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.

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.

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.

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.

Visitors’ induced seasonal demand spikes make supply dimensioning a challenging task. Smart card data have been widely used for exploring segmented passenger travel behavior; however, their potential in the tourism context remains underexplored. In this study, we portray distinct temporal and spatial visitor profiles in the Stockholm region by means of smart card data analytics. Empirical understanding of visitors’ travel patterns can inform public transportation planning in relation to tourist destinations and contribute to the strategic assessment and refinement of policies such as fare structures. In our analysis, visitors are defined using a combination of criteria, including: i) one-time users with a travel span of less than a week (activity criterion), and ii) users utilizing dedicated visitor fare products (fare product criterion). We apply k-means clustering and identify four activity clusters, namely Multi-travel short-stay visitors, Long-stay and frequent travellers, Day trippers, and Evening visitors, as well as three spatial visitor clusters denominated as City center visitors, Outside the city center visitors, and Mixed visitors. The clustering reveals that Day trippers dominate the activity segmentation, accounting for 38.5% of visitors, while the spatial segmentation results in three more evenly distributed clusters. Notably, 37.3% of visitors primarily utilize the public transportation system outside the city center, highlighting the need for policy attention beyond central areas – for example, accessibility improvements for less accessible visitor destinations. Last, our framework is fully reproducible, demonstrating the potential of smart card data in exploring visitors’ travel behavior.

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.

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.

Accessibility, defined as travel impedance between spatially dispersed opportunities for activity, is one of the main determinants of public transport use. In-depth understanding of its properties is crucial for optimal public transport systems planning and design. Although the concept has been around for decades and there is a large body of literature on accessibility operationalisation and measurement, a standardised approach is lacking. To this end, we introduce a dedicated graph representation of public transport networks, termed the Access Graph, or A-space, based on the generalised travel times between nodes. We introduce an edge between two nodes in the access graph if the travel time between them is below a certain threshold time budget. In this representation, node degree directly measures the number of nodes reachable within a predetermined time, reproducing the cumulative opportunities measure of access at each specific value of the time budget. We study the threshold-dependent evolution of the degree distribution of the access graph and define a set of accessibility indicators. The indicators are observed at network-specific and passenger-based characteristic times. We apply the methodology to a dataset of 51 metro networks worldwide. For the Washington DC metro, we validate the approach by comparing supply to demand at the origin–destination level. The new representation addresses accessibility at the network structure level, offering a network science-grounded and flexible conceptual framework for accessibility studies.

Reliable short-term demand forecasting is essential for managing shared micro-mobility services and ensuring responsive, user-centered operations. This study introduces T-STAR (Two-stage Spatial and Temporal Adaptive contextual Representation), a novel transformer-based probabilistic framework designed to forecast station-level bike-sharing demand at a 15-minute resolution. T-STAR addresses key challenges in high-resolution forecasting by disentangling consistent demand patterns from short-term fluctuations through a hierarchical two-stage structure. The first stage captures coarse-grained hourly demand patterns, while the second stage improves prediction accuracy by incorporating high-frequency, localized inputs, including recent fluctuations and real-time demand variations in connected metro services, to account for temporal shifts in short-term demand. Time series transformer models are employed in both stages to generate probabilistic predictions. Extensive experiments using Washington D.C.’s Capital Bikeshare data demonstrate that T-STAR outperforms existing methods in both deterministic and probabilistic accuracy. The model exhibits strong spatial and temporal robustness across stations and time periods. A zero-shot forecasting experiment further highlights T-STAR’s ability to transfer to previously unseen service areas without retraining. These results underscore the framework’s potential to deliver granular, reliable, and uncertainty-aware short-term demand forecasts, which enable seamless integration to support multimodal trip planning for travelers and enhance real-time operations in shared micro-mobility services.

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.

We present a method to classify street networks using only geo-tagged street-level imagery. By combining pre-trained image embeddings with unsupervised clustering, it produces visually coherent street typologies without supervised training or labeled data and requires only minimal data curation. The approach is lightweight, scalable, and, in principle, transferable across urban contexts. In a Delft (Netherlands) case study, we classify approximately 2,000 road sections using over 70,000 images. Our method recovers distinct street types such as residential, arterial, and historic ones. These results show that pre-trained visual embeddings alone can support effective street classification from visual inputs, offering a practical tool for urban planning, transport analysis, and mobility research.

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.

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.