1. Introduction
Rapid urban growth and increasing mobility demands continue to challenge the capacity and efficiency of public transport systems worldwide. According to the (United Nations, 2019), more than 68% of the global population will live in cities by 2050, placing unprecedented stress on existing transport infrastructures.
In many European metropolitan regions, public transport constitutes the backbone of sustainable mobility strategies but faces persistent issues such as capacity limits and skilled labor shortages (International Road Transport Union, 2023). The Munich Metropolitan Region exemplifies this situation as one of the world’s most dynamic regions, where economic vitality and demographic expansion have increased dependence on public transport (Reference Thibault, Nienhaus, Bayen, De Clercq and CartignyThibault et al., 2024).
To meet these challenges, technological innovations, most notably autonomous vehicles, are regarded as promising instruments for enhancing reliability, flexibility, and cost efficiency (Reference Ho, Tan, Lau and KhanHo et al., 2023; Reference Seredynski, Nielsen, Ekman and JohanssonSeredynski et al., 2023). In several European cities, autonomous buses are being explored as a means of extending or complementing existing public transport networks (Reference Makahleh, Ferranti and DissanayakeMakahleh et al., 2024). Their potential lies in reducing operational costs, compensating for driver shortages, and providing service continuity even in the context of resource constraints (Reference Ma, Liu and QuMa et al., 2021).
However, successful and durable integration of autonomous buses depends not only on regulatory and technical maturity but also on achieving public acceptance. Such systems must be designed to align with passenger expectations as well as comfort and accessibility needs. Yet most prior studies have either investigated current public transport patterns or general attitudes toward autonomous vehicles, seldom identifying which of the current public transport users would actually use autonomous public transport in the future and under what conditions. (Reference Ejdys, Gulc, Budna and Esparteiro GarciaEjdys et al., 2025; Reference Ho, Tan, Lau and KhanHo et al., 2023; Reference Schandl, Fischer and HudecekSchandl et al., 2025) Without knowing who is going to actually use autonomous buses, the risk of deploying autonomous systems that fail to meet real mobility needs and preferences is too high. A further challenge is the lack of understanding of demographic factors that influence acceptance, which could hinder widespread adoption: This is a particular problem for passengers with reduced mobility, because they rely even more on the use of autonomous buses and on the assistance provided by bus drivers than regular users do. (Reference Bansal, Kockelman and SinghBansal et al., 2016)
This paper therefore aims to address the question: Which population groups in the Munich Metropolitan Region are most likely to use autonomous buses in the coming years and what implications therefore arise for practical implementation? Drawing on a large-scale online survey conducted in 2024, it analyzes demographic characteristics, mobility behavior, and accessibility aspects, with a specific focus on people with reduced mobility.
2. State of the art
Research into autonomous vehicle acceptance has expanded rapidly, exploring user attitudes, trust in automation, and perceived safety. Empirical studies such as (Reference Bansal and KockelmanBansal & Kockelman, 2018; Reference Weigl, Nees, Eisele and RienerWeigl et al., 2022) consistently identify age, income, and gender as predictors of willingness to use autonomous modes of transport. Younger technology-savvy individuals tend to express higher acceptance, while elderly or risk-averse groups remain skeptical. Yet the demographic structure of future autonomous bus users in the Munich Metropolitan Region remains unquantified. (State capital Munich - Mobility Department, 2023)
Within Germany and Europe, most research emphasizes technical feasibility or general public attitudes. Pilot projects such as “RABus,” “AHOI,” “TaBuLa,” and “Ameise” (Germany) have demonstrated operational reliability and safety but relied on small samples or short-term passenger feedback (VDV, 2025). These trials provide limited demographic representativeness and thus insufficient insight into the potential user base for large-scale autonomous bus deployment.
Moreover, large national mobility studies, such as “Mobilität in Deutschland - MiD” (Reference Nobis and KuhnimhofNobis & Kuhnimhof, 2018), collect rich behavioral data but do not yet address questions about autonomous public transport. Consequently, there remains a clear empirical gap in terms of who will travel on autonomous buses and how these systems will interact with established multimodal mobility structures, particularly in metropolitan regions like Munich where public transport usage is already high.
This study was designed to close this gap by collecting quantitative data on demographic attributes, travel habits, accessibility requirements, and attitudes toward autonomous buses. In contrast to prior research, it integrates classical mobility indicators (vehicle ownership, modal split, trip frequency) with accessibility and inclusion variables. This comprehensive approach enables identification of likely early adopters and vulnerable groups requiring special consideration in the design and governance of autonomous public transport.
3. Methodology
The following chapter discusses the selection and implementation of the chosen research method for answering the research question. The method is grounded in analytical sociology and applies the “Extended Model of Mobility Behaviour” (xMooBe), developed by (Reference Weyer and HoffmannWeyer & Hoffmann, 2025), instead of referring to the Technology Acceptance Model (TAM) or the Theory of Planned Behaviour (TPB).
3.1. Selection of the investigation method
In order to answer the question of who will be using autonomous buses in the Munich Metropolitan Region in the future and what the usage behavior of these passengers will be, with a particular focus on passengers with reduced mobility, a large online survey was selected as the investigation method. This type of empirical study allows a larger number of participants to be reached with the same amount of effort than, for example, an interview study in which individual passengers are interviewed in greater detail. In a large online survey, the data obtained can also be evaluated numerically and demographic characteristics and usage behavior can be identified. Due to the larger number of participants to be surveyed, there are more participants who come from different regions of the Munich Metropolitan Region, who are of different ages, and who have different usage patterns with regard to public transport. For this reason, an online survey was conducted. (Reference Feehan and CobbFeehan & Cobb, 2019; Reference Mauz, Lippe, Allen, Schilling, Müters, Hoebel, Schmich, Wetzstein, Kamtsiuris and LangeMauz et al., 2018)
3.2. Implementation of the research method
The data on the implementation and execution of the online survey is explained in the following subchapters.
3.2.1. Research design and objectives
The conducted large-scale online survey is part of a German research project that examines the successful implementation and realization of autonomous public transport. Its goal was to produce an empirically grounded description of the demographic and behavioral profile of potential future users of autonomous buses in the Munich Metropolitan Region. Data was gathered through a structured online questionnaire combining behavioral, attitudinal, and accessibility-related items. This survey will also help answer further questions about autonomous public transport in the future.
3.2.2. Survey implementation
The online survey was administered between August and December 2024 through two complementary recruitment phases.
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1. Survey institution:
A professional survey institution recruited participants via an established online panel, targeting at least 1,000 responses from the Munich area and 500 from elsewhere in Germany. Fieldwork ran from August 19 to September 10, 2024, yielding 1,340 valid cases after cleaning.
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2. Public campaign:
Further responses were obtained via announcements distributed by the “City of Munich” (LHM) and the “Munich Transport Association” (MVV) from September 17 to December 19, 2024, producing 415 additional valid cases.
3.2.3. Data cleaning and validation
After merging and deduplication, the final dataset comprised 1,755 respondents. Duplicate submissions were removed using unique completion codes that were generated from individual personal data but cannot be traced back to the individual person.
Responses were additionally screened for logical consistency and completeness. Implausible entries were eliminated following a documented multi-step protocol which screens for implausible answers (e.g.: age = 0) and implausible answer patterns (e.g.: ABCABC…). Both datasets from the two recruitment phases underwent identical validation before merging.
3.2.4. Questionnaire structure
The online survey comprised five different thematic sections:
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1. demographics
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2. mobility resources and usage
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3. satisfaction with infrastructure
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4. mobility constraints
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5. attitudes toward autonomous vehicles (comfort, safety, reliability, environmental impact)
3.2.5. Sample characteristics and validation
First, it was checked whether the target quota of at least 75% of public transport users, and thus the correct target group, could be achieved. This target was met with a 91.9% quota of public transport users.
Most respondents (77%) resided in Bavaria, predominantly in the Munich Metropolitan Region. This is in line with the goal of focusing primarily on the Munich Metropolitan Region in order to address the specific needs of the research project for which this online survey was conducted. The remaining 23%, who do not live directly in Bavaria but come from other regions of Germany, were not excluded from the subsequent analysis, as no significant differences were observed regardless of whether they were included in the evaluation or not. In order to maintain the highest possible number of responses for a more accurate numerical analysis, these responses were retained and not excluded. This distribution is shown in Figure 1 below, which also lists other demographic data such as the gender, age, education, employment, and mobility restrictions of the respondents. This data can be used to compare the extent to which we deviate from the national average and thus identify any incorrect assumptions. However, a comparison with nationwide data shows that there are no significant deviations from this survey, which suggests that the sample is representative. (Reference Nobis and KuhnimhofNobis & Kuhnimhof, 2018)
Data from the online survey
Figure 1 Long description
Panel A: A horizontal bar graph showing gender distribution. The horizontal axis represents percentage values from 0 percent to 100 percent. The vertical axis lists gender categories: male, female, and non-binary. The bars indicate that 42 percent are male, 57 percent are female, and 1 percent are non-binary. Panel B: A horizontal bar graph showing age distribution. The horizontal axis represents percentage values from 0 percent to 100 percent. The vertical axis lists age groups: less than 20, 20 to 29, 30 to 39, 40 to 49, 50 to 59, 60 to 69, and greater than 70. The bars indicate the following percentages: 7 percent for less than 20, 22 percent for 20 to 29, 16 percent for 30 to 39, 17 percent for 40 to 49, 17 percent for 50 to 59, 13 percent for 60 to 69, and 8 percent for greater than 70. Panel C: A horizontal bar graph showing education levels. The horizontal axis represents percentage values from 0 percent to 100 percent. The vertical axis lists education categories: primary school, secondary school, high school, and no qualification. The bars indicate that 6 percent have primary school education, 29 percent have secondary school education, 62 percent have high school education, and 1 percent have no qualification. Panel D: A horizontal bar graph showing employment status. The horizontal axis represents percentage values from 0 percent to 100 percent. The vertical axis lists employment categories: full-time, part-time, retired, and other. The bars indicate that 63 percent are employed full-time, 14 percent are employed part-time, 15 percent are retired, and 8 percent fall under other categories. Panel E: A horizontal bar graph showing mobility impairment. The horizontal axis represents percentage values from 0 percent to 100 percent. The vertical axis lists categories: yes and no. The bars indicate that 12 percent have mobility impairments and 88 percent do not. Panel F: A horizontal bar graph showing geographic distribution. The horizontal axis represents percentage values from 0 percent to 100 percent. The vertical axis lists geographic categories: village (less than 5,000), small town (less than 20,000), city (less than 100,000), and metropolitan region (greater than 100,000). The bars indicate that 9 percent live in villages, 17 percent in small towns, 14 percent in cities, and 59 percent in metropolitan regions.
3.2.6. Ethical compliance
Participation was voluntary and anonymous. Informed consent was obtained in line with the General Data Protection Regulation (GDPR). No personal identifiers were collected.
4. Findings
The following chapter lists the results found in the online survey for the important categories within this paper. These are also shown in abbreviated form in the above Figure 1. However, this overview will now be discussed in more detail.
4.1. Demographic overview
The total sample comprised 1,755 valid responses, of which 1,355 were collected within the Munich Metropolitan Region. The demographic structure of the dataset broadly reflects the population distribution of the Munich Metropolitan Region, though with a slight bias toward younger and higher-educated participants, a pattern typical for online surveys. (Reference SmithSmith, 2008)
Participants ranged from 18 to 85 years old. The largest group (approximately 55%) was between 20 and 49 years old, 17% were aged 50-59, and 21% were aged 60 or older. The mean age was 44 years.
The sample was 57% female, 42% male, and 1% identifying as non-binary. The overrepresentation of women is consistent with the response trends in digital online surveys (Reference SmithSmith, 2008).
The fact that not all categories in Figure 1 add up to 100% is because the participants had the option to also provide no answer and this option is not listed separately in the diagrams.
The educational profile was distinctly high. Around 62% of respondents held a university entrance qualification (Abitur), and 29% had completed secondary school. Only a small proportion around 7% reported having a primary education or no qualification at all. This indicates an academically above average sample compared to national benchmarks. (Reference HausteinHaustein, 2024) About 63% were employed full-time, 14% part-time, 15% retired, and 8% other. Household income levels were broadly distributed, but nearly 43% reported monthly incomes between €2,000 and €4,000, corresponding to the German middle-income range. The income distribution reflects a relatively stable urban middle class. (Reference HausteinHaustein, 2024) Most respondents (≈ 68%) lived in multi-person households, while 32% lived alone. Among multi-person households, 29% included at least one child under 18 years of age.
Roughly 59% resided in large cities (over 100,000 inhabitants), 26% in small towns, and 15% in rural municipalities.
About 12% of the sample (n = 210) reported a mobility impairment, mainly walking or visual difficulties, thereby enabling a differentiated analysis of accessibility needs. This demographic data also corresponds to the nationwide average, which means that the survey was not primarily distorted by a specific user group or a situation in which one group has a disproportionately small impact on the findings Reference Nobis and Kuhnimhof(Nobis & Kuhnimhof, 2018). Within this subgroup, 58% cited walking difficulties, 18% visual limitations, and 35% other physical or sensory constraints. Compared to the general sample, respondents with disabilities were older (mean age of 56 years), less frequently employed, and had lower access to active mobility modes such as bicycles or electric scooters. Despite these barriers, public transport use among people with disabilities remained exceptionally high: 85% reported using buses or trains regularly. Their satisfaction with service reliability matched the overall sample average; however, only 42% expressed satisfaction with accessibility. This finding underlines persistent inclusion-related challenges and emphasizes the necessity of barrier-free design in future autonomous buses.
4.2. Mobility behavior
Transport mode availability demonstrates strong multimodality among Munich residents. 78% of respondents had access to at least one privately owned car in their household, while 71% owned a bicycle and 32% owned an electric bike or electric scooter. Approximately 65% held a public transport subscription, most commonly the “Deutschlandticket.” Given the wide availability of other mobility solutions among participants, it can be assumed that respondents are able to realistically answer questions about comparisons and attitudes toward various other transportation options.
Regarding modal share, 30% of respondents primarily used public transport, 24% relied on privately owned cars, and 17% was the share that favored both walking and cycling respectively. Only 3% named micromobility as their main mode. Most participants (74%) were monomodal travelers, while 22% described themselves as intermodal, combining several transport modes for daily mobility.
Travel frequency was high. About 68% of respondents traveled daily for work, education, or errands, while leisure-related trips accounted for 22% of total journeys. Over half of the participants (≈ 53%) used public transport several times per week or more. Satisfaction with Munich’s public transport system was generally high, with 66% rating it as “good” or “very good.” Strengths most frequently mentioned were reliability, frequency, and network coverage. Criticism focused primarily on accessibility deficits, particularly regarding elevators, tactile paving, and digital information systems. Among respondents with disabilities, accessibility limitations were perceived as the single most important barrier to satisfaction and future adoption of new transit technologies.
4.3. Acceptance of autonomous buses
When asked about their likelihood of using autonomous buses once introduced in Munich, 14.5% of respondents indicated they were “very likely” to use them, 48% were “somewhat likely,” and 37.5% remained “undecided” or “skeptical.” The most frequently mentioned advantages were environmental benefits (63%), reduced congestion (51%), and improved service frequency (47%). The main concerns were technical reliability (68%) and personal safety (42%).
Interest was highest among younger adults and frequent public transport users, whereas car-dependent and older groups were more hesitant. Among people with disabilities, willingness to use autonomous buses was comparable to the overall average (≈ 15% “very likely”), indicating that improved accessibility could significantly increase adoption within this group.
4.4. Differences between potential and non-potential users
To identify demographic and behavioral patterns that distinguished potential users of autonomous buses from those unlikely to adopt them, responses were grouped into the two main categories of potential users (“somewhat likely” or “very likely” to use) and non-users (“rather unlikely” or “very unlikely”).
Overall, 62.5% of participants belonged to the potential user group, while 37.5% expressed reluctance or uncertainty. Clear differences emerged between these groups in age, education, transport behavior, and attitudes.
4.4.1. Demographic differences
Potential users were significantly younger (average age of 39) than non-potential users (average age of 49). The 20-39 age cohort showed the highest openness to automated public transport. Education also correlated positively with acceptance. 65% of potential users held a university degree or entrance qualification, compared to 47% among non-potential users. Income differences were minor but indicated that mid- to upper-income respondents were somewhat more receptive.
Employment status influenced openness as well. Full-time employees and students were overrepresented among potential users, reflecting their higher daily mobility needs and potential benefit from reliable, high-frequency autonomous services.
4.4.2. Mobility behavior differences
Potential users displayed stronger integration into public transport. About 70% held a public transport subscription, compared to 52% of non-potential users. They also used buses and trains more frequently and perceived automation as an enhancement of existing mobility habits rather than a radical change.
Car ownership showed an opposite trend. 82% of non-potential users had access to a privately owned car, compared to 69% of potential users. This inverse relationship indicates that willingness to adopt autonomous buses aligns with lower car dependency and a more multimodal lifestyle.
Furthermore, 27% of potential users described themselves as intermodal travelers, while only 15% of non-potential users did so. This demonstrates a greater degree of flexibility and experimentation with new transport options among the potential user group.
4.4.3. Attitudinal and perceptual differences
Potential users tended to emphasize system-level benefits such as environmental sustainability, reduced congestion, and operational efficiency. Their responses reflect a positive perception of technological progress and trust in system performance. Non-potential users, in contrast, articulated safety-related skepticism and a general lack of trust in autonomous systems. Concerns focused on technical malfunction, cybersecurity, and the absence of a driver to ensure personal safety. Older participants in this group frequently cited discomfort with the loss of human oversight as a reason for their reluctance. Among people with disabilities, willingness mirrored that of the general population, but qualitative responses underscored that their eventual acceptance depends on visible accessibility improvements. Automation was generally supported only if it enhances, rather than complicates, the usability of public transport.
4.5. Summary
The dataset from this online survey depicts a predominantly urban, well-educated, and multimodal population with high engagement in sustainable mobility. Public transport usage is widespread and supported by extensive infrastructure. Nevertheless, barriers related to accessibility and digital inclusivity persist, particularly for elderly and mobility-impaired users. This again demonstrates that the Munich region has specific characteristics, that cannot easily be compared with other regions.
Potential adopters of autonomous buses are younger, multimodal, and technologically open, perceiving automation as a means of enhancing existing public transport services. Conversely, older and car-dependent users remain skeptical, emphasizing safety and reliability concerns, as has already been proven in many other studies. Reference Pigeon, Alauzet and Paire-Ficout(Pigeon et al., 2021) These findings highlight that the introduction of autonomous buses in Munich will require not only technological readiness but also targeted communication and inclusive design strategies to ensure broad public acceptance.
5. Discussion and limitations
In this chapter, key results will be examined in more detail and limitations will be highlighted.
5.1. Interpretation of key results
The results of this online survey provide a differentiated picture of who is most likely to become a future user of autonomous buses in the Munich Metropolitan Region. In line with international research, acceptance is strongly structured by demographic, socioeconomic, and behavioral factors.
Profile of likely future users:
The data indicates that the characteristic future user of an autonomous bus in Munich will likely be a younger adult (20-45 years), well educated, and digitally experienced, living in urban or suburban areas with good access to public transport. This user group already displays a high degree of multimodality, as they regularly combine buses, trains, and active modes such as cycling. They are motivated primarily by expectations of environmental benefits, improved service reliability, and cost efficiency, and tend to perceive autonomous buses as a technological enhancement rather than a disruption of established mobility systems.
These users are often commuters or students, rely less on privately owned cars, and are open to using digital platforms for trip planning or ticketing. Their openness reflects both a pragmatic and value-driven approach to sustainable urban mobility.
Profile of less likely users:
By contrast, individuals who are older (≥60 years), less multimodal, and more car dependent tend to exhibit lower acceptance. Their skepticism is not necessarily rooted in a rejection of automation per se, but in concerns about safety, reliability, and trust in technology. For many, the absence of an onboard driver triggers discomfort or insecurity. This group places particular value on human interaction, perceived control, and predictability, which are dimensions that must be actively addressed through transparent communication, demonstrations, and trust-building initiatives.
People with disabilities:
For people with disabilities, the data reveals a complex but encouraging picture. Despite greater reliance on public transport and frequent experience of accessibility barriers, their fundamental openness toward autonomous buses equals that of the general population. About 15% of mobility-impaired respondents declared they were very likely to use such services, while a larger share expressed conditional interest provided that barrier-free design, clear passenger information, and reliable assistance systems are ensured. The future mobility-impaired user of an autonomous scheduled-service bus can thus be described as an experienced public transport rider, typically older, and motivated by independence and inclusion rather than technological fascination. Their adoption depends primarily on whether automation visibly improves accessibility, comfort, and dignity of travel. For this group, the promise of autonomy is meaningful only if it translates into autonomous accessibility: the ability to use transport independently and seamlessly.
Integrating these user groups:
Taken together, the results portray two complementary user segments:
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1. a technologically confident early adopter group that will drive initial uptake, and
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2. a group with concerns relating to trust and inclusion, for whom acceptance hinges on user-centered design and perceived safety.
Designing future autonomous bus systems for Munich will require balancing these expectations. Technical innovation alone is insufficient; social integration and accessibility are equally crucial dimensions of success.
5.2. Methodological reflections
The survey’s large sample size and regional specificity strengthen its representativeness for the Munich context. The inclusion of detailed sociodemographic, behavioral, and accessibility variables provides a robust empirical foundation for understanding potential user segments.
Nevertheless, certain limitations must be acknowledged:
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• Self-report bias: The survey measures intentions and attitudes rather than observed behavior; actual adoption may differ once autonomous buses operate in practice.
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• Sample bias: The online recruitment process may have overrepresented digitally literate and higher educated individuals, slightly inflating openness toward automation.
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• Temporal limitation: Since data collection preceded real-world autonomous bus operations in Munich, perceptions reflect expectations rather than lived experience.
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• Regional bias: Data from regions other than the Munich metropolitan area could help to better understand the impact of socio-economic and infrastructural factors that influence the adaptation of automated systems in public transport.
Future research should address these limitations by combining revealed preference data from pilot operations with qualitative interviews to capture trust formation, adaptation processes, and user experience in real-world conditions.
5.3. Broader implications
The demographic and behavioral structure of potential users has direct implications for policy design, service planning, and communication strategies, which are discussed below.
Transport planning and service design
The demographic pattern of early adopters – young, well educated, and multimodal – suggests that initial implementation of autonomous buses should focus on dense urban corridors with strong commuter and student demand. Such environments offer both operational stability and user readiness. Nevertheless, automation must not remain confined to innovation zones. Gradual expansion toward suburban and peripheral areas is essential for preventing new accessibility gaps. Autonomous buses can strengthen first- and last-mile connectivity by linking residential districts with major transit hubs and by integrating seamlessly into the existing public transport network.
Accessibility and universal design
Accessibility is a core condition for legitimacy and long-term acceptance. The high reliance of mobility-impaired people on public transport, combined with their lower satisfaction levels, highlights the need for consistent universal design standards. Essential features include low floor entry, automatic ramps, tactile and auditory navigation aids, adaptable interior layouts, and accessible digital interfaces. Embedding such principles from the outset transforms accessibility from a regulatory requirement into a driver of social innovation.
Public perception and trust-building
Trust remains a key determinant of behavioral change. Younger users emphasize efficiency, while older and car-dependent individuals express concerns about safety and reliability. Trust can be fostered through transparent communication, demonstration projects, and opportunities to experience autonomous vehicles. Public campaigns should highlight social and environmental benefits, such as lower emissions and greater independence for people with mobility limitations, in order to frame automation as a public good rather than a purely technical innovation.
Policy and governance
Successful deployment requires coordination between municipalities, operators, and technology providers. Regulatory frameworks should establish shared evaluation standards for safety, accessibility, and user satisfaction, while promoting participatory planning and funding incentives for inclusive innovation. Sound data governance, ensuring transparency and privacy, will be critical for maintaining public trust and enabling evidence-based assessment of automated systems.
Societal and ethical dimensions
Automation presents both opportunity and risk. If implemented inclusively, it can enhance mobility equity and participation; if not, it may reinforce digital or economic divides. Policymakers should therefore treat affordability and accessibility as fundamental design parameters. The strategic goal should be to align technological progress with civic values, positioning autonomous buses as inclusive social infrastructure that supports sustainable, equitable urban mobility.
6. Summary and contribution
This study provides empirical insights into who is likely to use autonomous buses in the Munich Metropolitan Region. By integrating demographic, behavioral, and attitudinal dimensions, it outlines distinct user archetypes and highlights the coexistence of technological enthusiasm and social hesitation.
The findings indicate that the future user of an autonomous scheduled-service bus in Munich will most likely be a younger, multimodal, and environmentally conscious urban resident, already accustomed to digital and sustainable mobility services. This individual perceives automation as an opportunity for efficiency and flexibility within the public transport system. In contrast, older and mobility-impaired users are not inherently opposed to automation but require visible improvements in safety, communication, and barrier-free design to consider adoption.
The mobility-impaired future user can thus be described as a frequent public transport user whose acceptance hinges on autonomous inclusivity. They must receive assurance that technological progress directly translates into independence, comfort, and equal access. Addressing their needs is not only a matter of social justice but also a decisive factor in ensuring broad acceptance of autonomous public transport. The study contributes theoretically by demonstrating how analytical sociology and expected utility frameworks can capture the interaction between social structure and technology adoption in mobility contexts (see Reference Weyer and HoffmannWeyer & Hoffmann, 2025). Empirically, it provides a data-driven segmentation of the population that can inform targeted planning and communication strategies for autonomous bus deployment.
7. Outlook
As the research project conducted within this online survey moves toward pilot operations, these results establish a baseline for longitudinal evaluation of real-world usage and trust dynamics. Subsequent research should examine how the identified user segments evolve once autonomous buses become part of daily service. Longitudinal tracking will reveal whether initial openness translates into sustained adoption and how trust develops through direct experience.
Future work should also expand the analysis beyond Munich, enabling comparative studies across multiple research projects to assess how cultural, infrastructure-related, and regulatory factors influence acceptance. From a policy perspective, the next phase of research and development should focus on:
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• Implementing universal accessibility standards as a precondition for operation
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• Developing uniform strategies for communication between passengers and autonomous buses
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• Establishing feedback channels for continuous user participation in system design
Ultimately, the successful introduction of autonomous buses in Munich will depend not only on technological maturity but on social readiness: the ability of all population groups, regardless of age or ability, to trust and benefit from automation. By aligning innovation with inclusivity, autonomous public transport can evolve into a cornerstone of sustainable, equitable, and resilient urban mobility.
Acknowledgement
This research was funded by the Federal Ministry of Transport of Germany (BMV) on the basis of a decision of the German Bundestag with roundabout 12.7 million euros as part of the project MINGA in the framework of the funding guideline “autonomous and connected driving in public transports.”
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