Mass transit systems in many cities are facing growing congestion. One widely studied approach to alleviating this congestion involves solving the schedule-based transit assignment problem (STAP). STAP employs a joint choice model of departure times and routes, defining a spatial–temporal path wherein passengers decide both when to depart and which route to take. User equilibrium (UE) models for the STAP indicate the current congestion cost, while system optimum (SO) models can provide insights for congestion relief directions. However, most existing STAP studies under the SO condition rely on approximate SO (Approx. SO) models, which underestimate the potential for congestion reduction in the system. The few studies in STAP that compute exact SO solutions ignore realistic constraints such as hard capacity, multi-line networks, and spatial–temporal competing demand flows. This paper proposes an exact SO method for the STAP that overcomes these limitations. We apply our approach to a case study involving part of the Hong Kong Mass Transit Railway network, which includes 5 lines, 12 interacting origin–destination (OD) pairs and 52,717 passengers. Computing an Approx. SO solution for this system indicates a modest potential for congestion reduction measures, with a cost reduction of 17.39% from the UE solution. Our exact SO solution is 36.35% lower than the UE solution, which is more than double the potential for congestion reduction. We then show how the exact SO solution can be used to identify: (i) which OD pairs have the most potential to reduce congestion; (ii) how many passengers can be reasonably shifted; (iii) future systems’ potential with increasing demand and expanding network capacity.
This study assesses the long-term effects of fare-free transit (FFT) policies on ridership, at both the agency and neighborhood levels. Using a Fixed Effects model with Instrumental Variables on data from 125 U.S. transit agencies (2011–2023), we found that partial-route and full FFT policies increased agency-level ridership by approximately 17% and 45%, respectively. Then, having employed a Spatial Durbin model with Fixed Effects on bus data from Boston, MA, USA (2021–2023), we found that neighborhoods served by FFT routes saw 26% more boardings than those served by non-FFT routes. The impact of FFT varied by time of day, with smaller effects observed during peak hours, evenings, and weekends. By distinguishing between types of FFT policies (i.e., full, partial-population, or partial-route) and their spatial and temporal effects, this study offers actionable insights for transit agencies considering FFT. These insights are particularly relevant for agencies aiming to maximize ridership growth while balancing other factors, such as service coverage, serving population, and budget constraints.
Public transport provides well-documented health, environmental, and economic benefits by promoting active travel, reducing car dependence, and enabling efficient land use. However, accessibility analyses often rely on measures defined at static spatial locations (e.g., around residential or zonal anchors), which may not capture the dynamic and context-dependent nature of accessibility across daily mobility. Incorporating accessibility across daily activity locations allows for a more behaviourally grounded assessment, reflecting the spatial and contextual conditions under which travel decisions are made. This study examines how accessibility to bus stops, measured at home, trip origin, and destination, relates to bus ridership in Auckland, New Zealand. Using high-resolution GPS and accelerometer data from 108 participants (835 trips), we applied mixed-effects logistic regression models and tested work status and neighbourhood residency duration as moderators. Results revealed an unexpected pattern: higher accessibility to bus stops around home (home-based accessibility) was negatively associated with bus ridership. Accessibility to bus stops around trip destinations (destination-based accessibility) showed a positive association with ridership, but only among newer residents. Employed participants were more sensitive to home-based accessibility than those who were unemployed. These findings suggest that accessibility–ridership relationships vary across key locations encountered along individual travel trajectories, likely indicating that measures defined at fixed, static locations may provide an incomplete representation of travel behaviour. Trip-specific, dynamic approaches may therefore offer a more behaviourally grounded basis for informing transport planning and policy.
This paper investigates the factors affecting commuting mode choice, with a particular focus on the consideration and choice of aerial cable cars (ACC). It also examines the impact of transport-related social exclusion (TRSE) on mode choice, understood as the limited participation in society due to a lack of transport options. The case study is situated in Manizales, Colombia, a medium-sized city renowned for pioneering ACC as a transit mode and distinguished by its steep topography and residential development in hilly terrain, thereby providing a unique context for examining the impact of ACC on mobility. Data were collected through a questionnaire survey that inquired about daily mobility, including the main mode of transport, origin and destination, available transport options, and perceptions of transport barriers, with consideration of TRSE theoretical dimensions. The subjective perceptions were analysed using Confirmatory Factor Analysis (CFA) to extract latent factors, which were then used as explanatory variables in a discrete choice model of commuting, along with observed variables related to individual, household, and built-environment characteristics. The results of the CFA show that all items significantly loaded on the theoretical factors, including a combined latent variable for time- and money-based exclusion. A Nested Logit model was estimated, considering the correlation between bus and ACC as public transport. The results indicate that females are less likely to choose the ACC, and the presence of cars among workers also reduces the likelihood of using the ACC. Regarding the TRSE dimensions, we found that all dimensions have the potential to explain mode choice to some extent, with distinct influences considering the mode. For instance, a high perception of physical-based exclusion, related to the quality of transport infrastructure, reduces the likelihood of choosing transit modes, particularly buses. Additionally, individuals with a higher perception of information-based exclusion tend to favour modes that require less information to ensure a reliable journey, such as the ACC or motorcycle. Regarding fear-based exclusion, we found that a high fear perception increases the disutility of time; therefore, individuals prefer a faster arrival at the destination, thereby reducing the probability of incidents. Finally, we simulated the modal share with the inauguration of a new ACC line, resulting in a significant increase in the ACC commuting modal share. Nevertheless, new users initially came from buses, showing a lack of attraction for private vehicle users (cars and motorcycles). This study emphasises the importance of including ACC as a mode choice and identifies specific characteristics that influence its selection. Lastly, it highlights the relevance of perceptions of transport-related social exclusion on commuting mode choice by considering distinct effects for particular modes, enabling specific recommendations to improve city mobility.
Autonomous shuttle buses offer significant potential for improving public transportation, enhancing traffic safety, and reducing environmental impact. However, their successful implementation depends not only on technological development but also crucially on user acceptance. While previous studies have primarily investigated acceptance based on surveys of individuals without direct experience, especially in critical traffic situations, our study addresses this gap through a simulated driving test incorporating such scenarios. Extending the UTAUT2 framework, we integrate objective NeuroIS indicators to propose a psychophysiological mediation model. We captured both autonomic arousal (electrocardiogram, galvanic skin response) and cognitive stress appraisals (Perceived Stress Scale, NASA-TLX, single item scales) to examine their impact on the behavioral intention to use autonomous shuttles. Data from N = 104 participants were evaluated using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that Social Influence and Perceived Usefulness are the primary drivers of usage intention, whereas Trust and Perceived Risk showed no significant effect in the simulated environment. Furthermore, the analysis supports a significant negative impact of perceived Cognitive Reaction on acceptance. Crucially, physiological indicators demonstrated a divergent mediation pattern: while heightened cardiac activation marginally increased cognitive stress, electrodermal activity actively reduced it. This points towards a “eustress” or heightened task-focus phenomenon, mitigating perceived psychological burden during critical rides. These findings highlight the importance of social validation and emotional responses in the acceptance of novel mobility technologies. The study demonstrates that subjective cognitive load, shaped by specific physiological arousals, acts as a pivotal barrier to acceptance, providing a robust foundation for future real-world exposure studies.
Women's mobility within public transport systems is shaped by fear and gendered vulnerability. The aim is to explore the safety work undertaken by young women in Auckland, New Zealand using whole journey approach. Majority of the participants are transit-dependent and ride public transport to attend university. Interviews were undertaken with 29 young women using a semi-structured questionnaire. The data is analysed using reflexive thematic analysis. The overarching finding is that fear of darkness and fear of others are the root cause their precautionary behaviour. Participants engaged in extensive safety work, including pre-planning journeys, maintaining vigilance, creating spatial separation, and modifying their appearance to become less visible. The young women communicated with family and friends throughout the journey and especially in the return journey at nighttime. Many participants discussed avoiding travelling altogether after 9 pm. The safety strategies, while providing a sense of control, also impose emotional and physical burdens that constrain women's mobility and reinforce unequal participation in public spaces. The findings highlight a duality: safety work functions both as a tool of empowerment and a manifestation of systemic inequity that places responsibility for safety on women themselves. For some participants, safety work offered reassurance while for others, it was exhausting and unjust. The results expose safety work as both a coping mechanism and a symptom of systemic inequality, where women bear personal responsibility for public safety failures. Addressing these inequities requires not only environmental improvements but also cultural transformation to ensure that women's journeys are shaped by confidence rather than caution, and by freedom rather than fear.
Cross-traffic turns (left turns in right-hand traffic) significantly delay buses at intersections, reducing transit reliability. This study introduces the Bus Cross-Traffic Turn Priority Box, a design that allows buses to bypass turn queues by using through lanes and enables the pre-acceleration of turning traffic when no bus is present. Microscopic simulations conducted at three intersections (a stylized three-way intersection, a stylized four-way intersection and a real-world intersection using observed traffic data) demonstrate substantial delay reductions for buses and minor improvements for general traffic. Unlike traditional bus priority measures, this approach requires minimal infrastructure changes and enhances overall intersection efficiency, supporting its adoption in space-constrained urban settings to improve public transport performance without adversely affecting other road users.
We study a loyalty program in public transportation to disentangle the causal effects of two key behavioral drivers of customer expenditure: the "anticipation effect" (the prospect of earning a future reward) and the "consumption effect" (the impact of using an earned reward). To do so, we analyze "ZVV Bonus", a program by Switzerland's largest regional tariff association, using panel data from approximately 485,000 individuals. The program's design, which awarded credits for single-ticket purchases for use in the following month, allows for its interpretation as a natural experiment, which we evaluate using a within-subject difference-indifferences (DiD) approach. Finding that the baseline DiD estimates are likely biased due to violations of the parallel-trends assumption, we propose and apply a novel offline-benchmarked trend-correction method, which leverages aggregated offline sales trends to construct a counterfactual that is robust to common time-varying demand shocks. Our corrected estimates show that while the anticipation effect significantly increased gross expenditure, the consumption effect was negligible and associated with a near one-to-one cannibalization of sales. Consequently, the program resulted in a net revenue loss for the tariff association. Our findings suggest that for high-frequency services, such as public transportation, the anticipation of a reward is a more powerful motivator than the reward itself.
Public transportation (PT) in developing countries, such as Indonesia, faces significant challenges due to both the growing number of private vehicles and the increasing popularity of ride-hailing services. This study aims to assess PT passengers’ loyalty propensity by first classifying them according to their daily use of different travel modes and examining how PT service attributes and sociodemographic factors are associated with their loyalty propensity. Through an on-bus survey of passengers in Yogyakarta, Indonesia, and using latent class cluster analysis, two distinct groups were identified: 251 respondents classed as flexible passengers, that is, those who supplement their PT use with other travel modes, and 206 respondents classed as exclusive passengers, that is, those who are more dependent on PT and rarely use alternative modes. Meanwhile, Rasch model analysis reveals that, for both passenger groups, affordable ticket pricing, headway and punctuality, and in-bus travel time have the highest association with passengers’ loyalty propensity, while cleanliness and comfort and public perceptions show weaker associations. In addition, exclusive passengers exhibited higher loyalty propensity than flexible passengers, with sociodemographic factors including age, income, occupation, and family size playing a significant role in shaping loyalty propensity among exclusive passengers. In contrast, for flexible passengers, sociodemographic factors do not play a significant role in influencing their loyalty propensity. Based on these findings, targeted policy recommendations are proposed to retain both flexible and exclusive passengers by addressing their distinct needs and expectations.
Digitalization is reshaping public transport worldwide through apps, real-time updates, and cashless payments. These tools promise efficiency and convenience, but in rapidly growing cities they can also deepen divides if access is uneven. Ensuring equity in this transition is critical for mobility systems already strained by congestion and inequality. This study examines the Jakarta Metropolitan Area (JMA), Southeast Asia's largest urban region and a fast adopter of transport digitalization. We surveyed 1000 commuters and interviewed 20 to assess who benefits and who risks exclusion. Most riders reported using digital tools (approximate to 84% used more than one app; approximate to 49% paid by smartphone), yet exclusion remains: lower-education, lower-income, and commuters in unstable or short-term jobs were more likely to travel 6-7 days a week, pay in cash, and rely on single modes. Socioeconomic effects on digital adoption were generally weak (Cramer's V < 0.2), but spatial disparities were more pronounced, with long commutes concentrated among Bogor residents (V = 0.341). Interviews highlighted persistent barriers such as fragmented payments, inaccurate app functions, and limited accessibility for older users. To prevent digitalization from widening inequalities, we recommend three priorities: unify multimodal payments, simplify digital platform integration, and strengthen digital literacy and accessibility support.
This study proposes an evaluation framework to assess fixed-route transit (FRT) and demand-responsive transit (DRT), examining performance through real-world operational data. The framework is based on a concise set of key performance indicators (KPIs) synthesized from the literature that represent the perspectives of transit users and providers. The developed framework is applied to a real-world case study, where the same area was served by FRT and DRT. The service evaluation returned a mixed message on the performance of FRT and DRT services. The results show that operational cost and greenhouse gas (GHG) emissions per passenger kilometre travelled (PKT) in FRT are 80% lower than DRT indicating that FRT could move more passengers per kilometre at both lower cost and emissions compared to DRT within the case study. Overall, the proposed framework offers a practical solution for planners to evaluate transit options for communities in diverse spatiotemporal environments.
Driven by advancements in automation and connectivity, this paper explores the development of automated Public Transportation (PT) systems. This study addresses the critical role of human bus drivers in ensuring safety, accessibility, and service quality, examining how their diverse responsibilities can be translated into a modular technical framework. By analyzing legal frameworks in Europe and North America and conducting expert interviews with bus drivers in Munich, Germany, we identify the main tasks human drivers perform, including passenger safety monitoring, passenger assistance, communication, vehicle control, traffic monitoring, and service operation. From these tasks, we derive a conceptual framework of functional requirements organized within a Sense-Plan-Act (SPA) model and a Service-Oriented Architecture (SOA). We propose a Public Transport Control System that integrates requirements into a high-level architecture, providing a foundation for developing systems that can operate safely and efficiently while maintaining high service quality and meeting legal standards.
Urbanization in rapidly growing cities has intensified demand for sustainable public transport solutions. Despite their potential, traditional and electric bus services remain underutilized due to limited attractiveness. The case study is Hanoi (Vietnam), where bus use is constrained by reliability and access challenges and strong competition from private motorbikes and ride-hailing. This study investigates passengers’ preferences and their willingness to pay (WTP) for key service improvements in the bus system. A Discrete Choice Experiment (DCE) was conducted to evaluate six attributes: ride experience, schedule punctuality, bus app usability, emission reduction, walking distance to bus stops, and fare. Based on survey data from 366 respondents, the study estimates WTP and examines how socio-demographic factors influence user choices. The findings indicate that ride safety and punctuality are most valued, followed by environmental friendliness and the usability of mobile applications. Notably, passengers with children and those expressing environmental concern show higher WTP, whereas low-income and infrequent users are more price sensitive. These results offer practical implications for fare policies, service design, and targeted communication strategies to enhance public transport adoption. The study contributes to the limited literature on bus service valuation in developing countries and supports evidence-based planning through DCE methodology.
Mobility as a Service (MaaS) integrates various transport services (e.g., public transport, ridesharing, carsharing, and micro-mobility) into a single digital platform, aimed at reducing car ownership and traffic congestion. In July 2021, we began the MaaS trial (ODIN PASS) at the University of Queensland (UQ), targeting students and staff. We collected data on users' trip characteristics, reasons for using MaaS, subscription records, and feedback on the service (e.g., attitudes and opportunities). While existing research explores the diversity in MaaS user travel and subscription behaviours, few studies examine core themes such as attitudes and opportunities from a real-world trial. This study addresses this gap by (1) identifying the heterogeneity in users' travel and actual subscription behaviours through a latent class analysis model, and (2) uncovering diverse core themes (i.e., attitudes and opportunities) to inform the future development of MaaS by using thematic analysis. The latent class analysis reveals three distinct user groups. The findings of this study highlight that multimodal-curious users pay greater attention to MaaS operational issues and app functionalities compared to daily public transport users and regular cost-conscious public transport users. These users tend to have higher car ownership, be slightly older, and are more likely to be PhD students or staff. In contrast, regular cost-conscious public transport users, who comprise a large proportion of undergraduate students, appear to be primarily cost-focused, with particular concern for reducing the bundle price. The findings offer transport planners insights into tailoring strategies for different user groups, enhancing the development of MaaS.
Metro systems serve as the backbone of urban public transit and play a critical role in sustaining daily urban mobility. However, unexpected events such as severe weather or equipment failures can partially disrupt metro services. Emergency bus services provide a timely and reliable alternative to disrupted metro segments, effectively mitigating their adverse impacts. This paper addresses emergency bus route design in response to metro disruptions. Unlike the traditional emergency bus routing strategy that provides point-to-point shuttle services between a disrupted station and a turnaround station, this study proposes a novel routing strategy that incorporates nearby transfer stations as additional evacuation endpoints, thereby effectively distributing passenger flows and alleviating congestion at turnaround stations during emergencies. To support this strategy, a multi-objective optimization model is developed to balance evacuation efficiency and passenger travel experience. Furthermore, to address the dynamic and uncertain nature of metro disruptions, a rolling horizon optimization method is introduced to dynamically adjust bus routes based on updated information, thereby enhancing the quality of bus routing decisions under uncertainty. The effectiveness and applicability of the proposed model are validated through a series of case studies. Results demonstrate that the proposed strategy outperforms the traditional strategy in reducing passenger delay.
Efficient charging planning and scheduling are crucial for electric buses (e-buses) due to their limited range and extended charging times. This paper focuses on the problem of planning the charging infrastructure for a public transport network in a rural area. Due to longer routes and poor road conditions in rural areas, especially in developing countries, conventional diesel intercity bus services account for significant carbon emissions from bus transport. However, there is a gap in planning the electrification of rural bus systems, especially in terms of charging infrastructure planning. Accordingly, the aim of this research is to identify optimal charging schedules using an integrated modelling approach. In particular, an optimisation model is developed to simultaneously determine the optimum location and capacity of charging facilities, along with optimal charging schedules for e-buses. This model aims to minimise the costs associated with charging infrastructure and the electricity consumed by the buses, considering time of use (TOU) electricity tariffs. A real-world case study of Kalyana Karnataka Road Transport Corporation (KKRTC) in Karnataka, India is presented to test the efficacy of the developed model. For the considered scenario in the Kalburgi division (the largest division in KKRTC), with 11 depots and 887 bus routes, the model provides 52 optimal locations with a total of 82 opportunity chargers. According to the model, the feasible electrification level is 67.08% in the case of rural battery electric bus (BEB) systems for this division. Finally, a sensitivity analysis is presented to understand the effect of battery size and charger power on the results. The proposed approach offers operators a valuable tool for making optimal decisions regarding e-bus networks.
Recent advances in battery technology and the global shift toward sustainable transport have accelerated the adoption of electrified public transit systems. However, the implementation of such systems is often constrained by the need for large battery capacities and the high costs associated with stationary charging infrastructure. This study investigates the potential of Mobile Autonomous Charging Pods (MAPs) which are autonomous mobile charging vehicles as an innovative and cost-effective strategy to support the electrification of high-frequency urban bus lines. Using microscopic simulation for inner-city trunk lines in Stockholm, three charging configurations are evaluated: (i) depot-only charging, (ii) depot charging combined with end-station charging, and (iii) depot charging supported by MAPs. Results show that the MAP-based approach enables a reduction in total battery capacity by up to 67% compared to the depot-only strategy and yields total cost savings of over 7 million USD in total cost of ownership across an 11-year horizon. In addition to reducing capital and grid connection costs, MAPs offer greater operational flexibility and resilience by decentralizing energy delivery and enabling dynamic in-motion or stationary charging. The findings highlight MAPs as a scalable and economically viable solution that complements traditional depot infrastructure, offering a path toward more adaptable and efficient electric public transport networks.
Origin-destination matrices of traveller flows are a key ingredient to transport planning. In public transport planning, most agencies conduct origin-destination surveys to extract line-level origin-destination matrices. These matrices, however, only partially represent ridership, as only a fraction of travellers are surveyed. Hence, they need to be scaled to real ridership, typically by using automatic passenger counts (APC) and algorithms such as iterative proportional fitting (IPF). This procedure works well for busy lines, where seed matrices present few or no zeros (i.e., absence of observations for a given origin-destination pair), however it becomes less reliable on sparsely used lines, where seed matrices present a high percentage of structural and sampling zeros. It is currently unknown, up to which percentage of zeroes IPF can be reliably used, and how to handle zeroes more generally. In this paper, we apply IPF to simulated (ground truth) and real origin-destination seed matrices to quantify the reliability of IPF and to test different replacement values for zeroes. We work with matching data from automatic passenger counters and a large origin-destination survey with 26,000 + participants on 70 + public transport lines that was conducted in 2022 in Geneva, Switzerland. We find that the reliability of IPF measured by the estimation error exponentially correlates with the percentage of zeros in the seed matrix. We test replacement values of zeroes between 0 and 10 and find that 1 is the best replacement for sampling zeros in the seed matrix to minimize the estimation error and simultaneously improve convergence. Practitioners and academics can use these results to maintain the advantages of IPF in practice (computational lightweight, simplicity, implementation in most common software) yet improve its reliability on sparsely used lines.
Evaluation and prioritization of public transport infrastructure remain pivotal challenges for urban planners and transport agencies. Traditional Level of Service (LoS) metrics, while useful, often overlook the compounded impacts of delays on passengers, particularly on heavily used transport corridors. This paper introduces the "Man-Hours (M-H) Factor", a novel evaluation metric that integrates vehicle operational data, travel time deviations, and passenger occupancy to quantify the potential cumulative burden of delays on passenger time. Using Manhattan’s and Kaohsiung’s public bus network as a case study, the methodology uses extensive data, including GPS-based travel times and hourly passenger counts, to recalibrate LoS metrics and identify high-priority inter-stop sections for intervention. The results reveal significant man-hour savings potential in select inter-stop sections and demonstrate how Man-Hours (M-H) Factor shifts prioritization to heavily utilized routes, offering a more equitable and actionable framework for decision-making. By incorporating passenger-centric metrics, this study provides a scalable, data-driven approach to the evaluation and planning of transport infrastructure, with broad implications for sustainable and equitable urban mobility systems.
In recent years, Greece’s mobility infrastructure has been increasingly criticized for its unreliability, territorial disparity, and lack of transparency. These systemic issues have been exacerbated by socio-political unrest and tragic events, most notably the 2023 Tempi railway disaster. Through a mixed-methods approach; combining surveys, qualitative interviews, and participatory workshops, this study analyses how a group of Greek youth experience and interpret public transport. The findings reveal a fragmented system shaped by underinvestment, poor planning, and civic disconnection. Beyond infrastructure, the research highlights the emotional and political dimensions of mobility, with recurrent distrust in institutions, normalized resignation, and reliance on individual strategies. Ultimately, the article argues that transport in Greece might not simply be a technical challenge but also a perceived erosion of democracy, and calls for a redefinition of transport policy as a central element of social justice and public accountability.
