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As connected, electric, and autonomous vehicle (AV) services are developed for cities, the research is conclusive that the use of these services must be shared to achieve maximum efficiency. Yet, few agencies have prioritised designing an AV system that focuses on dynamic ridepooling, and there remains a gap in the understanding of what makes people willing to share their rides. However, in 2017, the Australian transport authority Transport for New South Wales launched over a dozen trials for on-demand, shared public transport, including AVs. In this paper, we investigate the user willingness-to-share, based on experiences from one of these trials. Four focus groups (19 participants in total) were held in New South Wales with active users of either the trialled on-demand dynamic ridepooling service (Keoride) or commercial ridepooling (UberPool). Through thematic analysis of the focus group conversations, the cost, comfort, convenience, safety, community culture, and trust in authority emerged as factors that influenced the willingness-to-share. When presented with driverless scenarios, the focus group participants had significant concerns about the unknown behaviour of their co-passengers, revealing sharing anxiety as a significant barrier to the adoption of shared AVs. This paper identifies previously disregarded factors that influence the adoption of AVs and dynamic ridepooling and offers insights on how potential users’ sharing anxiety can be mitigated.

Urban rail transit (URT) systems are critical to modern public transportation services. Unfortunately, disruptions in URT systems can lead to dysfunction and threaten sustainable development. This study analyses URT network sustainability from a vulnerability perspective. Two network attack scenarios, including random attacks and intentional attacks, are designed to assess different kinds of disruptions to URT networks. Under random attacks, nodes are randomly removed from the network. In contrast, under intentional attacks, key nodes are identified and removed based on topological metrics and passenger flow volume. Then, URT network vulnerability is evaluated by quantifying the changes in network efficiency and structural integrity under the network attacks from a spatio-temporal point of view. The real-world case of the Shanghai URT system from 1993 to 2020 is used to illustrate the vulnerability in the evolution of the URT system. The results indicate that the URT network is increasingly fault-tolerant and structurally robust over time. The URT network is more vulnerable to intentional attacks than to random failures. Additionally, there are significant spatial differences in the vulnerability of Shanghai URT network. Stations in the central activity zone (CAZ) are more fault-tolerant and robust than stations located outside of the CAZ. Furthermore, stations with large centrality and greater passenger flow volumes and lines with many key nodes and greater passenger flow volumes, are vulnerable to disruptions in the URT networks. This study provides a new index to comprehensively quantify node centrality; it also fills a research gap by analysing the vulnerability of URT networks based on both longitudinal and spatial patterns. Finally, this paper highlights significant practical implications for the sustainable development of URT networks, as well as the sustainable development of public transportation services.

In the context of global suburbanization and population aging, a low-speed, automated vehicle (LSAV) system provides essential mobility services in suburban residential areas. Although extensive studies on shared autonomous vehicle (SAV) services have been conducted, quantitative investigations on the operation of suburban LSAV systems are limited. Based on a demonstration pilot project of an autonomous vehicle called “Slocal Automated Driving”, we investigated the performance of an SAV system considering several scenarios in Kozoji Newtown, a suburban commuter town in Japan. The agent-based simulation results revealed that 40 LSAVs can satisfy the demands of 2263 daily trips with an average wait time of 15 min. However, in the case of a high-speed scenario, the same fleet size improved the level of service (LOS) by reducing the average wait time to two and a half minutes and halving the in-vehicle time. By contrast, the wait time in terms of the average and 95th percentile of the no-sharing ride scenario drastically deteriorated to an unacceptable level. Based on the fluctuations of hourly share rates, wait times, and the number of vacant vehicles, we determined that preparing for the potential fleet insufficiency periods from 7:00–13:00 and 15:00–18:00 can improve the LOS.

The main interest of this paper is to analyze the mobility acceptance factors of an automated shuttle bus last-mile service. There is limited research on the passengers’ perception of security and safety of automated mobility, whereas prior research is mostly based on surveys interested in attitudes towards self-driving vehicles, without being linked to the experience. We, on the other hand, are interested in passengers’ feeling of security and safety, after taking a ride with an automated shuttle in an open urban environment. For studying this, we conducted an automated shuttle bus last-mile pilot during a four-month period in the city of Tallinn in late 2019. The method is a case study focusing on one city with several tools for data collection applied (surveys, interviews, document analysis). The pilot, open and free for everybody, attracted approximately 4000 passengers, out of which 4% responded to the online feedback survey. For studying the operational capacity, we had a panel interview with operators of the shuttle service, in addition to analyzing daily operational log files. The results indicate that passengers’ perceived feeling of security and safety onboard was remarkably high, after taking a ride (and lower without a ride, in a different control group). The bus was operated only if operational capacity was secured, thus having significant downtime in service due to environment, technology and traffic-related factors.

Understanding the nature of consumers’ environmental behaviors will help design better environmental policies for a sustainable future. Drawing on the responsible environmental behavior (REB) theoretical framework, we disentangle the effects of social and psychological environmental factors on Europeans’ behaviors, considering that living contexts vary from country to country. Using data on attitudes to the environment sourced from the 2017 Eurobarometer, we measure the socio-psychological factors and environmental behaviors using exploratory factor analysis. A multilevel model measures the effect of individual-level environmental factors and analyzes the impact of the country context on Europeans’ environmental behaviors. Results show that the three tested environmental behaviors (eco-friendly purchasing, public transport use, and reduced resource consumption) are explained by individual-level environmental factors as well as by country differences, but the effects differ depending on the behavior considered. We also find that the effects of knowledge, attitudes, and perceived behavioral control are mediated by a set of social indicators (age, gender, education, and income). We conclude with a discussion of the implications for policymakers.

Transport in cities is one of the most important sources of emissions. Electromobility is an essential element in the catalogue of activities of local authorities aimed at combating climate change. Over the years trolleybus transport has been characterised by both phases of development and regression and is still an essential component of zero-emission urban transport in about 300 cities worldwide. The development of electricity storage technology, especially in the form of a battery, has opened up new prospects for this mode of transport. A trolleybus equipped with a battery (in-motion charging technology) gains unique characteristics for operation independent of the catenary. This study presents the approach for assessing the development of in-motion charging for trolleybuses in all Polish cities operating this means of transport. A set of KPIs has therefore, been set and analysed. The analysis covers a comparison between 2014 and 2019, aimed at showing the development of technological innovations in this field. The results clearly show that in-motion charging technology leads to the development of trolleybus transport, although this development has mainly a qualitative dimension. A key factor determining the development of trolleybus transport using in-motion charging technology is progress in the development of traction batteries.

Urban transportation issues continue to emerge and evolve as a result of rapid urbanization, and the systematic and scientific assessment of urban mobility is becoming increasingly essential. In this work, a Pressure-State-Response (PSR) model with 25 indicators was established to reflect the status of urban mobility. Then, the importance of indicators was determined with the interval-valued intuitionistic fuzzy analytic hierarchy process (IVIF-AHP) method, and the fuzzy comprehensive evaluation (FCE) method was applied to assess the overall status of urban mobility. The validity of the proposed model was demonstrated using the mobility system of Beijing as a case study, and the pressure, state, and response scores were calculated. The proposed assessment model can help to improve urban transportation monitoring and can also provide a scientific foundation for future urban transportation policymaking, planning, and traffic management, thereby further ensuring the sustainable development of urban transportation systems.

Understanding commuters’ behavior and influencing factors becomes more and more important every day. With the steady increase of the number of commuters, commuter traffic becomes a major bottleneck for many cities. Commuter behavior consequently plays an increasingly important role in city and transport planning and policy making. Although prior studies investigated a variety of potential factors influencing commuting decisions, most of them are constrained by the data scale in terms of limited time duration, space and number of commuters under investigation, largely owing to their dependence on questionnaires or survey panel data; as such only small sets of features can be explored and no predictions of commuter numbers have been made, to the best of our knowledge. To fill this gap, we collected inter-city commuting data in Germany between 1994 and 2018, and, along with other data sources, analyzed the influence of GDP, housing and the labor market on the decision to commute. Our analysis suggests that the access to employment opportunities, housing price, income and the distribution of the location’s industry sectors are important factors in commuting decisions. In addition, different age, gender and income groups have different commuting patterns. We employed several machine learning algorithms to predict the commuter number using the identified related features with reasonably good accuracy.