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.

Efficient matching in ride-hailing and ride-pooling services depends not only on how matches are constructed, but also on when the platform triggers a matching operation. Many systems use batched matching with a fixed time interval to accumulate requests before matching, which increases the candidate set but cannot adapt to real time supply-demand fluctuations and may induce unnecessary waiting. This paper proposes a reinforcement learning approach that learns when to trigger matching based on current system conditions. We formulate the timing problem as a finite-horizon Markov decision process and train the policy using the Proximal Policy Optimization algorithm. To address sparse and delayed feedback, we introduce a finite-horizon, potential-based reward shaping scheme that preserves the optimal policy while densifying the learning signal; the same framework applies to both ride-hailing and ride-pooling, where detour delay is incorporated into the reward for pooling. Using a data-driven simulator calibrated on NYC trip records, the learned policy adapts matching timing decisions to the current state of waiting requests and available drivers and outperforms fixed-interval, rule-based dynamic, and first-dispatch baselines. It reduces total waiting time by 3.1% in ride-hailing and 20.1% in ride-pooling, and detour delay by 36.1% in pooling, while maintaining short matching times.

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.

The rise of autonomous electric vehicles (AEVs) presents new challenges and opportunities for an efficient and flexible charging infrastructure. This study proposes a reinforcement learning (RL) based framework for optimizing the control and operation of mobile autonomous charging pods (MAPs) for maintaining the operation of AEVs through dynamic charging. We formulate a time and energy aware Markov Decision Process (MDP) to maximize the energy delivered, and the number of AEVs serviced, while also minimizing energy consumed and increasing efficiency. We integrate this framework with SUMO to enable realistic MAP-AEV interactions. A Proximal Policy Optimization (PPO) algorithm was used to train this MDP and identify the optimal control strategies for initiating, terminating, and balancing the network. The results show that the PPO agent can service around 175 AEVs, with an efficiency of 91.5%, representing a 25% improvement over baseline greedy heuristics. Moreover, the battery capacities of AEVs can also be reduced by up to 26%, without compromising the performance. The simulation results show the potential of the proposed method in providing a flexible, and scalable charging for future transport.

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.

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.

Previous research has shown that residential segregation often aligns with urban fragmentation in contexts where explicit segregation policies were historically implemented. However, it remains unclear whether this alignment also emerges in contemporary urban contexts where segregation is driven by market mechanisms and residential preferences. Here we analyze 520 cities across eight Western European countries using high-resolution demographic data and a Monte Carlo approach to test whether residential segregation of non-EU migrants aligns with urban fragmentation by railways, motorways, and waterways. We find that the relationship between residential segregation and urban fragmentation is highly heterogeneous across Europe. Rather than a uniform trend, our results reveal regional divergence: while the Netherlands and Germany exhibit a significant alignment, Spain, the United Kingdom, and Italy show less alignment than expected by chance. These findings suggest that urban barriers do not generally function as social frontiers in European contexts, with country-specific urban development potentially influencing the observed regional differences.

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.

High-speed rail (HSR) is often considered a promising and sustainable alternative for long-distance travel in the European context, aligned with Europe’s ambitious mobility and climate goals for 2050. However, a cohesive European HSR network is yet to be realised. Critically, the planning of a European HSR network requires considering how the network is to gradually evolve from its current fragmented state. We introduce an Evolutionary Network Growth model with Infrastructure and Network Effects considerations for European Rail (ENGINEER). This novel iterative network growth model selects the HSR infrastructure with the highest economic potential, continuously updating network configurations and demand patterns, subject to budget feasibility constraints. ENGINEER integrates cost estimates based on a microscopic representation and benefits estimated based on a macroscopic travel demand representation and is applied across 28 European countries. Our findings highlight the importance of path dependency and the benefits of an integrated decision-making in infrastructure planning. Model results demonstrate that ENGINEER can effectively identify promising HSR investments, yielding a cohesive and well-integrated European HSR network which leads to an increase in rail mode share per trip from 13% in 2023 to 27% by 2065.

Air–rail integration agreements are widely regarded as an important strategy to spark and stimulate a modal shift from air to rail. Intermodality has been consistently promoted by European transport policy over the last three decades. At the same time, the literature widely concurs on the potential benefits of air–rail integration for passengers, airports, airlines and rail operators. However, as of 2025, the availability of air–rail integration alternatives on the market is limited, and their potential benefits remain largely unexplored. Thus, this paper investigates the substitution potential of air–rail integration in Europe, compiling an inventory of rail connectivity at European airports and proposing a simple and interpretable indicator to quantify the Air–Rail Integration Substitution Potential (ARISP) at the route and airport levels. Our findings indicate that the substitution potential of air–rail integration in Europe is minimal: even when considering rail travel times within a 100% increase of existing air travel, the potential market represents less than 1% of the 1.2 billion intra-European air journeys. The modest competitiveness of rail travel times and the limited potential passenger flows on most substitutable routes suggest that air–rail integration should not be proposed as an environmental policy but rather as one to enhance connectivity. The ARISP indicator further reveals that the limited substitution potential is highly concentrated across a limited number of routes, airports, and geographical regions. Targeting them by directly connecting cities and airports’ railway stations with non-stop high-speed services (where possible) may enhance the effectiveness of air–rail integration on substitution. Our analysis shows that rail infrastructure and service provision at airports, as well as their position within the European railway network, are important determinants of the substitution potential of air–rail integration.

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.

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.

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.

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.

Widespread congestion in metro systems often hinders passengers from boarding the first arriving train, making them compelled to adopt an alternative route, some of which involve travelling backwards. While this travel strategy has direct consequences for forecasting passenger flow distribution in congested networks, little is known about the travelling backwards phenomenon and why people adopt this travelling behaviour. The aim of this study is to understand passengers’ perception of time in various segments considering travelling backwards. To achieve this, we develop a route choice model using revealed preference data from smart card records. We find that passengers exhibit a greater aversion to waiting time and onboard time while travelling backwards. Specifically, passengers perceive each minute spent waiting on the turn-back stations’ platform as equivalent to 1.97 min on the origin platform. Similarly, each minute spent onboard the backwards train is perceived as equivalent to 1.24 min on the forwards train. Ignoring this difference in perception would result in the underestimation of the expected social benefits of demand management policies. Finally, we assess the potential benefits of travelling backwards under various passenger flow conditions, offering valuable policy insights regarding whether and how this behaviour should be regulated or promoted.

This study proposes three mathematical programming models with distinct optimization objectives for transfer optimization in a bi-modal public transport network. To improve the applicability of the models and expedite the solution process, some acceleration techniques, including eliminating redundant constraints and incorporating valid inequalities, are suggested. The models and solution methods are applied to a small toy network and a real-life bi-modal public transport network. The results indicate that compared to the third model, the second model can reduce the total transfer waiting time by 12.29% to 30.31%, while the longest transfer waiting time may increase by 4.35% to 22.22%. Furthermore, the third model, which prioritizes minimizing the longest transfer waiting time, may increase the total or average transfer waiting time. The results suggest that decision-makers need to make a trade-off between reducing total passenger transfer waiting time (for efficiency) and reducing the longest passenger transfer waiting time (for fairness). .

We investigate the correspondence between network-based public transport network (PTN) supply indicators and passenger demand at the node level, by systematically assessing correlations between node centrality measures and passenger boarding counts across different graph representations of PTNs. At the stop-level, undirected L- and P-space representations with three different edge weightings: unweighted, service-frequency-weighted, and in-vehicle-time-weighted are analysed. In each case, we calculate degree, closeness, betweenness and eigenvector centralities and examine the relation shapes. At the route level, we examine degree and eigenvector centrality for unweighted and weighted C-space representations. We introduce a modified C-space representation with self-loops, with service frequencies as self-loop weights, and propose eigenvector centrality as a route-level supply indicator. Stop- and route-level properties are integrated using the B-space representation. This methodology was applied to a case study for a bus PTN in Ljubljana, Slovenia. Results show strong correspondence between passenger demand and degree and eigenvector centrality scores in the frequency-weighted P-space (correlation ≈0.7−0.8). Notably, the relationship between eigenvector centrality and passenger counts in the new C-space representation with self-loops exhibits logarithmic behaviour. Furthermore, the results suggest a minimum eigenvector centrality threshold (≈10⁻³) for a route to start facilitating passenger use. The route-level results from the B-space analysis show exponential convergence of passenger counts to route eigenvector centrality. Results of the stop-level analysis are in line with previous research and deepen the understanding of centrality measures as supply indicators. Most significantly, the route-level analysis is novel, and the results open promising venues for further research.

As concerns about climate change increase, the environmental impact of long-distance travel – including academic conference travel – is coming into focus. Multiple universities have recently started to deploy sustainability policies committed to net-zero targets. However, it remains uncertain today whether academics are ready to embrace such initiatives and transform their practices for the sake of the environment. In this study, we explore the motivational factors behind academics’ willingness to limit their conference travel based on a survey conducted in Spain. The results highlight the role played by a set of demographic, socioeconomic, work-related, and attitudinal factors, as estimated by means of an ordered logit model. All else being equal, postdoctoral researchers and individuals who live in single-person households are more likely than others to reduce their travel. In terms of psychological attributes, we detect that individuals with a higher level of green values and more influenced by social norms are more intended to limit their conference trips. Conversely, those who believe that conferences are a driver for professional development are less willing to lower their travel. Our findings can help institutions to identify the segments of academics with a higher (and lower) probability of changing their behaviour towards more sustainable habits.

Today, there is growing concern over the climate impact of long-distance travel – academic conference travel included. While institutions worldwide develop sustainability policies, it remains unclear whether academics are willing to adopt such measures and change their behaviours for environmental reasons. Based on a survey of 245 Spanish academics, we identify a set of motivational factors underlying individuals’ intentions towards conference travel. Using Latent-Class-Cluster-Analysis, we detect three distinct profiles: “Low-concerned academics”, with limited regard for environmental concerns and social norms; “Performance-oriented academics”, driven by career advancement; and “Flight-shamed academics”, highly aware of environmental and social issues. Additionally, we examine perceptions of the actual, minimum, and expected number of conferences attended annually, recognising notable discrepancies. Our findings can inform targeted policies in four key areas: promoting greener choices, reshaping academic culture to normalise lower-carbon practices, raising awareness and strengthening institutional engagement, and supporting online participation.

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.