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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

To facilitate the shift from conventional to electric buses, the required charging infrastructure must be deployed. This study models the charging station location selection problem for fixed-line public transport services consisting of electric buses. The model considers the deadheading time of electric buses between the final stop of their trip and the locations of the potential charging stations with the objective of minimizing vehicle running costs. The problem is solved at a strategic level; therefore, several parameters of day-to-day operations, such as deadheading distances, are included as aggregate data considering their average values. In addition, it considers different charger types (slow and fast), which are subject to a day-ahead scheduling of the charging sessions of the buses. The developed model is a mixed-integer nonlinear program, which is reformulated as a mixed-integer linear program and can be solved efficiently for large networks with more than 1940 bus trips and 336 charging installation options. The model is applied in the Athens metropolitan area, demonstrating its potential as a decision support tool for selecting charging station locations and charger types in large public transport networks.

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.

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.