We propose a topological formulation of accessibility based on the notion of Access Graph, in which two nodes are connected if they are reachable within a given travel time. We trace the emergence and evolution of its subgraphs with imposed levels of connectedness, specifically maximal clique and k-cores. We propose two complementary sets of accessibility indicators, cumulative and threshold, based on integral measures of subgraph growth and times at emergence of k-cores, respectively. For a meaningful comparison of networks across different dimensions, we contrast the realised accessibility with that of an idealised network on the same set of nodes. The proposed measures offer a view of accessibility that extends beyond the commonly used node-averaged indicators. Empirical analysis of 42 metro networks worldwide demonstrates universal patterns of accessibility behaviour. We illustrate the practical application of this approach on a case study where we examine the accessibility impacts yielded by alternative infrastructure and service developments. Our results amount to the reconceptualisation of accessibility within the complex network framework.

Tradeable Mobility Credits (TMC) are a novel demand management policy. Travel can be priced based on externalities and travellers are allocated TMC, which are consumed when travelling, with the price depending on trip characteristics. Travellers can buy/sell TMC in exchange for money. In this study, we analyse (1) how travel behaviour would be affected by a TMC-scheme, (2) TMC trading behaviour and (3) their interaction. We carry out an online stated preference survey, and apply a latent class choice model (LCCM) to analyse travel behaviour, whereas credit trading is analysed by means of a multiple linear regression. A key finding throughout the research is that TMC tend to be perceived non-linearly, with a logarithmic transformation often outperforming linear specifications. This means each additional credit carries less value. The LCCM reveals three out of four groups (88 % of respondents) consider their current balance when making travel choices. Two groups (∼50 %) are predominantly unimodal, travelling almost exclusively by bicycle or public transport. Others base their decision primarily on travel time and cost. In trading, the exchange rate and balance have a substantial influence, offering evidence for loss aversion. The number of travel instances remaining, and the experience of having performed a trade in the past also affect trading behaviour, whereas socio-demographic characteristics are found to have a limited impact. Our result show a TMC policy can achieve substantial behavioural adaptations, reaching the desired outcomes. The limited awareness of such policies, concerns about equitable TMC allocation and additional hassle associated with trading remain challenges to be addressed.

Understanding multi-modal urban mobility patterns is essential for effective planning and policy-making. Traditional data sources, such as infrequent surveys or smart card records, often lack the temporal, spatial, and modal comprehensiveness required to fully capture the complexity of multi-modal travel behavior. Emerging mobility data sources are instrumental in capturing these patterns and in enabling additional insights. This study leverages a digitally collected trajectory-level dataset (i.e., TravelSense) obtained from a smartphone application operated by the public transport authority of Helsinki, Finland. Unlike conventional public transport data, TravelSense provides insights into modal choices alongside temporal and spatial travel characteristics. In order to analyze mobility patterns and explore the capabilities of this novel dateset, a Latent Profile Analysis is employed to classify travelers based on these attributes over a week-long period, with profiles compared across three consecutive years (2022, 2023, and 2024). Findings reveal that while spatial travel patterns remain relatively stable, temporal and modal patterns exhibit greater variability. A distinct shift is observed between 2022 and subsequent years, likely reflecting post-pandemic behavioral changes. Key traveler groups identified include exclusive active mode users (13 % annually) and non-private car users, whose share declined from 38 % in 2022 to approximately 20 % in 2023 and 2024. Study findings offer valuable input for shaping evidence-based mobility policies, particularly those aiming to support sustainable travel behavior and adapt to evolving urban mobility needs through enhanced multi-modality. TravelSense enables detailed analysis of temporal, spatial, and modal travel patterns, underscoring the value of novel data for multi-modal transport research.

Providing relevant information is crucial in public transport systems. With the rise of digital passenger information systems (PIS), personalization has emerged as a means to meet passengers’ information needs better. To better understand how personalization can be implemented in PIS, five levels of personalization have been identified in the literature, highlighting varying degrees of system autonomy and passenger involvement. While these levels have advanced the understanding of personalization, their practical application remains limited. This paper builds upon an existing framework of personalization levels. It introduces an evaluation tool composed of distinct performance measures to help PIS developers assess their system’s current personalization level and identify opportunities for technological advancement. The tool enables a comparison of the personalization functionalities against the best practices defined by the personalization levels. The paper further outlines the creation of the tool through functional benchmarking, introduces system behaviors across levels, and evaluates commercial PIS through case studies, offering actionable insights for advancing PIS personalization.

Ride-sourcing platforms such as Uber and Lyft are prime examples of the gig economy, recruiting drivers as independent contractors, thereby avoiding legal and fiscal obligations. Although platforms offer flexibility in choosing work shifts and areas, many drivers experience low income and poor working conditions, leading to widespread strikes, protests and lawsuits against the platforms. In response, minimum wage regulation is adopted to improve drivers’ welfare. However, the impacts of this regulation on drivers as well as on travelers and platforms, remain largely unknown. While ride-sourcing platforms do not disclose the relevant data, state-of-the-art models fail to explain the effects of minimum wage regulation on market dynamics. In this study, we assess the effectiveness and implications of minimum wage regulation in ride-sourcing markets while simulating the detailed dynamics of ride-sourcing markets under varying regulation intensities, both with and without the so-called platform lockout strategy. We apply the model to Amsterdam due to the availability of detailed travel-demand data; while the framework is transferable to other cities, the magnitude of the results may vary with local market conditions. Our findings reveal that minimum wage regulation impacts substantially drivers income but may also lead to higher fares for travelers and threaten platforms’ survival. When platforms adopt a lockout strategy, their profitability significantly improves and drivers earn even more, although many others lose their jobs, and service level for travelers consequently declines. These findings highlight the complex trade-offs involved in regulating ride-sourcing market.

Demand prediction is essential for effective management of Mobility-on-Demand (MoD) systems, as accurate forecasts enable better resource allocation, reduced wait times, and improved user satisfaction. Beyond that, probabilistic prediction methods that explicitly account for uncertainty are particularly valuable, as it allows decision-makers to assess risk and make robust plans under uncertain operational environments. However, most existing approaches focus on point predictions, which fail to capture the full spectrum of possible future outcomes. For probabilistic prediction, many methods typically rely on strong parametric distributional assumptions that may not accurately reflect the complex real-world environments. Nonparametric methods proposed in the literature, although promising, often suffer from high computational costs and model complexity, limiting their practical applicability. To overcome these challenges, we propose the Spatial-Temporal Graph Convolutional Network Variational Autoencoder (STGCN-VAE), a novel deep learning framework designed for uncertainty-aware probabilistic travel demand prediction in MoD services. The STGCN-VAE effectively captures complex spatial-temporal dependencies and inherent uncertainties in MoD demand data, generating diverse and realistic future demand scenarios and constructing comprehensive demand distributions. Specifically, the proposed framework integrates three key components: a Spatial-Temporal Graph Convolutional Network (STGCN) to learn complex spatial-temporal dependencies, a Variational Autoencoder (VAE) to compress these patterns into a latent space, and a Kernel Density Estimation (KDE) module to accurately construct probabilistic demand distributions and quantify uncertainties. Experiments on four different real-world MoD datasets including both rideshare and bikeshare services across different cities demonstrate that STGCN-VAE consistently outperforms state-of-the-art baselines in both point and probabilistic prediction, highlighting its robustness and broad transferability across service modes and urban contexts.

On-board crowding in public transportation has significant impact on passengers' travel experience. New land-use planning configurations can have wide-ranging crowding effects in the public transportation system. Nevertheless, there is a lack of knowledge on the crowding implications caused by new urban developments. In this study, we propose a method for quantifying the network-wide crowding implications of a new urban development. We apply the method to different kinds of urban developments in terms of type, size, location, proximity to high-capacity public transportation connections as well as socioeconomic characteristics. Size and proximity to a high-capacity connection are highly influential factors in determining the value and the geographical extent of the crowding implications. The analysis proposed in this paper can serve as a tool for the ex-post quantification of the on-board crowding impacts using automated data sources. The insights gained can be utilized in more efficient dimensioning of the supply (service) for newly developed areas as well as for placement of future urban developments accounting for the resulting crowding effects.

Short-haul Flight (SHF) bans aim to stimulate the air-to-rail modal shift, consequently curbing the aviation sector's environmental impact. We investigate the potential implications of various SHF ban policy designs on CO2-equivalent (CO2e) emissions, passengers’ travel times and rail capacity under the assumption of full air-to-rail modal substitution. Ranging from 0.4 Mt to 7.5 Mt CO2e, respectively 0.6% to 12.3% of the emissions of commercial intra-European aviation, the environmental impact of SHF ban policies is shown to be largely dependent on the policy design, namely the affected journey types and rail in-vehicle time thresholds. Our findings underscore the significant challenges of implementing such policies for the longer rail in-vehicle time thresholds and wider geographical scopes associated with noticeable environmental benefits. Despite the marginal impact of SHF ban policies on capacity utilisation in the case study, considerable interventions on rail infrastructure would be required to absorb existing air demand completely while ensuring attractive schedules. The results contribute to the ongoing policy debate, providing actionable insights to support Europe's ambitious environmental goals in the transport sector.

Modular vehicles (MVs), equipped with autonomous driving, communication, and platooning capabilities, are emerging as a promising innovation in transportation, offering the potential to enhance operational efficiency, flexibility, and environmental sustainability. However, challenges and barriers to their successful implementation are not yet fully understood, which limits the realization of these benefits. This literature review synthesizes existing research on MVs across various applications, including passenger and freight transport, to provide a systematic evaluation of state-of-art, opportunities and challenges for modular freight transport systems. The review identifies research gaps in five areas, such as their integration with multimodal transportation, and highlights key deployment challenges including regulatory hurdles, human factors, financial constraints, and operational complexities. Our findings emphasize the need for policy development, system design research and further empirical validation to assess the practical feasibility and impacts of MVs in the freight transport sector.

The electrification of transportation has emerged as a key focus area over the past decade, driven by the rise of electric vehicles (EVs) and supportive governmental policies. Conventional EV charging solutions, while foundational, face notable challenges such as high infrastructure costs, low flexibility, and underutilization. Simultaneously, emerging transportation modes such as autonomous vehicles, shared mobility, modular systems, and aerial vehicles, introduce additional complexities, demanding more innovative charging solutions. This review emphasizes the potential of charge-on-the-move systems referred to as dynamic charging, as a transformative approach to address these challenges. Dynamic charging enables EVs to recharge while in motion, presenting opportunities to minimize battery sizes, reduce emissions, and optimize operational efficiency. The study critically evaluates state-of-the-art dynamic charging technologies, including their benefits, limitations, and applicability to future mobility systems, while also comparing these solutions based on infrastructure costs, readiness, and scalability. The findings suggest that the future of EV charging will likely involve a hybrid approach, integrating both conventional and dynamic solutions. Key priorities for advancing dynamic charging include developing optimization models for infrastructure deployment, finding the balance between battery size and battery life, establishing interoperability standards, and enhancing energy transfer efficiency while ensuring safety and sustainability. By addressing these research challenges, dynamic charging systems have the potential to redefine EV infrastructure and support the broader transition to sustainable and efficient mobility ecosystems. This review serves as a guide for researchers and planners seeking to align charging technologies with evolving transportation needs.

Shared on-demand mobility services, also known as microtransit, have become a major mobility provider around the world, yet this has predominantly taken place within urban areas. In areas with lower population density and poor quality public transport, such services could substantially improve accessibility. In early 2023, a regional microtransit pilot was carried out in the Ljubljana Urban Region in Slovenia. To assess the preferences towards such a service, a stated preference experiment is carried out among pilot participants, comparing car, public transport and microtransit for their daily commute. The obtained data is modelled using a Panel mixed logit model, with random parameters modelled as normally or log-normally distributed. Additionally, we also model for potential nesting effects among the alternatives. The results show participants perceive microtransit as a viable alternative, with public transport commuters finding it particularly attractive, whereas car commuters see it on par with the car. Parking price and a guaranteed parking spot tended to be key factors for decision-making. Simulating different policies, we conclude that combining subsidising microtransit and higher parking prices is the most effective strategy for achieving a modal shift primarily from car to microtransit while not affecting public transport as much.

Providing relevant information to passengers is essential for the functioning of the public transport system. With the digitalisation of passenger information systems (PIS), passengers currently have access to large amounts of information. To avoid cognitive overload among passengers, public transport systems experiment with applying personalisation to PIS, allowing for the provision of tailored information according to the needs and desires of passengers. Notwithstanding, systematic definitions and guidelines for designing personalised PIS in public transport are currently lacking. We, therefore, introduce a framework for assessing the personalisation levels of PIS, to close the gap between theoretical conceptualisations and practical implementations of PIS. Our framework defines five levels of personalisation, which are substantiated by a review of 40 papers focusing on personalisation in PIS.

Bert van Wee, professor in Transport Policy at Delft University of Technology, the Netherlands, faculty Technology, Policy and Management, is retiring. Given his large contributions to EJTIR as Editor-in-Chief, editorial board member, author and reviewer, this Editorial Note is dedicated to his work for EJTIR. Over 25 years, van Wee published 18 papers and 1 book review in EJTIR covering a wide range of topics from road pricing to urban rail transport, vehicle automation and port throughput. What his studies have in common is that they explore how transport policies affect land-use and travel behaviour, as well as the economic and wider societal impacts of those policies. Bert van Wee’s generalist view on the transport system is rare, but, given the rising complexity of the system, increasingly needed to indeed be able to address the future challenges.

The shift towards environmentally friendly and efficient electric bus transportation systems oftentimes raises unexpected operational issues. This study models the Electric Bus Charging Station Location Problem (EB-CSLP) to develop a more resilient charging infrastructure, focusing on time-related and energy consumption uncertainties, specifically inter-station travel time delays. The model accommodates various charger types and maintains time continuity in the charging of electric buses. Initially formulated as a mixed-integer nonlinear program (MINLP), our stochastic optimization model is reformulated into a mixed-integer linear program (MILP) which minimizes both deadheading times and queue waiting times at the charging locations. The stochastic optimization model is tested in a real-world case study in Athens, Greece, considering multiple scenarios with varying inter-station travel times and energy consumption. The results demonstrate its effectiveness as a potential decision-support tool for selecting the optimal charger types and charging station locations under travel time and energy-related uncertainties.

Ride-hailing has become an important part of the urban mobility landscape. The main contribution of this study is to estimate how travellers perceive time when using ride-hailing compared to using conventional public transport, to better understand ride-hailing mode choice. We combine two unique datasets containing actual, individual passenger behaviour for the Washington DC area from October 2018: a large set of almost 250,000 individual ride-hailing trips made using Uber, and more than 326,000 public transport trips obtained from automated ticketing data. Contrary to previous studies our model estimations rely on over half a million directly observed passenger choices between ride-hailing and public transport, based on which we estimate a discrete choice model to infer travel time perceptions for both modes using a binomial logit model. Our results show that on average ride-hailing in-vehicle time is perceived 35% less negative than public transport in-vehicle time. We also found that waiting time for ride-hailing is valued 1.3 times more negative than ride-hailing in-vehicle time, which is about 20% less negative than the ratio between waiting and in-vehicle time found for public transport. Our study enables a more accurate modelling of ride-hailing by using mode-specific travel time coefficients derived from large-scale empirical data, which can improve the accuracy of modelling outputs and thus improve decision-making processes.

This research introduces a new method for constructing and training an Urban Space Embedding Model (USEM) by integrating human perceptions and street-level images (SLI) into its formulation. Traditional urban embedding models often overlook subjective human experiences, such as perceptions of safety or attractiveness. To address this gap, our method leverages similarity judgments from over 1500 participants, who compared different urban spaces based on SLI. These human judgments were then used as a supervision signal in training the USEM, allowing the model to capture both visual and perceptual information about urban spaces. The method is implemented across the Netherlands, using around one million geo-tagged SLI, and demonstrated in Rotterdam. This approach represents a significant advancement in urban computing by incorporating human-centered data into urban modeling. It offers new opportunities for city planners and policymakers to better understand how urban spaces are perceived and to consider these perceptions in efforts to design more livable and inclusive environments.

The emergence of social media offers unprecedented opportunities to map social unrest with high spatiotemporal resolution. This study leverages geolocated social media footage to analyze the spatiotemporal distribution of the 2023 ‘Nahel Merzouk’ riots in France. Using a fine-tuned computer vision model, we detect riot-related content in visual data and validate our approach by comparing the spatiotemporal patterns of detected posts with rioting events reported in the press. Our method yields a spatial resolution of 300 300 m, thereby facilitating a detailed analysis of riot distributions at unprecedented scale. By applying density-based clustering, we map riots across seven French cities, revealing their highly localized and bursty dynamics. This study opens pathways for future research on the causes and dynamics of social unrest, enabling a deeper understanding of urban riots and their potential mitigation.

The shift from private vehicles to public and shared transport is crucial to reducing emissions and meeting climate targets. Consequently, there is an urgent need to develop a multi-modal transport trip planning approach that integrates public transport and shared mobility solutions, offering viable alternatives to private vehicle use. To this end, we propose a preference-based optimization framework for multi-modal trip planning with public transport, ride-pooling services, and shared micro-mobility fleets. We introduce a mixed-integer programming model that incorporates preferences into the objective function of the mathematical model. We present a meta-heuristic framework that incorporates a customized Adaptive Large Neighborhood Search algorithm and other tailored algorithms, to effectively manage dynamic requests through a rolling horizon approach. Numerical experiments are conducted using real transport network data in a suburban area of Rotterdam The Netherlands Model application results demonstrate that the proposed algorithm can efficiently obtain near-optimal solutions. Managerial insights are gained from comprehensive experiments that consider various passenger segments, costs of micro-mobility vehicles, and availability fluctuation of shared mobility.

Recent advances in automation have accelerated the development of autonomous electric vehicles (AEVs), which offer the potential for continuous operation, constrained primarily by the need for recharging. We propose a dynamic charging strategy based on Mobile Autonomous Charging Pods (MAPs), which are battery-equipped electric vehicles capable of transferring energy to AEVs while in motion. We introduce a dedicated simulation framework within the microscopic traffic simulator SUMO, incorporating MAP-specific modules for assignment, navigation, and real-time energy transfer under realistic traffic constraints. We model the behavior of both MAPs and AEVs in a stylized looped network and evaluate system-level performance under various demand and fleet configurations. Key performance indicators include energy consumption, charging efficiency, battery utilization, and reductions in AEV battery capacity requirements. Simulation results demonstrate that MAPs can effectively support continuous AEV operation, achieving up to 14% battery downsizing with minimal infrastructure investment, while also reducing travel time by 7%, relative to fixed charging solutions. This study lays the foundation for simulation-based evaluation of MAP-based dynamic charging as a scalable, flexible, and efficient alternative to fixed charging solutions.

As a two-sided digital platform, ride-sourcing has disruptively penetrated the mobility market. Ride-sourcing companies provide door-to-door transport services by connecting passengers with independent service suppliers labelled as “driver-partners”. Once a passenger submits a ride request, the platform attempts to match the request with a nearby available driver. Drivers have the freedom to accept or decline ride requests. The consequences of this decision, which is made at the operation level, have remained largely unknown in the literature. Using agent-based simulation modelling on the realistic case study of the city of Amsterdam, the Netherlands, we study the impacts of drivers’ ride acceptance behaviour, estimated from unique empirical data, on the ride-sourcing system where the platform applies regular and surge pricing strategies, and riders may revoke their requests and reject the received offers. Furthermore, we delve into the implications of various supply–demand intensities, a centralised fleet (i.e., mandatory acceptance on each ride request) versus a decentralised fleet (i.e., ride acceptance decision by each driver), ride acceptance rates, and surge pricing settings. We find that the ride acceptance decision of ride-sourcing drivers has far-reaching consequences for system performance in terms of passengers’ waiting time, driver's revenue, operating costs, and profit, all of which are highly dependent on the ratio between demand and supply. As the system undergoes a transition from undersupplied (i.e., real-time demand locally exceeds available drivers) to balanced and then oversupplied state (i.e., more available drivers than real-time demand), ride acceptance decisions result in higher income inequality. A high acceptance rate among drivers may lead to more rides, but it does not necessarily increase their profit. Surge pricing is found to be asymmetrically in favour of all the parties despite adverse effects on the demand side due to higher trip fare. This study offers insights into both the aggregated and disaggregated levels of ride-sourcing system operations and outlines a series of transport policy and practice implications in cities that offer such ride-sourcing systems.