The operation of automated buses (ABs) as a feeder transport service has been advocated as one of the most suitable ways to integrate shared automated vehicles into the current public transportation system. Previous studies based on pilot demonstrations have explored public attitudes and perceptions toward ABs, but most of those studies have relied on cross-sectional data, which cannot capture changes in attitudes over time. To address this gap, a one-year, three-wave panel survey was conducted in Stockholm, Sweden, with a focus on passengers’ real-world experiences in relation to an automated bus operating in mixed traffic on public roads. Latent curve analysis was applied to the longitudinal data to examine the evolution of service evaluations. Although the findings indicated that passengers initially rated speed and travel time unsatisfactorily, these evaluations showed steady improvement over time. In contrast, perceptions of safety, comfort, and convenience were positive at the outset but underwent a moderate decline throughout the study period. Current public transport users and tech-savvy individuals were identified as the primary user groups most likely to adopt ABs in the future.

Transport policy research has been developed systematically since the second half of the 20th century, when urban planning and transportation systems became critical components of economic development and urbanization. The field has evolved through a wealth of diverse publications and scholarly contributions. Our comprehensive guide synthesizes the state of the art in 20 major categories of transport policy research, identified based on a data-driven categorization. Each category is represented by a leading expert in the field, providing a rigorous assessment of the state of the art on an area together with an identification of challenges to inform future research and policymaking. Aggregated analysis reveals six major trends in policy research, namely: Adaptability, Network Integration, Sustainability, Wireless Connectivity, Equity, and Resilience — combined to form the word ANSWER, with each component being crucial to understanding and addressing the field's multifaceted challenges. Our guide aims to provide answers to pressing challenges and serve as an essential, accessible reference regarding transport policy research for academics, policymakers, and industry stakeholders alike.

To help automated bus services to be competitive in the market, understanding what factors influence the public's acceptance and adoption of an automated bus service and how these factors change over time is critical. Various factors affect users’ acceptance of this new bus mode, with the quality of service standing out as a significant consideration. Based on pilot demonstrations, some prior studies have explored the factors influencing the user acceptance of new automated vehicle technology based on real-life riding experience. However, these studies are restricted to predicting the adoption of an automated bus by utilising cross-sectional data, but with no data to explore whether public attitudes and acceptance would change over time. To fill the research gap, a longitudinal survey was conducted. Using the panel data, the present study focuses on users with real-world riding experiences on automated buses operated in a mixed-traffic environment on public roads in Stockholm. Contributing to the longitudinal analysis of the public's acceptance of automated buses, we develop a novel conceptual model integrating the service quality and the technology acceptance model (TAM). A dynamic structural equation model is employed to explore the changes in judging criteria regarding service adoption among adopters and non-adopters. The findings indicate that comfort and convenience are the most significant determinants of satisfaction and the perception of usefulness, which, in turn, positively affect people's adoption intentions, as well as encouraging favourable word-of-mouth behaviour. It is expected that the provision of faster, safer, more comfortable and convenient riding experiences with automated buses will eventually increase the use of these buses, as well as improve word-of-mouth communication.

While mobility and safety of drivers are challenged by behavioral changes, the increasingly complex road environment has placed a higher demand on their adaptability. The ultimate goal of this paper was to identify the impact that the balance between task complexity and coping capacity had on crash risk. Towards that aim, an integrated model for understanding the effect of the inter-relationship of task complexity and coping capacity with risk was developed. A vast library of data from a naturalistic driving experiment was created in three countries (i.e., Belgium, UK and Germany) to investigate the most prominent driving behavior indicators available, including speeding, headway, overtaking, duration, distance and harsh events. In order to fulfil the aforementioned objectives, exploratory analysis, such as Generalized Linear Models (GLMs) were developed, and the most appropriate variables associated to the latent variable “task complexity” and “coping capacity” were estimated from the various indicators. Additionally, Structural Equation Models (SEMs) were used to explore how the model variables were inter-related, allowing for both direct and indirect relationships to be modelled. The analyses revealed that higher task complexity levels lead to higher coping capacity by drivers. Additionally, the effect of task complexity on risk was greater than the impact of coping capacity in Belgium and Germany, while mixed results were observed in the UK.

The i-DREAMS project focuses on establishing a framework known as the ‘Safety Tolerance Zone (STZ)’ to ensure drivers operate within safe boundaries. This study compares Long-Short-Term-Memory Networks and shallow Neural Networks to assess participants’ safety levels during i-DREAMS on-road trials. Thirty German drivers’ trips and Forty-Three Belgian drivers were analyzed using these methods, revealing factors contributing to risky behavior. Results indicate i-DREAMS interventions significantly enhance driving behavior, with Neural Networks displaying superior performance among the algorithms considered.

Automation technology has received great attention recently and been expected to play an important role in future transport systems. Understanding determinants of travellers' intention to use the bus and recommend the service to others is critical to promote such new travel modes. Although previous studies have focused on plausible scenarios based on hypothetical behavioural reactions, predicted adoption intention and recommendation behaviour may not be consistent with those exhibited when the users are exposed to the real automated bus service. Using a panel survey conducted in Stockholm, where an automated bus services is currently operating in the mixed traffic environment on the public road, this study addresses this research gap by investigating public acceptance and usage of these automated buses. Applying a behavioural model, this study explores key influencing factors of automated bus use and word of mouth intention. The results indicate that users' needs and travel demands have a strong power to influence people's intentions to use such a new public travel mode and recommend the service to others. Furthermore, individuals' behaviour intention is found to have no significant effect on actual behaviour in this study, which suggests a gap between intention and action.

Using vehicle trajectory datasets developed over a study section for three traffic flow levels, a rectangular hyperbolic relation between time-to-collision and relative speeds in vehicle-following behavior were observed. A new methodology for estimating the probable rear-end collisions in the given traffic stream is developed based on this relation. The vehicle-following behavior is examined in terms of the hysteresis phenomenon concerning the distance gap (DG) and relative speed (RS). Further, based on the follower's attention toward towards the leader vehicle, a novel surrogate safety measure, called Instantaneous Heeding Time (IHT), was conceptualized. This measure represents the time gap available based on the positions of the leader and follower vehicles. After exploring vehicle-following behavior, IHT, DG, and RS were used to estimate the rear-end collision probability. The applicability of the proposed methodology is tested using different thresholds (IHT, DG, and RS) and applied to the study section at the three traffic flow levels.

The sustainable and continuous development of public transport systems is crucial to ensuring robust and resilient transport and economic activity whilst improving the urban environment. Through technological improvement, cities can increase the competitiveness of public transport, promote equality and pursue a multi-modal shift to greener solutions. The introduction of vehicle automation technology into existing public transport systems has potential impacts on mobility behaviours and may replace conventional bus service in the future. This study examines travellers’ preferences for automated buses versus conventional buses, using a context-dependent stated choice experiment. This experiment measured the effects of context variables (such as trip purpose, travel distance, time of day, weather conditions and travel companion) on the choice of automated buses versus conventional buses. The results were analysed using mixed logit models, and the findings indicate that, in general, choice behaviours do not diverge much between the choice of automated bus and conventional bus. However, individuals’ choices are more elastic towards the changes in automated bus service levels compared to conventional bus service. The results show that poor weather conditions may lower the quality and reliability of public transport service, and the probability of choosing an automated bus over a conventional bus is reduced due to such disruptions. In addition, passengers travelling for work purposes, covering long distances, or travelling with companions are more likely to choose conventional buses than automated buses.

Traffic conflicts are heavily correlated with traffic collisions and may provide insightful information on the failure mechanism and factors that contribute more towards a collision. Although proactive traffic management systems have been supported heavily in the research community, and autonomous vehicles (AVs) are soon to become a reality, analyses are concentrated on very specific environments using aggregated data. This study aims at investigating –for the first time- rear-end conflict frequency in an urban network level using vehicle-to-vehicle interactions and at correlating frequency with the corresponding network traffic state. The Time-To-Collision (TTC) and Deceleration Rate to Avoid Crash (DRAC) metrics are utilized to estimate conflict frequency on the current network situation, as well as on scenarios including AV characteristics. Three critical conflict points are defined, according to TTC and DRAC thresholds. After extracting conflicts, data are fitted into Zero-inflated and also traditional Negative Binomial models, as well as quasi-Poisson models, while controlling for endogeneity, in order to investigate contributory factors of conflict frequency. Results demonstrate that conflict counts are significantly higher in congested traffic and that high variations in speed increase conflicts. Nevertheless, a comparison with simulated AV traffic and the use of more surrogate safety indicators could provide more insight into the relationship between traffic state and traffic conflicts in the near future.

The rapid development of automated buses holds great potential for the development of transportation systems. As research into innovative forms of automated transportation systems gains momentum, it is important to understand the public's perceptions of such public transport systems. Previous studies have contributed based on hypothetical scenarios, but not based on real observations. Based on an online survey in Stockholm in March 2019, the current research addresses this gap by investigating the public's perceptions from a real, fully operational, automated public transportation service operated in a mixed traffic environment on public roads. The respondents were selected along the automated bus line in Barkabystaden, Stockholm. Our findings indicate that (1) The presence of onboard operators has a positive impact on respondents' perceived safety, (2) People who have not taken automated buses before have a more negative perception of driving speed of the bus service than people who have taken the buses before, (3) Attitudinal factors, such as public perceptions of safety, driving speed, reliability, and convenience, have a significant influence on the acceptance of the new bus system, (4) As an emerging and innovative transportation mode, automated buses are expected to attract a high share of regular public transportation mode users and the younger generations in the future, (5) Social-demographic characteristics such as gender and income had no significant impacts on the adoption of the new technology. The results provide the characteristics of early bus adopters and their travel behavior and help to prioritize possible investments and allow the policymakers and private industries to identify the special needs of users.

In this study, the possible causal links between the built environment, travel attitudes and travel behaviour of people that have moved between totally different urban and transportation contexts were investigated. A quasi-longitudinal design was developed to collect data about the perceived neighbourhood characteristics, neighbourhood preferences, travel attitudes and changes in car, walking and bicycle usage of 51 Greeks who relocated from the US to Greece. Variable reduction techniques were applied prior to developing our models because of the small sample size. The results of this study offer support for a causal relationship between the built environment and travel behaviour identified in the existing literature. For instance, longer travel time to city center was associated with higher car use, higher density neighbourhoods and increased accessibility to local amenities were associated with increased bike use and better access to a district shopping center was associated with more walking after relocation. Our results also showed that in contexts like Greece, where transport infrastructures are not adequately developed, lack of safe bike conditions and easy access to public transportation are important determinants of bicycle use and walking. Thus, according to our results, promoting sustainable mobility in contexts like Greece would require not only enhancing accessibility through relocation of activities, but also by improving infrastructures for public transport, bicycle and walking.

This paper presents analyses of how the economic recession that started in 2008 has influenced the number of traffic fatalities in OECD countries. Previous studies of the relationship between economic recessions and changes in the number of traffic fatalities are reviewed. Based on these studies, a causal diagram of the relationship between changes of the business cycle and changes in the number of traffic fatalities is proposed. This causal model is tested empirically by means of multivariate analyses and analyses of accident statistics for Great Britain and Sweden. Economic recession, as indicated both by slower growth of, or decline of gross national product, and by increased unemployment is associated with an accelerated decline in the number of traffic fatalities, i.e. a larger decline than the long-term trend that is normal in OECD countries. The principal mechanisms bringing this about are a disproportionate reduction of driving among high-risk drivers, in particular young drivers and a reduction of fatality rate per kilometre of travel, probably attributable to changes in road user behaviour that are only partly observable. The total number of vehicle kilometres of travel did not change very much as a result of the recession. The paper is based on an ITF-report that presents the analyses in greater detail.