes sure that labor laws and company regulations are followed, thereby ensuring high productivity and compliance (ROCKCREST, 2024). AI-based tools such as UKG Pro Workforce Management streamline scheduling, attendance tracking, and compliance monitoring. Its machine learning component forecasts the number of workers required and creates the most optimal schedules based on previous performance and emerging trends (UKG, 2021a). This reduces overstaffing and overtime costs and aligns staffing with current requirements. A built-in rules engine monitors break time, overtime restrictions, and local labor laws, alerting managers of potential infractions and enforcing compliance with rules (UKG, 2021b). Logistics staffing decisions can be improved even further with the rise of AI and corresponding tools like this (UKG, 2021b). Asset management The following application fields can be identified in the asset management domain at the operational level: ƒ Truck utilization and capacity assessment: AI improves smart truck utilization by improving cargo loading efficiency and reducing underutilized capacity. Using Transmetrics’ AI, DPD was able to obtain better data about its loading cacues throughout the picking process. For real-time data access, the Frontline solution integrates seamlessly with DHL’s warehouse management system, establishing a process that is fully connected end-to-end. This has resulted in a 15% productivity increase, a reduction of error rates to 0.1%, and time savings of 50-70% during onboarding (TeamViewer, 2025). Overall, AI adoption in postal and parcel systems is increasing significantly, particularly in connection with sorting, routing, and customer operations (Dobrodolac et al., 2025). Some of the application cases for AI-powered routing are demonstrated below. Route optimization Route optimization systems driven by AI assist businesses in removing congestion and bottlenecks by optimizing transportation routes, schedules, traffic flows, and capacity distribution while reducing travel time according to real-time demand fluctuations. This entails traffic flow forecasting, route adaptation for weather-related delays, lastmile delivery utilizing geospatial intelligence, and multi-stop optimization to identify the optimal delivery sequence based on vehicle capacities, travel distances, and time windows. Consequently, these systems enhance delivery speed, optimize fuel usage, and lower harmful emissions. Route optimization can be identified as one of the most widely applied AI use cases in logistics. Various companies actively involved in the organization of transport benefit from AI in transportation routing, where one can differentiate between inhouse solutions (e.g. Wellspring from Amazon, ORION from UPS) and vendor solutions (e.g. DispatchTrack or Wise Systems, the latter of which is in use at DHL). The quantifiable benefits of AI-driven route optimization are significant. The introduction of ORION resulted in $320 million in savings and reduced UPS’s fuel consumption by 10 million gallons (RoboticsTomorrow, 2025). In 2024, Maersk achieved a reduction in fuel consumption of up to 15% while also reducing shipping times for critical routes by approximately 20% (Codex, 2025). This was made possible by Maersk’s AI-driven predictive analytics solution, which considers weather patterns, ocean currents, and other influencing factors such as port congestion to determine the optimal route for each of its shipments (Reddy, 2023; Raj, 2024). Workforce and shift planning Artificial intelligence is becoming crucial in assisting logistics firms with planning shifts and staffing. It aligns employees with evolving demands, particularly in high-traffic Figure 1: Digit robot in its testing phase at Amazon (source: Amazon, 2023) Intelligent Logistics  LOGISTICS DOI: 10.24053/ IV-2025-0073 International Transportation (77) Collection ǀ 2025 7 Figure 2: myDHLi - an AI-powered virtual assistant from DHL Global Forwarding (DHL, 2025b) human-steered pipeline with the following logic: objective setting and constraints (human), large-scale scenario generation and financial/ service/ resilience scoring (AI), decision governance (human), and the incorporation of post-implementation learning back into the digital twin (human + AI). This allows for long-term logistics chain commitments while making the respective network adaptable in the short and medium term. The processes of demand forecasting, inventory management, supplier evaluation, and asset management are overseen at the tactical level and are being redefined by artificial intelligence at the medium-term logistics planning level. AI provides support in making more strategic procurement decisions, optimizing inventory policy, forecasting demand, and continuously evaluating suppliers and contracts with real-time analytics and predictive modelling. All these choices feed into each other, making the corresponding system anticipatory rather than reactive. These insights result in logistical functions that are lean, adaptive, and strategically aligned with the long-term vision of the organization. As these moving pieces become tighter and more synchronized at the tactical level, it reduces waste while streamlining costs and responsiveness. AI therefore enhances the tactical level by establishing an essential connection between strategic intent and operational implementation. The operational logistics level is characterized by a wide dissemination of AI-driven applications, with use cases in warehousing (picking and sorting), route optimization, asset management (truck load optimization, 5. Discussion Based on desk research, this paper attempts to reveal the current dissemination level of AI applications in logistics. Figure 3 summarizes the outcomes of our study, presenting the respective AI applications in association with both the logistics planning levels and various elements of the supply chain (procurement, manufacturing, and distribution). The creation of physical infrastructure (e.g. terminals, cross-docks) and the design of complex multi-national logistics routes/ corridors remain strategic, longterm, largely irreversible investments that must be evaluated via feasibility studies and multi-criteria analysis. The related literature underscores that facility allocation choices are costly to reverse and span long time horizons (Snyder et al., 2005), which is why they are considered at the strategic level even as AI improves the surrounding analytics. Despite the increasing maturity of AI tools and AI integration into existing solutions for strategy-level tasks in general, the current consensus is that today’s systems still mainly offer decision support and cannot serve as fully autonomous greenfield designers. AI already excels at components of the entire pipeline (demand forecasting, risk analysis, routing, financial optimization inside digital twins), but fully autonomous, end-to-end steering of all the steps within high-complexity tasks (from greenfield site screening to feasibility analysis, engineering design, financing, and staged rollout) without active human governance is not yet standard practice. One pragmatic short-term target involves an AI-assisted, tion texts, or brand names), CustomsAI instantly identifies the matching TARIC code (the integrated tariff code of the European Union). Existing customs classifications are also checked and corrected if necessary, and product-specific VAT rates are assigned automatically (eClear, 2025). Customer service AI-powered chatbots are used in the transport and logistics industry to better understand customer intent, answer queries in an efficient manner, and provide shipping updates. While there has still not been a broad dissemination of a standalone chatbot product in customer service among the industry giants, some examples from the world’s transport and logistics leaders are presented below. MyDHLi (see Figure 2), an AI-powered virtual assistant used by DHL Global Forwarding, was launched in 2020. Using this 24/ 7 chatbot, customers receive real-time access to quotes, transport modes, carbon emissions, and other key shipment data. The tool is accessible from any device and allows customers to analyze their logistics activities using a range of filters and customizable views. A set of further features, like daily proactive updates listing key milestones on demand, is also available (DHL, 2025b). Meanwhile, enterprises seem to favor vendor partnerships and tend to publicize outcomes as vendor case studies rather than in-house deployments. As an example, Live- Person technology and LivePerson Conversational Cloud was used to develop and integrate a chatbot into GLS Slovenia’s webpage, SMS channel, and Viber and WhatsApp messaging platforms (2Mobile, 2025). LOGISTICS  Intelligent Logistics DOI: 10.24053/ IV-2025-0073 International Transportation (77) Collection ǀ 2025 8 codex.team/ blog/ ai-and-supply-chain-optimization-leading-to-a-25-reduction-in-delivery-times D.E.M.M.C.S, 2025: Risk Monitoring in AI-Enabled Supply Chains: Why Human Oversight Remains Critical. https: / / www.demmcs.com/ archives/ risk-monitoring in ai enable d supplychain s -whyhu man-oversight-remains-critical DHL, 2025a: AI in logistics: lending a helping … arm. https: / / www.dhl.com/ global-en/ delivered/ innovation/ ai-in-logistics.html DHL, 2025b: A New Generative AI Assistant to Boost Your Logistics Efficiency. https: / / www.dhl.com/ usen/ home/ global-forwarding/ latest-news-and-webinars/ mydhli-generative-ai-logistics.html Dohrmann, K. et al., 2024: Logistics Trend Radar 7.0. DHL Group. https: / / www.dhl.com/ de-en/ home/ innovation-in-logistics/ logistics-trend-radar.html Dobrodolac, M. et al., 2025: Exploring the Potential Applications of Artificial Intelligence in Parcel. Management Science Advances. Volume 2, Issue 1 (2025) 107-11. eClear, 2025: CustomsAI. https: / / eclear.com/ product/ customsai/ Farahani et al., 2025: Capacity planning in logistics corridors: Deep reinforcement learning for the dynamic stochastic temporal bin packing problem. https: / / www.sciencedirect.com/ science/ article/ pii/ S1366554524003338 Focalx, 2025: Fleet Inspections: Monitor your fleet condition with AI. https: / / focalx.ai/ fleet-management/ GEP, 2024: How AI-Powered Supplier Relationship Management Can Unlock the Hidden Value of Procurement. https: / / www.gep.com/ blog/ strategy/ ai-powered-supplier-relationship-management-for-procurement Guizzardi et al., 2025: Requirements Analysis for a Digital Twin to Increase the Resilience of Multimodal Corridors: A Case Study in the Twente Region. https: / / link.springer.com/ chapter/ 10.1007/ 978-3-031-94931-9_18 Hitachi, 2025: Fleet management is easy with Hitachi. https: / / social-innovation.hitachi/ en-us/ thinkahead/ transportation/ fleet-management/ Inform, 2024: Trendreport (Generative) KI in der Logistik - Hype oder Gamechanger? Accessed 25.06.2025. Available at: https: / / www.inform-software.com/ de/ landingpages/ trendreport-generative-ki-in-der-logistik#Get-results different logistics and supply chain operations. Incomplete, biased, or quickly evolving data can lead AI models to a conclusion that seems correct on the surface but is, in fact, flawed. In a field as complicated and expensive as this, such mistakes can rapidly turn into a financial loss, a tarnished reputation, or a regulatory risk (D.E.M.M.C.S, 2025). Ultimately, the way forward should be a balanced combination of innovation and responsibility. AI should become a tool that not only drives efficiency and adaptivity in logistics, but also serves as a trusted foundation for sustainable and resilient logistics processes. ▪ SOURCES AlMahri et al., 2024: Enhancing Supply Chain Visibility with Knowledge Graphs and Large Language Models. https: / / arxiv.org/ abs/ 2408.07705 Amazon, 2025: Amazon announces 3 AI-powered innovations to get packages to customers faster. https: / / www.aboutamazon.com/ news/ operations/ amazon-ai-innovations-delivery-forecasting-robotics Amazon, 2023: Amazon announces 2 new ways it’s using robots to assist employees and deliver for customers. https: / / www.aboutamazon.com/ news/ operations/ amazon-introduces-new-robotics-solutions anyLogistix, 2025: Supply Chain Network Optimization. https: / / www.anylogistix.com/ features/ supply-chain-network-optimization/ Blue Yonder, 2025: AI & Machine learning. https: / / blueyonder.com/ why-blue-yonder/ ai-and-machine-learning#trust Business Insider, 2025 : A massive seaport in Texas is using an AI-powered digital replica to track ships and prepare for emergencies. https: / / www. businessinsider.com/ corpus-christi-port-ai-shiptracking-emergency-training-2025-5 Codex, 2025: AI and Supply Chain Optimization Leading to a 25% Reduction in Delivery Times. https: / / vehicle inspection), workforce planning, customs clearance, and customer service. Overall, it was identified that both vendor-owned solutions and in-house AI deployments are present among the companies. While comparing the dissemination of AI at all the planning levels in logistics, operation-level use cases in particular (optimized routing, improved resource utilization) appear to play a “mainstream” role in the current logistics AI landscape. These in turn are driven by logistics giants like Amazon, DHL, or Maersk, which see optimizing the productivity of their existing logistics infrastructure as a target function. Along with cost reductions, a significant improvement in service level is being achieved here, as the examples covered demonstrate. 6. Conclusion AI is becoming a structural driver that is transforming the way supply chains are designed, run, and implemented at all levels. At the strategic level, it increases the adaptability of network design, investment prioritization, and the management of supplier relationships, which results in more shock-resilient supply chains. At the tactical level, it transforms demand forecasting, inventory management, procurement, and asset management into anticipatory, data-driven functions. At the operational level, AI has already started becoming the norm, with measurable efficiency improvements in warehousing, routing, workforce planning, and customer engagement. Combined, the above-mentioned developments indicate that AI in logistics is no longer about isolated applications, but connected ecosystems that bridge the gap between long-term vision and daily execution. The purpose of the technology is still predominantly decision support (with human governance still necessary), but the trend is toward more autonomous and self-optimizing supply chains. The main challenge facing logistics companies is not whether to implement AI, but how to integrate it in a manner that strikes a balance between efficiency with resilience and innovation with responsibility. The application of AI in logistics offers many benefits, and yet the implementation process should be approached carefully because it also presents some challenges. The two main concerns are data security and privacy, and the expenses involved in installing and maintaining the required infrastructure. To ensure the safety of sensitive customer data, companies need to invest in the secure storage of such information and comply with the regulations concerning data maintenance (the ILS Company, 2025). Moreover, human supervision is very much needed when applying AI in Operational planning level Strategic planning level Tactical planning level Distribution Procurement Manufacturing Supply Chain Network Design and Optimization Demand forecasting and capacity planning Customer Service Supplier Evaluation Workforce & shift planning Warehouse picking & sorting Route optimization Predictive maintenance Customs clearance Customs clearance Inventory Management Truck utilization & capacity assessment Vehicle inspection and condition monitoring Figure 3: Overview of AI applications at the different logistics planning levels (authors’ own research) Intelligent Logistics  LOGISTICS DOI: 10.24053/ IV-2025-0073 International Transportation (77) Collection ǀ 2025 9 UKG, 2021a: Flexible scheduling solutions to support your people and their unique life-work experiences. https: / / www.ukg.com/ sites/ default/ files/ legacy/ kronos/ resources/ pdf/ en/ UKG -Dimensions-Scheduling-Profile.pdf UKG, 2021b: How technology helps restaurants keep up with evolving labor laws and regulations. https: / / www.ukg.com/ sites/ default/ files/ legacy/ kronos/ resources/ pdf/ en/ rt0256-USv3-restaurant-compliance-ebook.pdf Wu et al., 2025: Digital Twin Technology in Transportation Infrastructure: A Comprehensive Survey of Current Applications, Challenges, and Future Directions. https: / / www.mdpi.com/ 2076- 3417/ 15/ 4/ 1911 2Mobile, 2025: GLS Slovenia - Improving Customer Service. https: / / www.2mobile.si/ en-gb/ case-studies#MGLS Cover image: © iStock.com/ imaginima Raj, Anita, 2024: AI in Transportation and Logistics: Steering Towards Smarter Operations. https: / / throughput.world/ blog/ ai-in-transportation-and-logistics/ Reddy, Rahul Kumar, 2023: Case Studies Of How AI Is Being Used To Improve Sustainable Supply Chains. https: / / ruralhandmade.com/ blog/ case-studiesof-how-ai-is-being-used-to-improve-sustainable River Logic, 2025: Network Design Optimization for Supply Chain. https: / / riverlogic.com/ solutions/ network-design-optimization-for-supply-chain/ River Logic RoboticsTomorrow, 2025: AI Route Optimization Saves Money, Cuts Fuel Consumption and Provides Faster Delivery Times. https: / / www.roboticstomorrow.com/ stor y/ 2025/ 01/ ai-route optimi z ati o n s ave s m o n eycu t s f u e l co n s um p tion-and-provides-faster-delivery-times/ 23800/ ROCKCREST, 2024: The role of AI and automation in workforce management for smarter scheduling. https: / / www.rockcrest.com/ post/ the-role-of-aiand-automation-in-workforce-management-forsmarter-scheduling Scoutbee, 2025: Scoutbee. https: / / www.scoutbee.com/ Snyder et al., 2005: Facility Location under Uncertainty: A Review (long-lived, hard-to-reverse strategic decisions). https: / / coral.ise.lehigh.edu/ larry/ files/ pubs/ stochloc.pdf TeamViewer, 2025: DHL supply chain uses Frontline Pick to help increase productivity and reduce error rates. TeamViewer Customer Success Story: DHL Tengler Consulting, 2025: From one-time planning to continuous optimization: The AI revolution in supply chain network design. https: / / tenglerconsulting.com/ en/ ai-driven-supply-chain-networkdesign/ The ILS Company, 2025: Unlocking the Potential of AI in Logistics: Benefits, Challenges & Best Practices. https: / / www.ilscompany.com/ ai-in-logistics/ ToolsGroup, 2025: Responsive Inventory Management and Rebalancing. https: / / www.toolsgroup.com/ solutions/ rebalance-ai/ Transmetrics, 2022: Improving Logistics Network Capacity Management with AI & Demand Forecasting. https: / / www.transmetrics.ai/ blog/ logistics-network-capacity-management/ Jaggaer, 2025: Complexity simplified. https: / / www. jaggaer.com/ complexity-simplified KlearNow.ai, 2025: Importer ROI calculator. https: / / www.klearnow.ai/ roi-calculator-usa/ Kosasih, E. and Brintrup, A., 2024/ 2025: Towards knowledge graph reasoning for supply chain risk management using graph neural networks. h t t p s : / / i d e a s . r e p e c . o r g / a / t a f / t p r s x x / v 6 2 y - 2024i15p5596-5612.html McKinsey, 2025: Digital twins: Boosting ROI of government infrastructure investments. https: / / www.mckinsey.com/ industries/ public-sector/ our-insights/ digital-twins-boosting-roi-of-government-infrastructure-investments McKinsey, 2024a: Digital twins: The key to unlocking end-to-end supply chain growth. https: / / www. mckinsey.com/ capabilities/ quantumblack/ our-insights/ digital-twins-the-key-to-unlocking-end-toend-supply-chain-growth McKinsey, 2024b: Harnessing the power of AI in distribution operations. https: / / www.mckinsey.com/ industries/ industrials-and-electronics/ our-insights/ distribution-blog/ harnessing-the-power-of-ai-in-distribution-operations Moshood et al., 2024: Infrastructure digital twin technology: A new paradigm for future construction industry. https: / / www.sciencedirect.com/ science/ article/ pii/ S0160791X24000678 Mohammed, I. A., 2023: Artificial Intelligence in Supplier Selection and Performance Monitoring: A Framework for Supply Chain Managers. https: / / kuey.net/ index.php/ kuey/ article/ view/ 8650/ 6502 Nag et al., 2025: Exploring digital twins for transport planning: a review. https: / / etrr.springeropen.com/ articles/ 10.1186/ s12544-025-00713-0 Netstock, 2025: Netstock Announces the Release of its AI-Powered Predictive Planning Suite. https: / / www.netstock.com/ blog/ netstock-announcesthe-release-of-its-ai-powered-predictive-planning-suite/ PwC, 2025: PwC’s 28th CEO Survey. Accessed 21.08.2025. Available at: https: / / www.pwc.nl/ nl/ actueel-publicaties/ assets/ pdfs/ 28th-ceo-survey.pdf Eugen Truschkin, Dr., Director Rail and Intermodal Logistics Consulting (I.TAC 211), DB Engineering & Consulting GmbH (Deutsche Bahn AG) Emin Huseynov, Expert Rail and Intermodal Logistics Consulting, DB Engineering & Consulting GmbH (Deutsche Bahn AG) Remmon Sarka, Logistics Consultant, Rail and Intermodal Logistics Consulting, DB Engineering & Consulting GmbH (Deutsche Bahn AG) LOGISTICS  Intelligent Logistics DOI: 10.24053/ IV-2025-0073 International Transportation (77) Collection ǀ 2025 10 User Acceptance of Autonomous Shuttle Busses Tests in Berlin’s KISM project First results of test drives, experts and focus groups related to the introduction of level 4 automated shuttle busses as first and last mile service in Berlin, Germany in 2024 automated driving, autonomous public transport, acceptance The KIS’M project (https: / / testfeldstadtverkehrberlin.de/ en/ avf/ kism) tested in 2024 driverless Shuttle Busses in Berlin on the UTR Test Area at the former Airport (TXL). A key research aim is to assess post-test technology acceptance, which is crucial for successful implementation. The aim of the study was to record and descriptively analyse the distribution of acceptance, using the UTAUT2 model as a basis. Acceptance was operationalised by surveying usage intentions (cf. Venkatesh et al., 2021). In addition, relevant latent attitudes in connection with mobility and user characteristics were analysed, which can be qualitatively substantiated. This makes it possible to identify potential opportunities and barriers to the acceptance of this technology. Another focus was on analysing user characteristics, including socio-demographic characteristics as well as mobility-related and other personal characteristics. On the one hand the test was an opportunity to verify the questionnaire, for a subsequent representative city-wide household study in Berlin, which was conducted later in 2025. On the other hand, the user experience results could be evaluated within this small sample, and the user characteristics could be described. This makes it possible to produce hypotheses about user groups in the representative study. The initial results indicate a high level of acceptance of autonomous shuttle buses as an additional public transportation option for firstand last-mile services. However, despite the positive shuttle evaluation after the first experience, the intention to use the service regularly is limited, with only about one-third of participants reporting plans for frequent use. Wulf-Holger Arndt, Jakob Busch, Robert Linke-Wittich, Katharina Lange, Christoph Schäper DOI: 10.24053/ IV-2025-0074 International Transportation (77) Collection ǀ 2025 11 1 Background and research interest 1.1 Introduction The use of fully automated Level 4 journeys in Berlin’s public transport system pursued in the KIS’M project represents an innovation, both for the developers and operators as well as for the potential users. A central research question of this project is the investigation of technology acceptance, which plays a decisive role in the success of the introduction of this innovation. Technology acceptance is considered in a differentiated manner and analysed depending on the acceptance object, the affected subjects and the specific context (Schäfer & Keppler, 2014). The investigation is carried out on various conceptual levels, ranging from individual user intentions and group-specific user behaviour to social approval or rejection and the evaluation of specific user experiences (e.g. through usability tests). Various methods from social and participation research are used to comprehensively analyse the multi-layered determinants of acceptance. Practical application and user testing is a central component of the KIS’M project. Due to changes in technological availability, the service realized within the scope of the project was adapted by the operational and technical partners, and the design of the acceptance tests was adjusted accordingly. A Wizard of Oz setup was implemented through teleoperated driving, so the participants believed the shuttle was fully automated. The acceptance tests between November 4 and 8 2024, were based on a passenger survey (n=35) after two test drives and were conducted at the innovation campus “Berlin TXL - The Urban Tech Republic” (UTR) test site. Initial results from the qualitative and quantitative data collected are presented below. 1.2 Project description The state of Berlin faces the challenge of providing affordable and reliable local public transport in rapidly growing residential areas. KIS’M wanted to test and implement such a demand-responsive public transport service with driverless vehicles at UTR the site of the former Tegel airport and then on the neighbouring public roads. This is to be realised for the first time taking into account the amendment to the Road Traffic Act on autonomous driving (BMDV, 2021). Seamless networking with the mobility system and traffic control is important here. The possible technical solutions are to be further developed with a broad committee of experts and users. In addition, a socially accepted vision of the mobility of tomorrow is to be developed. The project was funded by the Federal Ministry for Digital and Transport (BMDV) with €8.46 million over the period from the beginning of 2022 to spring 2025. The main objectives of the project are the implementation of driverless on-demand transport and the development of safe, reliable and accepted processes for technical supervision at virtual stops and in the vehicles are among the ambitious goals of KIS’M. Better networking and cooperation between automated vehicles and with the traffic control system are intended to achieve greater traffic safety, even in a mixed system. More up-to-date and accurate map and traffic information is to be derived from the data recorded in traffic using artificial intelligence. In addition, KIS’M intends to use the experience gained to develop a strategy for the further use of driverless vehicles and their transfer to regular operation in the state of Berlin. 1.3 Research interest The overarching goal of this study is to develop a deeper understanding to support the widespread introduction of driverless minibuses in Berlin’s public transport services. The aim is to ensure that the service meets the diverse user requirements so that it not only appeals to existing demand but also offers the potential to promote a transport transition towards a reduction in motorised private transport. The technology of driverless minibuses could represent a significant improvement in the quality of the service, which is also attractive to car users and could therefore increase the acceptance and use of this transport alternative. The KIS’M project implemented an important innovation: test users were able to experience and then evaluate a fully automated Level 4 drive without being aware of the different underlying technology. This opportunity enables the investigation of technology acceptance under relatively realistic conditions. The study focuses on three central questions: (1) who chose to use the shuttle (with attention to potential user-group categories), (2) whether participants would intend to use such a shuttle once fully implemented after the test ride (usage intention as the core acceptance construct, following UTAUT2), and (3) how frequently they would use it if it were available. In addition, attitude-based items were included to capture general, usage-independent evaluations of such services. These attitudinal dimensions were developed through a mixed-methods approach and informed by qualitative pre-interviews. The overarching aim of the study was to capture and descriptively analyse the distribution of acceptance, drawing on the dependent variables of the UTAUT2 model (Venkatesh et al., 2021). Acceptance was operationalised primarily through usage intentions. The questionnaire was further complemented by measures of latent attitudes related to mobility as well as by user characteristics, including socio-demographic attributes, mobility-related factors, and other personal characteristics. Additional attitude items addressed perceptions of the service — such as environmental friendliness or contributions to overall quality of life — enriched by qualitative insights. This multidimensional approach enables the identification of potential opportunities and barriers to the adoption of automated shuttle services. Understanding these characteristics is essential for interpreting acceptance patterns and for identifying distinct user groups. The test deployment served a dual purpose. First, it provided an empirical setting to validate the survey instrument for a subsequent representative, city-wide household study in Berlin in 2025. Second, the data collected from this pilot sample enabled an initial evaluation of user experience and a descriptive profiling of users. These insights support the development of hypotheses about relevant user groups and their acceptance levels prior to large-scale testing in the representative study. 2 Description of methods In order to record the technology acceptance of (potential) users, this project proceeded on various levels. A triangulation of quantitative and qualitative methods was used. The aim was for these methods to iteratively complement each other to be able to analyse as many different aspects of acceptance of autonomous driving in Berlin’s public transport system as possible. The following methods were used: Expert interviews, focus groups, passenger tests and representative Berlin-wide survey conducted after the tests in 2025, which will presented in an upcoming publication. The expert interviews were used for preliminary exploration of the concept of acceptance and further categorisation for the following survey methods. The focus groups focussed on the evaluation of the planned service (use of an autonomous shuttle (level 4, on-demand, ride-pooling) on the first and last mile of Berlin’s public transport system). The passenger tests served as an exploratory, non-representative survey in which potential users could gain and evaluate initial experience with autonomous driving technology and its integration into public transport. However, this was a laboratory-like situation. Due to the difficulty of technically implementing level 4 automation in road traffic, the journeys were carried out teleoperated. However, a purely autonomous driving situation was simulated for the test subjects, with the legally required safety driver being sold as an observer. A test track was set up on DOI: 10.24053/ IV-2025-0074 International Transportation (77) Collection ǀ 2025 12 MOBILITY  autonomous shuttle busses eral participants also stated that they were critical of the increasing singularisation of society, meaning that the wishes of each individual must always be taken into account immediately. Category - Perceived safety: In the case of the target group of older people, the main issue raised was safety through accessibility. Several people criticised conventional BVG vehicles in terms of accessibility when boarding, for example when bus drivers do not stop close enough to the kerb, which is a particular problem for people with walking difficulties. Concerns were also expressed that there might not be enough space for wheelchairs or pushchairs due to the small size of the vehicles. The time of day also plays a role in safety concerns. Some emphasised that safety concerns on public transport are particularly prevalent at night. Older people and people with walking difficulties often need barrier-free access and well-lit stops and paths at to feel safe. In this context, the proximity to other people was emphasised as a safety concern, as there are no alternative options in a vessel the size of a shuttle. In this context, respondents mentioned negative experiences with conventional means of transport, such as bad odours or a reluctance to sit too close to other people. At the same time, it was also noted that the transport companies are generally trusted to place sufficient emphasis on passenger safety. Category - Intended use: If we look at the statements regarding the usage category, we can recognise different attitudes. There were voices that would definitely consider the offer in comparison to conventional means of transport. Different scenarios of use were mentioned, especially for the transport of heavy items, some luggage or shopping. In addition, the possibility of a “neighbourhood bus” was mentioned, i.e. the possibility of direct transport with such a shuttle close to one’s own place of residence to be able to cover short distances. However, alternative scenarios for bridging the first or last mile were also repeatedly mentioned. In particular, the option of walking still seems to be a serious alternative for the participants. One person spoke of being happy to walk the first or last mile, as long as it was attractively designed. Walking was also mentioned as a way to stay physically fit. However, participants noted that they would consider using a shuttle for the first or last mile if their physical condition deteriorated and they were less able to walk. Nevertheless, a so-called singularisation was also criticised here (as in the on-demand transport setting category). Instead, emphasis should be placed on planning a city in a way that makes the UTR site for this purpose, on which test subjects (n=35) completed two different test drives and answered both quantitative questions in a survey and qualitative ones in a group discussion, both on the technology assessment or on a potential intention to use it. The evaluation of the user data collected from the passenger survey therefore highlights the following: 1. a detailed description of the sample; 2. an analysis and exploration of the mobility behaviour of the respondents and 3. an examination of the acceptance distribution, taking into account both the intention to use and support of the service based on attitudes independent of usage. 3 First results 3.1 Initial results from the qualitative analysis 3.1.1 Focus groups The results of focus groups with the target group of older people (>65 years) are presented below. The target here was on the evaluation of the offer based on a travel chain created in advance and presented to the participants. To systematically evaluate the results, categories were created before the analysis using a deductive procedure, on the basis of which the transcript of the focus group was analysed. Category - Choice of transport: In relation to the question about the evaluation of different means of transport and the associated personal experiences, it was stated here that participants prefer public transport (rail) as long as they do not have to make any or fewer changes. One person argued that they accept time restrictions but can compensate for these with other advantages, such as more comfort, no stress when changing between modes of transport or the ability to do non-travelling activities such as reading or relaxing. Category - Hiring on-demand traffic: In the first group, a number of criticisms and reservations were voiced about an on-demand service. For example, there was criticism of potential traffic congestion caused by large numbers of vehicles. Concerns were expressed that an on-demand system that sends smaller vehicles could flood the roads and lead to a chaotic transport system. Fixed timetables and larger vehicles would be a better solution instead. This need for reliable timetables instead of individual bookings was also mentioned, being sceptical about the flexibility of the on-demand system and preferring welltimed timetables. In addition, reliability and predictability are a decisive advantage, as this avoids unnecessary use of resources for individual requests. In this context, sevthe most sense for society as a whole. This means, for example, that all everyday necessities are accessible within a short space of time and that public spaces are designed in such a way that they fulfil a wide range of requirements 3.1.2 Group discussion after test drive Following the test drives, the test subjects were asked open questions. The test subjects’ statements on their general intention to use the shuttle service are presented below. Positively some respondents stated that they could imagine using such a service as they find it practical and flexible. It would be an attractive option for short distances in particular, such as the so-called first or last mile. It could be a useful addition in poorly connected areas or at certain times of the day, such as at night. Some mentioned specific use cases such as use on hospital grounds or for transport to cultural events. The shuttle was also seen as a promising solution in rural areas to better connect remote areas and make everyday life easier. Another argument in favour of using the shuttle was the environmental aspect. Many respondents expressed the hope that the shuttle could make public transport more attractive and thus reduce private transport. Parked cars could thus disappear from urban areas, which would improve the quality of life. Some participants were also enthusiastic about the technology. They found the test drive pleasant and quiet and saw the technology as an exciting new concept that could enable innovative mobility solutions. Despite this positive feedback, there were also numerous critical voices. One common reason for not using the service was technical concerns. Many test subjects had doubts as to whether the system would work reliably enough, especially in the event of malfunctions or unforeseen situations. The fact that there is no human on board to intervene in an emergency also led to uncertainty. The cost-benefit ratio was also viewed critically. Some participants considered the costs to be disproportionate, especially for short distances that could just as easily be covered on foot or by bike. Another point of criticism was the logistics of the shuttle. Some expressed concern that waiting times or detours could lead to disadvantages because of the ridesharing concept. This would be a considerable restriction, especially if there is time pressure. The speed and efficiency of the shuttle were also questioned, especially in comparison to bicycles or other modes of transport. One specific target group that was frequently mentioned in the responses was senior citizens. Possible barriers due to the need to use apps or QR codes were mentioned DOI: 10.24053/ IV-2025-0074 International Transportation (77) Collection ǀ 2025 13 autonomous shuttle busses  MOBILITY here, which could be difficult for older people to overcome. Some respondents had the impression that the shuttle was a “technical gimmick” that did not cater for all population groups equally. In addition to these practical and technical concerns, there was also general scepticism. Safety concerns played a major role. Participants wondered how the system would react to unforeseen obstacles or how situations could be handled in which the shuttle suddenly stopped and could not continue its journey. There were also concerns that the system could be misused, for example by vandalism or unauthorised persons entering the shuttle. The idea of sharing a vehicle with strangers made some test subjects uncomfortable, particularly with regard to possible conflicts or unpleasant behaviour on the part of passengers. Another point raised was the long-term integration of the shuttle into local public transport. Some respondents emphasised that the service should not compete with mass transport systems such as trams or underground trains. Instead, they saw it as a supplement in poorly developed rural areas or as a feeder to central hubs. However, it was also criticised that such projects are often introduced without sustainable planning and then discontinued. The test subjects expressed clear expectations with regard to the technology. They wanted transparent information about waiting times, journey times and possible detours to make it easier to plan usage. The operation of the shuttle should also be as intuitive as possible, with simple buttons or clear instructions. Some saw the need to provide a contact person, either virtually or via a central control centre, to offer support in the event of questions or problems. To summarise, it can be said that the test subjects see both opportunities and challenges in the autonomous shuttle. While some recognise the potential and advantages, such as flexibility, environmental friendliness and innovative technology, there are also technical, logistical and social concerns that could hinder widespread acceptance. Successful deployment therefore requires transparent communication, simple operation and sensible integration into existing transport concepts. 3.2 Initial results from the quantitative analysis 3.2.1 Sample description The survey sample comprised 37 participants, of whom 2 were excluded due to straight-lining responses, resulting in an effective sample of n = 35. This sample is not representative for Berlin households. Participants were recruited through multiple channels, using a recruitment tool from the project Partner Deutsches Zentrum für Luft- und Raumfahrt (DLR), other project contacts, and a snowball sampling system. Participation was voluntary but incentivised with a compensation payment. Despite the limited sample size, the 35 respondents who completed the survey exhibited substantial variation in both socio-demographic and mobility-related characteristics. Of the 35 respondents, 27 live in Berlin districts. The differentiation between places of residence inside and outside the centre of Berlin (Ringbahn-line highlighted in red in Figure 1: Participants’ places of residence by postcode; own illustration (Geoportal Berlin; OpenStreetMap) DOI: 10.24053/ IV-2025-0074 International Transportation (77) Collection ǀ 2025 14 MOBILITY  autonomous shuttle busses of people stating that their privacy is only partially or not at all restricted, while 9% do not feel restricted at all. The results show that environmental friendliness plays an important role for the respondents: 60% of participants stated that they fully or somewhat feel obliged to use climate-friendly modes of transport based on personal principles. Within Berlin, this aspiration appears to be practicable, as only 23% of respondents tended to (strongly) disagree with the statement ”I can do what I want to do by public transport”. At the same time, however, 31% state that they (rather) cannot organise their everyday life without a car. Nevertheless, over two thirds of respondents (69%) find it easy to switch from car to public transport in everyday life. 3.2.2 Acceptance distribution Intended use: According to Venkatesh et al. 2012, technology acceptance can be operationalised using the UTAUT2 (Unified Theory of Acceptance and Use of Technology 2) through behavioural intention to use and use behaviour. Behavioural intention is the central dependent variable of the model, as it is a strong predictor of subsequent actual use. The intention to use was measured among the test subjects with three common items, based on the German translation by Rybizki et al. (2022): 1. Assuming I had access to automated shuttles, I would use them in the future 2. If automated shuttles are permanently available, I intend to use them. 3. I want to use these automated shuttles when I get the chance. Following the user experience at the testing area, responses to all three questions were highly positive, with 80-88% of participants indicating strong agreement. The reliability analysis for the BI variables shows a very high internal consistency with a Cronbach’s alpha of 0.891 and three items. This means that the items reliably measure the same construct and the scale is robust for further analyses. An aggregated variable was created by computing the mean of the three UTAUT items (sum divided by three, with values ranging from lower to higher), which was exhibits the highest daily usage at 51% (Berlin 28,1 %: ibid.), indicating its importance in participants’ routine mobility. Walking is also frequent, with 49% reporting daily travels exclusively on foot. Weekly patterns reveal that 23% use bicycles one to three times per week, 31% use cars, 34% use public transport, and 40% walk. Monthly or less frequent use is relatively low for public transport (3% use it less than monthly, none never use it), while bicycles and cars show greater variation, with 17% of participants never or almost never using a bike and 11% never or almost never using a car. Overall, these data suggest a high reliance on public transport and walking, complemented by moderate bicycle use and lower car dependency, reflecting a mobility pattern typical of urban, multimodal transport environments, but even higher as in Berlin average households. Access to forms of mobility: Almost 83% of respondents (n=29), stated that they had a car driving licence. This means that an above-average number of people with a car driving licence are represented in the sample. Around 75% of the respondents have a monthly ticket in public transport, which is much higher than in Berlin average of 30% (Tagesspiegel, 2023). With 20% of people in the sample not having a car at their disposal, including car sharing, the picture is consistent with driving licence ownership. However, the 63% always having a car available exceed the 49% of households in Berlin that have at least one car (MiD, 2017). 20% again do not own a working bicycle. In metropolises, an average of 28% do not own bicycles (MiD, 2017). Of those surveyed, 11% ride electrified bicycles. Attitudes towards public transport: These indicators on mobility attitudes are based on Hunecke et al. 2021. The questions on mobility attitudes show that physical proximity is a problem for many people on public transport: 46% of respondents fully or somewhat agree with the statement ”People get too close to me in an unpleasant way on public transport” . Restriction of privacy, on the other hand, is perceived less strongly, with the majority (66%) the figure) suggests that respondents within the Ringbahn area tend to have better public transport connections, while respondents outside are more frequently dependent on cars. Although there is no clear distinction between postcodes and the Ringbahn route, a rough classification can be made. This shows that a higher proportion of respondents live outside the Ringbahn (within: 7; outside: 13). Age: With 18 male and 17 female respondents, the analysis shows that the gender distribution among the respondents was almost balanced. The largest age group among the respondents is the 20-29 age group (46%), followed by the 30-39 age group (11%). The other age groups have lower proportions, although the distribution is relatively even from the age of 40. To counteract the uncertainty surrounding the relationship between age and openness to technology, technology adoption types were also examined according to Rogers’ diffusion model. Adopter types: The sample shows an above-average number of “innovators” and “early adopters” (Rogers, 2003[1962]). While innovators account for 2.5% in the traditional distribution, the proportion of people achieving the highest scores in this sample is 26%. At 20%, the proportion of participants achieving high scores is also significantly higher than the usual percentage representing “early adopters” (13.5%). This shows a bias of the sample in favour of people with a high affinity to technology and people who are willing to take risks. Accordingly, there is a underrepresentation of the early and late majority as well as the “laggards”. This is the case with laboratory-like technology trials - participation may rely on voluntary engagement and a sense of curiosity. Level of education: Over 54% of the test subjects had a university degree, which is well above average. In 2022, 20% of individuals aged 15 and over held an academic degree (Destatis, 2024). Following Rogers, it can be assumed that more highly educated people have a generally positive attitude towards technology, as they potentially understand it better and recognise its benefits. Employment: Three groups were frequently represented among the 35 test subjects: at 34%, students were most frequently represented, 23% were employees and 17% were pensioners. Mobility Behaviour The mobility behaviour of the sample shows a diverse usage pattern across different transport modes. Bicycles, including e-bikes, are used daily or almost daily by 43% of participants, while car usage is lower, with only 20% (Berlin 14,5 %: SRV 2023) reporting daily use. Public transport (regional bus and train) (electrical) bike car regional bus & train exclusively walking daily or almost daily 43 % 20 % 51 % 49 % one to three days per Week 23 % 31 % 34 % 40 % one to three days per Month 6 % 23 % 11 % 6 % less than monthly 11 % 14 % 3 % 6 % never or almost never 17 % 11 % 0 % 0 % Table 1: Frequency of use per mode of transport (n = 35) DOI: 10.24053/ IV-2025-0074 International Transportation (77) Collection ǀ 2025 15 autonomous shuttle busses  MOBILITY which represents a substantial and impressive proportion this question was asked independently of personal usage intentions. 68% (somewhat or fully agree) perceive this as an improvement to the quality of life in their neighbourhood this may be attributed to the fact that it provides coverage for gaps or the last mile in public transport networks, which has not been previously available. 74% see the offer as an opportunity to contribute to climate protection as enhancing the attractiveness of public transport through such services may help reduce private car usage and thereby lower emissions. From the perspective of local residents, respondents demonstrate high tolerance, as 72% accept that minor disruptions may occur during the introduction of the service. Only a limited correlation can be identified between mobility attitudes and usage intention. The item environmental friendliness shows a low positive correlation with the intention to use the shuttle recorded after the test drive (r = 0.275). However, this effect is not statistically significant at a 95% confidence level (p = 0.110). The items on rejection of physical proximity and restriction of privacy correlate slightly negatively with the intention to use the shuttle surveyed before the test journey (r = -0.114 and r = -0.112). People who value privacy and personal space intend to use the shuttle less. However, these effects are also not significant (p = 0.516 and p = 0.520 respectively), and after the test drive there are no longer any relevant correlations. This could lead to the interpretation that the use has dispelled these concerns, which has to be further explored. 4 Discussion and Conclusion This study examined user acceptance of an autonomous shuttle service in the context of an on-site passenger survey, undertaken prior to a comprehensive, city-wide investigation planned for 2025. The analyses presented here remain exploratory in nature. A more extensive bivariate examination of socio-demographic and mobility-related determinants of acceptance, as well as a multivariate modelling of the classical constructs of UTAUT2, could not yet be conducted due to the modest sample size. Such analyses reed that 51.4% indicated trust in the technology, 57.1% attributed it to the fact that it was a test ride rather than a real traffic situation, and 34.3% felt reassured by the presence of observers in the vehicle. 3.2.3 Attitudes independent of intended use In addition to the UTAUT2 model, which operationalizes acceptance based on intention and usage, theoretical groundings (e.g., Lucke, 1995) and our qualitative results show that there are “usage-independent” attitudes that describe technology acceptance. This was formulated through the support-variable “I support the introduction of automated shuttles in public transport” and additionally through further variables to query usage-independent attitudes. Such as the question about improved quality of life in one’s own neighbourhood after the introduction, or the willingness to overlook initial disruptions during the introduction, as well as the questions “Such shuttles contribute to improving the quality of life in my neighbourhood” and “Such shuttles contribute to climate protection.” Acceptance is therefore seen as a broader support of the use of driverless minibuses in local public transport. The analysis aims to quantify the perceptions and expectations of the respondents in these categories and to evaluate their relevance for supporting the new mobility offer (table 4). 3.2.4 Description and interpretation Fundamentally, 81% (somewhat and fully agree) of respondents favour a comprehensive introduction of such shuttle services, used as the dependent variable in further analyses. The aim is to analyse the determining factors behind acceptance and to quantify their influence on the dependent variable by means of a variance decomposition. Overall, the respondents exhibited an average cumulative usage intention (‘agree’ and ‘strongly agree’) of 74 %. Extended question to specify the dependent variable: A specific extension of the question was made in order to capture the dependent variable more precisely. This is done by focussing on the regularity of use to quantify the frequency of use as a decisive factor. This extension aims to differentiate the underlying usage patterns more clearly and to enable a differentiated analysis of usage habits. Planned frequency of use: The planned frequency of use for the shuttle services shows a remarkable intensity. 74% of respondents stated that they would use the service at least 1-3 times per month, with 32% even stating that they would use the shuttles at least once a week, i.e. regularly. These figures reflect a significant intention to use the service regularly and could indicate that there is an interest in using the shuttle on a regular basis, provided that the availability and the service meet users’ expectations. But the attitude behavior gap could additionally be a problem. In the forerunner project Shuttles&Co a quite irregular use was noted, with only 8.5% of the 246 surveyed passengers stating that they used the line-based L3-shuttles at least once a week (Linke-Wittich et al., 2023). Willingness to pay: A more reserved assessment of the offer can be observed in the “willingness to pay more”: cumulatively, 49% of respondents answered, “rather not” or “not at all” to the question: “I would be willing to pay more for the expansion of public transport services through such shuttles.” Safety perception during the ride: The perception of safety during the ride was predominantly positive. Reasons for this includ- Table 2: Responses on the Items of Behavioural Intention (n=35) I support the introduction of automated shuttles in public transport. If automated shuttles are permanently available, I intend to use them. I want to use these automated shuttles when I get the chance. Mean strongly disagree 3 % 0 % 0 % 1 % rather disagree 17 % 20 % 3 % 13 % neutral 11 % 11 % 11 % 11 % rather agree 46 % 43 % 49 % 46 % strongly agree 23 % 26 % 37 % 29 % Frequency % never or almost never 4 11 less than monthly 5 14 one to three times per month 15 43 one to three times per week 10 29 daily or almost daily 1 3 Table 3: Intended Frequency of Use MOBILITY  autonomous shuttle busses DOI: 10.24053/ IV-2025-0074 International Transportation (77) Collection ǀ 2025 16 Stelter, Sarah (2023). Akzeptanzuntersuchung von hochautomatisierten Shuttlebussen im Realbetrieb in Berlin-Tegel - Erkenntnisse zu Einstellung und Nutzung. Journal für Mobilität und Verkehr (17), 14-26. 8. Lucke, D. (1995). Akzeptanz: Legitimität in der “Abstimmungsgesellschaft“. Leske + Budrich: Opladen. 9. Rybizki, A.; Ihme, K.; Nguyen, H.P.; Onnasch, L.; Bosch, E. Acceptance of Automated Shuttles-Application and Extension of the UTAUT-2 Model to Wizard-of-Oz Automated Driving in Real-Life Traffic. Future Transp. 2022, 2, 1010-1027. https: / / doi. org/ 10.3390/ futuretransp2040056 10. Tagesspiegel 3.5.2023: “Zehntausende neue Abonnenten bei der BVG: Fast 900 Millionen Fahrgäste befördert”, https: / / www.tagesspiegel. de/ berlin/ 29-euro-ticket-in-berlin-zehntausendeneue-abonnenten-bei-der-bvg-9756320.html, 12/ 11/ 2025 11. Van der Laan, J.D., Heino, A., & De Waard, D. (1997). A simple procedure for the assessment of acceptance of advanced transport telematics. Transportation Research - Part C: Emerging Technologies, 5, 1-10 12. Schäfer, M. & Keppler, D. (2013). Modelle der technikorientierten Akzeptanzforschung - Überblick und Reflexion am Beispiel eines Forschungsprojekts zur Implementierung innovativer technischer Energieeffizienz-Maßnahmen. In: ZTG Discussion Paper. 13. SRV, 2023: https: / / www.berlin.de/ sen/ uvk/ _assets/ verkehr/ verkehrsdaten/ zahlen-und-fakten/ mobilitaetin stae dten sr v-2023/ sr v_ 2023 _ b e r lin _ t ab e lle n b e r icht.p d f ? t s=174 4 02635, 12/ 11/ 2025 14. Venkatesh, V.; Thong, J.Y.; Xu, X. Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Q. 2012, 36, 157. Eingangsabbildung: © Arndt ipants to support the early stages of implementation. The qualitative findings deepen this picture: participants recognized both potential and challenges. They emphasized flexibility, environmental advantages, and the innovative character of the shuttle, but also voiced technical and safety-related concerns, uncertainties regarding cost and reliability, and usability challenges particularly for older persons. Overall, acceptance is present but contingent upon transparent communication, a user-friendly interface, and meaningful integration within the broader public transport system. In sum, while the sample is not representative, the results provide valuable early indications of user acceptance and highlight the strengths of the survey instrument. They suggest that subsequent testing phases should - as widely intended move beyond controlled environments and be carried out under real-life operating conditions with actual user groups. To establish generalizable findings and allow for robust multivariate analyses of acceptance determinants, the instrument will be applied in a representative, city-wide survey across Berlin. ▪ SOURCES 1. BMDV - Bundesministerium für Digitales und Verkehr (2021): https: / / www.bmv.de/ SharedDocs/ DE/ Artikel/ DG/ gesetz-zum-autonomen-fahren.html, 12/ 11/ 2025 2. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. 3. Destatis, 2024: https: / / www.destatis. d e / D E / P r e s s e / P r e s s e m i t t e i l u n g e n / Z e n s u s 2 0 2 2 - P r e s s e m i t t e i l u n g e n / P M _ z e n s u s 2 0 2 2 _ 5 0 . h t m l ? u t m _ s o u r c e = c h a t g p t . com12/ 11/ 2025 4. Hunecke, M., Heppner, H., & Groth, S. (2021). Questionnaire on psychological factors influencing car, public transport and bicycle use (PsyVKN): factor structure, psychometric properties and validation. 5. MiD - Mobilität in Deutschland (2017): https: / / www. mobilitaet-in-deutschland.de/ downloads.html 6. Rogers, E. M. (2003[1962]). Diffusion of innovations. 5. Aufl. New York: The Free Press. 7. Linke-Wittich, Robert; Schäper, Christoph; Arndt, Wulf-Holger; Busch, Jakob; van der Wel, Elmer; quire a substantially larger and more diverse sample and will therefore be carried out as part of the forthcoming Berlin-wide survey. Several limitations must be acknowledged. First, the experimental setting resembled a laboratory-like trial rather than a real-world deployment, which may have influenced participants’ behaviour and expectations. Second, the sample size was small, and participant recruitment did not involve random procedures. As is typical for technology trials of this kind, participation was likely driven by voluntary engagement and curiosity, resulting in a self-selection bias. Moreover, the study design does not capture the perspectives of individuals who chose not to attend the test day (“non-users”), even though understanding acceptance barriers requires explicitly surveying those who do not intend to use the service. Identifying the motivations for actual non-use remains challenging, particularly when data is collected at the interface between surveying intended use before implementation and observing initial use (and user experiences) in pilot-like scenarios. Despite these constraints, the findings demonstrate that the measurement instrument especially the UTAUT2 construct of behavioural intention to use exhibited high construct validity. Furthermore, methodological triangulation enabled the development of additional attitude-based items that proved analytically fruitful. The results indicate a high intention to use the shuttle among a heterogeneous set of test participants following the ride experience: 74.3% expressed positive usage intention (“agree” or “strongly agree”). The user-group characteristics reflect a comparatively young, highly educated cohort with strong everyday public transport use and a higher share of “innovators” in the sense of Rogers (2003). However, when asked about expected frequency of future use, only one third reported an intention to use the service regularly a quantity that may be further affected by the problem of the attitude-behaviour gap. Potential acceptance barriers emerged as well. Even among a largely public-transport-affine group, almost half (45.7%) reported discomfort with the physical proximity of other passengers, indicating that shuttle size and the absence of a driver may represent substantive concerns. Although respondents ascribed potential benefits to the shuttle such as enhanced neighbourhood quality of life and contributions to climate protection, with 60% reporting a personal commitment to climate-friendly mobility there remains no willingness to pay higher fares, which constitutes a clearly identifiable acceptance barrier. At the same time, the high tolerance for initial operational disruptions (72% acceptance) suggests readiness among partic- Wulf-Holger Arndt, Dr.-Ing., Head of research unit Mobility and Space, Center for Technology and Society, Technische Universität Berlin, Germany wulf-holger.arndt@tu-berlin.de Jakob Busch, Center for Technology and Society, Technische Universität Berlin, Germany Robert Linke-Wittich Katharina Lange Christoph Schäper (electrical) bike car regional bus & train exclusively walking daily or almost daily 43 % 20 % 51 % 49 % one to three days per Week 23 % 31 % 34 % 40 % one to three days per Month 6 % 23 % 11 % 6 % less than monthly 11 % 14 % 3 % 6 % never or almost never 17 % 11 % 0 % 0 % Table 4: Attitudes independent of intended use DOI: 10.24053/ IV-2025-0074 International Transportation (77) Collection ǀ 2025 17 autonomous shuttle busses  MOBILITY the case of railways, as a new system, the track, means of transport, operating and safety systems, and interfaces with the surrounding environment had to be considered from the outset. This resulted in a “closed system” with defined and controlled entry and exit points for the transport of people and goods. Motorized private transport, on the other hand, initially developed on the entire existing historical road network, 1. Preliminary remark Paradigm was not a standard term in sciences with a focus on practical application, such as construction or planning disciplines. Due to the rapid technical development of means of transport during the industrialization period and thereafter, it was first necessary to develop the foundations for their infrastructure, legal basis, space requirements, and operating conditions. In which was not designed for the new vehicles and speeds. Over time, adaptation to the demands of cars and car traffic led to an “open system” without controllable entry and exit points, designed according to the individual ideas of various decision-makers across different disciplines and without clear lines of responsibility. This development was largely influenced by the economic interests of the industries involved. This had an impact The Paradigm Shift in Transportation: Progress for Science, but a Revolution for Practice. A summary Paradigm shift, core hypotheses, system behavior, evolution, consequences for practice The concepts of mobility, space-time-speed, and freedom of choice as core hypotheses in motorized transport correspond to our worldview. This paradigm brings with it new problems such as traffic jams, traffic fatalities, climate damage, and urban sprawl. However, technological and cultural development is exceeding the limits of our evolutionary capabilities. The paradigm shift was brought about by insights into system behavior and our own core hypotheses, which have been empirically confirmed. These can be described as scientific progress, but they represent a revolution in practice. Hermann Knoflacher DOI: 10.24053/ IV-2025-0075 International Transportation (77) Collection ǀ 2025 18 on the research branches and institutes at universities as well as the technical colleges that were gradually established. In the process, existing modes of transport were subordinated to the requirements of car traffic or, in the phase of rapid motorization, were often simply forgotten[1]. As an open system, unlike railways, motorized road traffic was designed by various disciplines. In addition to engineers, these included urban planners, architects, lawyers, and later also spatial planners. They all shared ideas about the motorization of the population, the development of “mobility,” speeds, and the optimization of private car use[2]. The planning and design of facilities and operating conditions were based on experience, successful assumptions, and legal conditions that were only established in the course of motorization in the first half of the 20th century. Driving dynamics and traffic flow were at the center of this sectoral way of working and thinking, which also included legally securing and massively subsidizing the space requirements for passenger cars. “The car is part of the home” was the attitude of teachers and professionals not only in the 1970s, but one that is still reflected in building and garage regulations today. 2. The paradigm of the transport system The unquestioned basic assumptions on which most of today’s (not only) road traffic is based are essentially the following: ƒ Growth in the mobility of the population with increasing prosperity. ƒ Economic benefits of fast transport systems through travel time savings. ƒ Omnipresence and priority of motorized transport. Justifications are primarily derived from the analysis of data such as the relationship between gross national product and the level of motorization[3] as an argument for the growth of mobility with increasing prosperity[4], the individual experience of shorter travel times between source and destination due to higher speeds, which can be used to calculate and justify the benefits of faster transport systems[5], and the population’s desire to own a private car[6]. The legal system for road traffic was designed in accordance with these conditions, so as the technical guidelines that determine the physical design of traffic facilities and operating conditions. The basic assumptions for the design of the transport system, which have also been adopted in teaching and administrative practice, were and are so convincing from individual experience that they have not been questioned and still provide the political justification for the transformation of the world from a people-friendly to a car-friendly one. They can be described as the core hypotheses of transport[7] on which today’s transport system is based. 3. The problem in practice: not everything works as predicted The methods[8] of this paradigm have reduced some of the problems of transport in the industrial society, but they have created new ones, which have been perceived as symptoms and are being mitigated but without system-thinking in mind. Thus, neither the expansion nor the electronic control of traffic signal systems has prevented traffic jams under the existing premises but has instead increased them to new dimensions. Added to this were the problems with general noise pollution[9] from this system, changes in settlement and economic structures[10] and [11] that had not been anticipated, the release of greenhouse gases[12] with serious consequences for climate change[13]. These are terms that do not describe a static state, but processes that have been created by modern technical transport systems. Even such elementary principles as the preservation of human life and health have literally “fallen by the wayside” when more than 1.2 million people are killed in the system[14] and tens of millions suffer serious, sometimes irreversible, damage to their health[15] per year. However, this only concerns the direct effect on life and health and not the indirect effects on the main causes of death in today’s society, such as cardiovascular disease caused by exhaust fumes[16] and traffic noise[17] [18]. One of the best documents on this subject is the book published by Dinesh Mohan in 2014, “Traffic Safety, Sustainability, and the Future of Urban Transportation”[19], which covers a wide range of measures within the old paradigm. At that time, however, the paradigm shift that was fundamentally changing the concepts, views, and responsibilities within the disciplines was already recognized scientifically. 4. The emergence of the paradigm shift has several sources In transportation, too, the paradigm shift did not arise intentionally, but rather from efforts to improve the prevailing assumptions, limits, methods, and procedures. In retrospect, it is clear that the paradigm shift in transportation did not emerge from a single discipline or science, but is supported by research findings from several disciplines and is also linked to practice, where questions arose and continue to arise from doubts and contradictions between expectations and experience[20]. Looking back, the following “roots” can be identified: ƒ Contradictions between urban planners[21] and transport planners[22]. ƒ Contradictions between historical urban structures and the demands of car traffic[23]. ƒ Contradictions between observation and fundamental assumptions about the performance of roads[27] and their geometric dimensions[28]. ƒ Questions in laws[24], ordinances[25] and guidelines[26]. ƒ Discoveries in the data : route consistency, mobility purposes[29] and time consistency[30]. ƒ Discoveries of the effects of fundamental evolutionary laws of behavior[31] , even in the technically altered environment of transportation[32] , and even more importantly the realization of where and how cars affect the human brain[33] : Behavior is structurally determined and therefore the responsibility of those responsible for structural design. ƒ The findings of evolutionary theory and evolutionary epistemology as well as systems theory[34]. ƒ Plausibility and logic[35], forecasting and actual development[36]. This opened up opportunities to test scientific insights into the processes of car traffic as a basis for political decisions and their review through practical implementation. The traditional boundaries between disciplines and even between universities (at that time) could be crossed more easily, at least in Austria, if the resources developed in the mainstream could be used to address new issues. The period between 1970 and 1990 and the political conditions and scientific freedoms of the time were the prerequisites for the development, testing, and verification of those research results that can be described as core hypotheses for the paradigm shift 5. Core hypotheses of the new paradigm Some of the findings from various disciplines were already made more than half a century ago, but the connections were not recognized due to the division of disciplines. For example, Lill’s law of travel, which is well known in transportation, can be traced back to fundamental psycho-physiological laws of behavior developed by Weber-Fechner[37] (1846-1872) in the same century. However, it took several more years before the concept of paradigm shift in transportation emerged in 2005, which was not published until 2016[38]. The technical content, including some of the practical consequences, had already been part of the author’s teaching at the institute since 1975, and the principle was published in a paper in 1985[39]. DOI: 10.24053/ IV-2025-0075 International Transportation (77) Collection ǀ 2025 19 Paradigm Shift  MOBILITY ing them. Neither the existing paradigm nor the associated methods are questioned. The causes of the problems this creates remain undetected, partly because the repercussions on the transport system occur via other disciplines and sciences that lie far beyond the boundaries of professional perception.[45] Above all, however, the measures resulting from T1 fit in with the ideology of unlimited growth of the global economic system, the consequences of which are practically demonstrable in T2. The new paradigm has proven itself in practice. The new paradigm can therefore be assessed as scientific progress according to the criteria of Kuhn and Lakatos. 7. Why is the paradigm shift in transportation a revolution for practice? Unlike the paradigm shifts in astronomy or physics, which primarily affected the scientific community, these shifts took place in systems that were not designed, planned, or projected by humans, but initially only provided new insights into existing natural systems, from the universe to human health. They expanded the possibilities of technical progress, improved the use of existing resources (agriculture[46]), and facilitated and enhanced human living conditions. In the longer term they also led to changes in power relations, such as the overcoming of feudal rule. The paradigm shift in transportation, on the other hand, affects many familiar relationships that were shaped by the myths of the old paradigm, and thus also the relations between people, working conditions, and the economy. Transport can no longer be treated as a playground for unchecked political decision-making, because scientific progresses are revealing the flaws of the old system and demanding more responsible action. There is no longer any justification for “mobility growth” by replacing environmentally, socially, urban and cli- 6. Is the paradigm shift in transportation a scientific advance? According to Lakatos, the following conditions must be met in order for a sequence of theories to be reconstructed as scientific progress: A newer theory T2 predicts facts that would not be expected from the standpoint of an earlier theory T1 (“theoretically progressive”); Such hypotheses are partially confirmed empirically (“empirically progressive”); T2 can explain why T1 has proven itself empirically so far [40]. In our case, T1 is the old, still prevailing paradigm, and T2 is the new paradigm. T2 not only predicts that traffic jams are caused by the T1 method[41], but can also justify this theoretically. Similarly, the “death of city centres”[42] and urban sprawl[43] or the flaws of the “economy of scale”[44] in economics can also be theoretically justified. The new paradigm is “theoretically progressive” - and not only in these cases. The hypotheses of T2 have been empirically confirmed in many cases. Forecasts on traffic jams, urban sprawl, the decline and/ or revitalization of city centers, economic centralization, employment effects, mobility costs, accidents, environmental pollution, and climate change can be empirically verified in many cases. What were forecasted in T1 can now be partially predicted in T2 based on system behavior. The new paradigm is “empirically progressive.” T2 can explain why T1 has proven itself empirically so far: The measures in T1 relate to the level of symptoms and are successful in the short term. For example, the prediction that congestion will be eliminated by adding more lanes is usually immediately confirmed. However, the mediumand longterm effects of these forecasts are not only neglected; they are often used as a pretext to repeat or intensify the same short-term measure, instead of investigating the underlying causes of the failure and addressmate-friendly, safe forms of transport such as walking, cycling and public transport with environmentally, socially, urban and climate-unfriendly car traffic, which additionally threatens the life and health of the population and restricts their freedom of mobility. Due to the constancy of travel time budgets and equal travel time distributions of individual mobility, no benefit from higher speeds in the system can be rationally justified[47], because these always lead to cost increases directly through expenditure (laws of physics) and indirectly through negative external effects, land consumption, noise, exhaust gases and undesirable structural changes[48]. The economic theory of “economies of scale” is flawed, incomplete and therefore misleading[49]. Taking the real effects into account, on the other hand, leads to the fundamental evolutionary principle of optimizing resources, energy, spatial diversity and resilience. These facts demand new foundations for teaching, administration, planning, politics, and economics. Cause-related solution by separating private parking spaces from human settlements and activities. The attachment of cars to people changes their behavior in such a way that fundamental social (consideration for the free and safe mobility of unprotected road users), civilizational (structures of cities, villages, local jobs, etc.) and cultural ties (sedentariness, environmental awareness, ethical responsibility, polluter pays principle) are altered and even lost. Millennia of experience with pedestrian-scaled and community-oriented environments were not considered in the last century. Safe and healthy living conditions in human settlements are incompatible with the omnipresence of private motor vehicles. It was only through the excessive destruction of human dimensions in the new development areas of the 20th century as a result of the possibilities offered by private car transport that local, regional, and global problems were created, which are becoming increasingly destructive in ecological, social, economic, climatic, and cultural dynamics. The challenge of car-free settlements, villages, and cities cannot be met by declarations, but requires practical measures in the legal, financial, administrative, and political systems with effective sanctions. The problems must be solved where they arise and not by driving away, as has been the case in the last two centuries. In order not to be accused of fundamentalism, it should be emphasized that this is not fundamentally about every car, but about private or privately used cars. It is not about the use of motor vehicles that facilitate the lives of disabled people as a means of mobility, nor about “working traffic.” Reference values System behavior The unit of measurement is the human being. It adapts to the requirements of humans and their Society. The standard speed is that of a pedestrian. Safe diversity and density of functions. Separation of human activities and private parking spaces. Car-free human settlements. Humans in their life phases and environment. “Mobility in space” is purpose-oriented. Constancy of the average number of trips per day. The transport system must be geared towards functional diversity, proximity, social relationships, safety, nature, and a sustainable economy. There is no time saving through speed in the system. Fast means of transport become disruptive factors. In addition to the laws of nature, the biological laws governing living beings, human behavior and the limits of our senses must be taken into account and accepted. The bond between cars and people leads to fundamental changes in individual and systemic behaviour and prevents free choice of mode. Table 1 Core hypotheses of the new paradigm MOBILITY  Paradigm Shift DOI: 10.24053/ IV-2025-0075 International Transportation (77) Collection ǀ 2025 20 Both account for less than 10% of today’s car traffic and are usually compatible with public transport or can be integrated into pedestrian traffic. 8. Responsibility for the environment as a catalyst for a sustainable and climate-friendly behavior “Catalysts occur in nature in many different ways. In living organisms, almost all chemical reactions that are essential for life are catalyzed (for example, in photosynthesis, respiration, or energy production from food). The catalysts used are usually specific proteins, such as enzymes.” It should be noted that this is a very ancient evolutionary environment in which these processes take place as described and illustrated[50], in which the respective catalyst influences the decisions of the further course. If the result proves successful under the given conditions, it also produces repercussions in the system. This can be seen in many examples in industry, such as the Haber-Bosch process for synthesizing ammonia from the elements oxygen and hydrogen [51]. From the perspective of the new paradigm, the transportation system involves processes in the formation of relationships between living beings and their environment. Under today’s conditions, this environment is mostly artificially altered, having been optimized for cars and car traffic over the last century. As demonstrated in the third core hypothesis, the bonds between people and cars are so deeply rooted in evolutionary terms due to the way parking spaces are arranged that the “activation energy” required to travel by private car is reduced far more than for any other forms of mobility. This, in turn, encourages thinking, planning, projecting, financing, legitimizing, and organizing mobility in favor of private car travel as the “natural” way to meet human needs. Today’s transportation systems thus emerged from the sum of the individual needs of a motorized society, neglecting all other forms of mobility, construction, settlement, administration, and even economic activity. And this is established, defended, and legally secured by alternating feedback from within the oldest evolutionary layer and from outside by the seemingly rational actions of science, research, teaching, administration, industry, the media, and politics. The fact that human rights have fallen by the wayside in the process is no longer even noticed[52]. The new paradigm makes it possible to hold those responsible for reestablishing the catalysts for behavior that is humane, socially, environmentally, and economically compatible, sustainable, and enforceable in a climate-friendly manner. 9. Practical consequences of the paradigm shift When we, as a society, have spent over 100 years building a world and have become accustomed to the individual advantages that are believed to be progress, the difficulty lies in realizing that the system behaves differently than previously assumed. Mobility becomes personand purpose-oriented and thus detached from the means of transport and can be evaluated and accounted for in a largely ideology-free manner using system indicators such as land use, energy consumption, social impacts, etc. Due to the constancy of time spent in mobility, speed is losing its central importance in planning and project management. When one considers the effort that has been made for more than half a century to introduce or reduce speed limits, one can imagine what a revolution this fact means in practice. Even more difficult is to understand the limits of our evolutionary makeup and the emerging interrelationship between our energy consumption and the mobility energy of cars on our oldest evolutionary brain layer and, from there, on all higher levels of our behavior in feeling and thinking. Regaining freedom requires a physical separation of parking spaces and human activities or settlements. 10. Summary of the effects of the paradigm shift The complex effects of the new paradigm affect not only transportation but all related disciplines. Neither urban sprawl nor the decline of city centers and villages is possible with the separation of car parking spaces and the spatial functions of human activities. The goals of spatial planning can thus be achieved. Spatial planning methods that separate functions in space are no longer justifiable. Settlements will once again be supported by vibrant social and economic relationships, enabling them to become sustainable again. Traffic congestion is not a problem, but rather an indication of a demand for car travel that has been created. It is not a cause for concern, but rather a reason to eliminate the causes of this undesirable development in the system. Public transport can be planned and operated at an appropriate level largely without subsidies. Improvement of quality of life through the elimination of noise, exhaust fumes, and accident risks in public spaces. Strengthening of local autonomy and diversity against corporate encroachment. Wherever cars have been removed from cities, they have been filled with life. Wherever this has not been the case, spatial and urban structures are inadequate. These must be remedied, not further concealed by car traffic, as has been the case up to now. Areas of responsibility become visible with the new paradigm, and competencies shift accordingly. Academic curricula must be redesigned. Measures must be realigned, and those that destroy the system must be made visible and sanctioned. Measures must be reviewed for their compatibility with nature, social behavior, system efficiency, and local economic cycles. Morality and ethics thus become part of practical decisions and measures. 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Dr. tech, Research Unit of Transport Planning and Traffic Engineering Institute of Transportation, TU Wien MOBILITY  Paradigm Shift DOI: 10.24053/ IV-2025-0075 International Transportation (77) Collection ǀ 2025 22 International Transportation (77) Collection ǀ 2025 23 Gerd Aberle Dr. rer. pol. 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