• The factors that cause transfer inconvenience were identified. • A Structural Equation Model is developed based on attitude and behaviour data. • Transfer inconvenience is mostly influenced by physical infrastructure. • Transfer inconvenience has a substantial influence on behaviour. • Transfer inconvenience perception can be a tool to evaluate integration. Public transport and bicycles are two essential components of a sustainable urban transport system, with their integration increasing their potential to reduce car use in urban areas. However, their combined use has one essential weakness, which is the transfer between two travel modes. This additional step creates a feeling of inconvenience to potential travellers. Understanding the factors that cause inconvenience allows to implement policies and measures that ensure an efficient integration and consequently an effective transition to more sustainable travel options. However, the factors that cause inconvenience are currently understudied. This study fills this knowledge gap by identifying these factors and measuring their effect on transfer inconvenience. It also explores the influence of perceived transfer inconvenience on the choice to use a bike as an access or egress mode to a public transport stop. Using data (n = 1007) from an online survey questionnaire from the region of Nord-Jæren in Norway, a Structural Equation Model (SEM) has been developed. The results of the analysis show that the perception of transfer inconvenience plays a substantial role in the choice to combine bicycles and public transport since it can predict up to 10 % of the variance in the choice to use bicycles. The results also suggest that practical elements, like physical infrastructure, play a vital role in shaping transfer inconvenience.

Public transportation (PT) plays a crucial role in catering to the mobility needs of school students. However, the disparities of PT travel characteristics among different socioeconomic groups have been underexplored. In this study, we explore disparities in PT usage, after-school activity participation, journey length, and on-board crowding among different socioeconomic groups as well as between different educational stages (primary, secondary). We specifically focus on home-to-school and school-to-activity PT journeys utilizing automated data sources (smart card data). Results from the case study of Region Stockholm show that travel time and crowding exposure vary across the case study area. Specifically, school students coming from areas with higher income levels, higher shares of cooperative housing, or lower vehicle ownership tend to need less time to travel to school. In terms of student categorization, secondary students with diverse socioeconomic backgrounds tend to travel longer to school compared to primary students. Concerning journeys to after-school activities using PT, results reveal that school students from areas with high vehicle ownership and education or lower employment levels, as well as students from suburban/rural areas, have lower odds of using PT. The findings can assist policy makers and PT agencies in designing more equitable and youth-friendly PT systems, improving access to schools and to after-school activity locations. • Social disparities in public transportation use among school students are assessed. • Home-to-school and school-to-activity journeys are analyzed. • Students with a higher socioeconomic profile need less time to travel to school. • Vehicle ownership reduces after-school participation using public transportation. • Results may assist in youth-friendly design of public transportation networks.

Evaluating Perceived Fairness in public transit: A choice model investigation of Chicago riders

Personalised incentives for demand management of congested public transport systems: A reverse-engineering approach and application

With the introduction of highly automated vehicles (HAV; SAE Level 4) driverless mobility systems may fundamentally change public transportation. For potential passengers of these systems, new situations may arise, leaving them without a driver to directly communicate with. This can be problematic when the driving automation is confronted with situations it might be unable to manage by itself, which would cause a minimal risk maneuver (MRM), e.g. a complete standstill of the vehicle. Some concepts enhance these systems with a remote assistant who can support the HAV, adding novelty to the transportation process. This increased novelty may result in user discomfort for passengers, causing a need for information systems that address this. One possibility to do so and to thus improve hedonic quality and thus comfort for passengers is by providing system transparency via a media rich in-vehicle Human-Machine Interface (iHMI), which provides information to passengers about the ADS and its processes. However, there is still a gap in research on the ideal communication mechanism to ensure good hedonic quality and comfort during these situations. We conducted a simulator study in virtual reality (VR), investigating interfaces based on media richness theory to address this issue. The interface provided a multi-layered iHMI consisting of transparency information presented with varying levels of media richness. In a block design, participants experienced three versions of an iHMI, one presenting information via text, another combining text and auditive presentation and a third adding a human avatar simulating Face-to-Face communication. After each scenario, participants completed questionnaires regarding understanding, predictability, trust and user experience. Study results reveal significant increases in the hedonic quality of user experience and trust towards the system for interfaces with increased media richness, providing a first step towards enhancing user comfort with iHMI in automated mobility systems during challenging situations for the automation system, such as MRM.

Public transport subsidies and the marginal cost of public funds: An interpretative review

Competitive or complementary role of bike-sharing to public transport: Budapest case study

Psychological determinants of transport mode choice: A comprehensive dataset from Pabna Municipality, Bangladesh.

Causal impacts of demand responsive transit on public transit demand: A spatial assessment framework

Optimization of carriage parking based on simulation of passenger dynamics in the dynamic autonomous non-stop rail transit system

ABSTRACT Climate change is a ‘super wicked problem’ intricately linked to social inequality, with the most marginalised populations both contributing least to emissions and bearing the brunt of climate impacts. While these groups arguably have the most to gain from climate action, many climate policies risk exacerbating inequality, particularly when they place a disproportionate burden on low-income communities. This paper explores the tension between climate mitigation and social equity, with a particular focus on sustainable transport. Drawing on qualitative case study data from a pan-European project that examined the impact of car-reduction policies on socioeconomically disadvantaged urban neighbourhoods, the analysis focuses on a policy aimed at improving the public transport system in a Swedish city. Despite focusing on a seemingly equitable ‘pull’ measure, significant citizen resistance emerged. Through interviews with residents and policymakers, this paper examines how perceptions of justice shaped perceptions of the policy. Applying a mobility justice perspective to the findings reveals a disconnect between policymakers’ assumptions and residents’ lived experiences – highlighting the importance of procedural justice and meaningful public participation and underscoring the complexities of designing climate policies that are both environmentally effective and socially just.

ABSTRACT Promoting healthy and sustainable cities through urban design and planning is essential to improving the urban environment and overall population well-being. The Global Healthy and Sustainable City Indicators (GHSCI) initiative is a collaborative effort that provides open-source tools to support the development of spatial indicators for urban health promotion. This study used GHSCI software to analyze spatial indicator data for the Jakarta region. The collected data were evaluated and compared with the median values of the 25 initial cities from GHSCI’s first collaboration project. The research findings highlight Jakarta’s comparative position among other cities within the GHSCI framework. An analysis of GHSCI reveals both strengths and weaknesses in Jakarta’s efforts to achieve a healthy and sustainable urban status. Key findings include limited access to fresh food markets; high intersection density in most neighborhoods, which may serve as a potential marker for promoting walkability; and widespread access to public transportation, with the majority of residents living within proximity to bus stops. While these indicators provide valuable information, this study suggests that future refinements of GHSCI could benefit from expanded indicators that incorporate lessons from recent public health crises to better predict urban health.

ABSTRACT This study contributes empirical evidence on competition among Autonomous Air Taxis (AATs), Autonomous Taxis (ATs), and Autonomous Buses (ABs). An offline survey including a stated choice (SC) experiment and personal attitude statements was conducted. Observations from 561 residents of Shanghai, China were collected. A latent class hybrid choice model was developed and estimated. The estimation results show that personal attitudes and heterogeneous preferences significantly affect the acceptance of the AV modes. Approximately 80.13% of respondents with low incomes and unstable employment are highly sensitive to the costs and travel times of ATs and AATs. About 19.87% of respondents who have high incomes and stable work are easily influenced by choices of others in their social networks. Moreover, choice preferences for AVs are significantly influenced by personal attitudes toward AVs, such as perceived safety of autonomous taxis and a dislike of public transportation.

Modeling heterogeneity in fault attribution of Pedestrian-Vehicle crashes using a Random parameter Binary Logit approach.

Passenger perception of vehicle occupancy in public transport and factors that shape crowding estimations.

ABSTRACT Optimizing feeder characteristics is crucial for improving public transit efficiency. This study focused on optimizing feeder vehicle headway and capacity for a fixed route, formulating a non-linear mixed-integer optimization problem, aiming to minimize the total cost, including operator and user cost. Three distinct headway strategies: optimal, capacity, and policy headways are considered. A heuristic optimization algorithm, combining an analytical gradient-based method with a brute-force search, is developed to obtain optimal solutions. The model is applied to two feeder routes serving the Ratmalana and Agulana railway stations, Sri Lanka, using observed on peak hour transfer demand. Experimental results indicate that smaller vehicles with shorter headways are favoured for low- to medium demand situations. The sensitivity analysis reveals that as demand increases, smaller vehicles become less suitable, necessitating a shift to higher-capacity vehicles. The corresponding vehicle sizes with headways are presented, demonstrating this transition across different demand levels.

ABSTRACT This study investigates the factors influencing public transport acceptance in Egyptian cities using advanced machine learning techniques, including Optimal Regression Forest (ORF) and Variance-Based Sensitivity Analysis (VBSA). We analyze data from a survey of 2,511 respondents using a stated preference experiment to identify key drivers of acceptance and quantify their impact. VBSA was applied, quantifying both direct and interaction effects of fourteen explanatory variables. The results show that socioeconomic variables, perceived benefits, and service reliability are the most influential factors, with significant implications for policymakers seeking to improve public transport adoption. By integrating ORF and VBSA, our model achieves strong predictive performance, providing actionable insights for enhancing transport policy and service design. These findings contribute to the literature on public transport planning and offer practical recommendations for urban planners and policymakers in Egypt and in other Middle East and North Africa (MENA) cities with comparable socioeconomic and institutional conditions.

Abstract The current U.S. federal administration has sought to intervene into every aspect of academic life, university functioning, and the research enterprise including by attacking academic freedom and integrity and canceling and retreating from publicly funded research. Such actions have profound adverse effects on the U.S. public, especially its most marginalized communities, and on science, itself. This perspective provides a telling example of such impacts through our own experience of funding cancellation, the disruptions it causes and the effects it has on urban systems and the communities they support. By focusing on our project that sought to center environmental justice communities in urban transportation and climate planning we offer insights into the wide-ranging effects of such disinvestment, including on sustainability and air quality efforts, with recommendations for moving forward to advance sustainable, equitable, and resilient cities.

ABSTRACT Nowadays integration of Generative artificial intelligence (GenAI) bots in public transportation systems offers convenience and efficiency to commuters. However, data breaches pose privacy-security risks that impact commuters’ trust and intention to continue using. Studies have examined the factors that lead to data breach risks in AI bots, but the impact on the anthropomorphic GenAI bots is still unexplored. Drawing on the theory of planned behavior, we examine how data breaches on anthropomorphic GenAI can impact the commuter’s intention toward their usage. Mixed-methods approach combining partial square structural equation modeling (PLS-SEM) with Artificial neural network (ANN) and interviews in a data breach environment is employed. The findings revealed that data breaches significantly influence commuters’ usage intention, moderated by trust. This study has theoretical and practical implications, highlighting the need to prioritize robust security measures and trust-building strategies to ensure commuter retention and satisfaction under data breaches.

The transition to electric buses involves a shift in public transport systems, requiring changes to the role of public transport authorities (PTAs). This study analyses how PTAs' role as planners and procurers of public transport is impacted by electrification, employing a sustainability transition perspective. Through thematic analysis of semi-structured interviews with 25 interviewees involved in eight public procurements, both qualitative and quantitative changes were identified. The findings show that the organisational model influences how electrification changes the role and responsibilities of PTAs, as new technology alters established procurement logics and the reinterpretation of procurement principles. Additionally, this transition necessitates changes in long-term strategic planning, including adjustments in ownership structures and the distribution of responsibilities. The emergence and assessment of new questions and phenomena, for example, regarding battery production and sustainability, further change the role of the PTA. The rapid pace of this technological change challenges PTAs' ability to proactively manage developments, creating a dynamic where PTAs both take on new responsibilities and react to market changes. This study highlights the emerging tensions between PTAs' procurement principles and long-term planning objectives, calling attention to the need for a balanced approach to manage their evolving assignments effectively in the face of new technology.