ABSTRACT. This paper uses data from the 2023 German national travel survey to investigate residential parking location choice. A two-step modeling framework has been developed using multinomial logit models. The results show that spatial characteristics and intra-household consistency are the most influential determinants, whereas vehicle and household attributes play a minor role. Overall, private off-street parking dominates, though significant variation exists across urban contexts and socioeconomic groups. The proposed methodology can be transferred to other national travel surveys and integrated into agent-based travel demand models to improve mode choice modeling, thus supporting the design of more effective transport policies.

ABSTRACT. Demand-Responsive Transport (DRT) is increasingly introduced to improve mobility in areas where conventional Public Transport (PT) offers limited frequency or coverage. Given the high operational costs of DRT, it is crucial for its further adoption,to rigorously assess its benefits, considering not only traditional operational metrics such as waiting and travel times, but alsoaccessibility. Accessibility measures the ease of reaching surrounding opportunities such as jobs, healthcare, or education. However, data-driven accessibility computation for integrated DRT-PT services remains challenging in newly deployed territories,where historical operational data are unavailable or scarce. This paper proposes a Domain-Adaptive Multi-Task Learning (DAMTL) framework to learn job accessibility patterns fromoperational data of different “source” territories, in which DRT is already deployed. Through adversarial domain adaptation,such a knowledge is transferred to a target territory with limited data. We apply the framework to three French metropolitan areas: Lille and Strasbourg as source territories, and Orléans as thetarget. Results show that with approximately 5 weeks of DRT operation in the target territory, the accessibility estimates issuedby our method approximate well (70.1%) the estimates achievable over∼ 6 months. These results show that our method can be successfully employed in the initial phases of its deployment in a new territory(e.g., in a pilot), to estimate, from the beginning, the benefits of DRT to the accessibility of a territory. Such early accessibilityestimation can be used to confirm the pertinence of a certain DRT deployment or to diagnose some undesired accessibilitydistribution outcome and correct it via appropriate service planning intervention.

ABSTRACT. Accessibility is a central lens through which transport geographers understand potential mobility patterns, yet it is rarely used to explain realised behaviour beyond correlative associations. In this paper, we frame accessibility as a territorial benefit and show that differences in these benefits, operationalised through the Modal Accessibility Gap (MAG), can predict realised modal shares without requiring individual-level utility or cost data.

We test three variants of MAG, fit with Hansen-type, Shen-type, and Multimodal Spatial Availability (MSPAV), in an aggregated zonal model of home-work trips for the Madrid metropolitan region to predict mode share via standard regression. Of the three measures, multimodal SPAV provides the strongest predictive performance. We argue these results reflect the structural resonance between the MAG fit with multimodal SPAV model and binary logit (utility-difference) models, offering a theoretically grounded yet operationally simple approach that maintains territory as the unit of analysis.

In this way, this work provides a data-efficient multimodal accessibility methodology that may be used to forecast modal split, evaluate multimodal competitiveness, and inform policy in heterogenous metropolitan contexts.

ABSTRACT. Discrete choice models are very powerful models able to incorporate a large range of behaviours. However, the theoretical interpretability that is a key feature of these models often limits the possibility of adequately modelling the behavioural complexity behind the decision process, despite the availability of a rich dataset. An exemplary case of such complexity is the inertia effect. Several functional forms are used in the literature, each based on a specific type of information. This study exploits the potential of Neural Networks to autonomously extract the temporal dynamics of habit, leading to inertia. Specifically, the network extracts a continuous 'history-support' index that adapts dynamically to the decision environment. These effects are subsequently integrated and tested within a standard, interpretable random utility model, and the results are compared with the same effects measured in the traditional way. In an empirical application using the six-week Mobidrive panel from Karlsruhe, Germany, the results confirm that the habit component exhibits a large individual effect and significantly improves the explanation of observed choices. More interestingly, it reveals that the learned habit influence exhibits temporal decay and varies significantly by trip purpose, capturing rich behavioural dynamics that traditional static frequency measures fundamentally fail to represent.

ABSTRACT. Generative machine learning is increasingly gaining attention in the choice modelling field. By learning the underlying probability distribution of training data to synthesise new observations, these models offer a powerful framework for anomaly detection, i.e., identifying abnormal or atypical patterns in the input data. We introduce the use of variational autoencoders for anomaly detection. This is a generative model with two components, an encoder that compresses the input data into a low-dimensional latent representation, and a decoder that reconstructs the input from this latent representation to produce new data. By comparing the original data to reconstructions generated from samples of the latent space, reconstruction errors can be computed and used to flag atypical observations. We demonstrate that anomalies have a substantial impact on choice model outputs, such as values of time. This framework enables choice modellers to better detect outliers in high-dimensional datasets, thereby supporting data cleaning and model specification.

ABSTRACT. This paper develops a cyclist route choice modelling framework based on calibrated empirical travel time rather than distance alone or conventional infrastructure proxies. Using a large crowdsourced GPS dataset from Copenhagen, Denmark, we identify cruising segments in smoothed and map-matched trajectories, estimate non-parametric empirical link-level cruising speed distributions, and regularise them through hierarchical Bayesian shrinkage. We then estimate link-level delays as the difference between observed traversal time and cyclist-specific expected cruising time, again using shrinkage to ensure network-wide coverage. These cyclist- and link-specific travel times are aggregated to the route level and introduced into multinomial logit route choice models. The results show that travel-time-based specifications substantially outperform conventional distance-based models, and that the best-performing linear specification distinguishes between cruising time and delay. The framework provides a basis for future extensions toward more fundamental route costs, including, e.g., physical effort.

ABSTRACT. The shared passenger-freight transport services for urban–rural buses have been advocated to make full use of idle bus capacities during off-peak hours. Given the unstable characteristics of urban–rural passenger flow under the shared passenger-freight transport mode, how to allocate bus compartment space and ensure the service quality of both passenger and freight transport has become the critical issue. This paper proposes a flexible bus compartment space allocation method, based on which a mixed-integer programming (MIP) model aiming to minimize the total social cost is proposed to optimize the urban–rural bus timetable, compartment partition-ing scheme, and freight allocation plan. An adaptive large neighborhood search (ALNS) algo-rithm is developed to solve the proposed model. This study is expected to prioritize passenger transport services while improving the quality of freight transport services, and demonstrate the application of the proposed urban–rural bus service optimization model through case studies.

ABSTRACT. As railway networks are requested to accommodate higher traffic volumes, infrastructure managers face a fundamental trade-off between throughput, timetable robustness, and economic performance. Adding trains can increase access-charge revenue, but it can also reduce recovery margins and amplify the propagation of delays, thereby increasing performance-related penalties. This paper presents a mesoscopic simulation-based framework for assessing that trade-off at the corridor level by integrating traffic-density scenarios, conflict-resolution logic, and a simplified financial evaluation based on Track Access Charges (TAC) and Performance Regimes (PR). Using an assumed corridor and Swedish pricing parameters as an illustrative case, the analysis shows that net economic performance is non-linear with respect to traffic density: beyond a certain utilization range, secondary delays increase faster than revenue, reducing net profit. The contribution is therefore methodological: to demonstrate how operational simulation and financial assessment can be combined to identify traffic-density ranges that remain economically robust under stochastic disturbances.

ABSTRACT. Passenger flow management is a major issue for Transilien SNCF Voyageurs, which has to synchronise millions of passengers with thousands of trains daily. However, on the platforms, passengers often tend to congregate in specific areas, creating congestion. These imbalances not only disrupt passenger comfort but also affect their safety and train punctuality. Passenger positioning strategies depend mainly on the configuration of the platforms at the departure and arrival stations. In this study, we rely on these elements to propose a model that can be generalized to other stations. Our approach is based on discrete choice models, which are particularly well-suited to model this type of decision-making problem. This also allows us to quantify the impact of each factor on positioning choices.

ABSTRACT. The object of the paper is to analyze the calculation of user equilibria in static multimodal networks. The multimodal model is a static version of the GSOM (generic second order model) which is a macroscopic model in which passenger and vehicular flows are distinct but the passenger flow is subordinate to the vehicular flow. Nodes in the network represent intersections, stations, interchanges and hubs. The paper will first address the conception of the static multimodal GSOM model, in particular the issue of travel costs. Then the paper will consider progressive travel choice of travelers, which is described as a probability of route choice. This Travelers improve progressively their choice in order to achieve optimal route choice while minimizing their daily rate of choice change. This process is described as a MFG (mean field game) problem, a solution of which is proposed.

ABSTRACT. Connected autonomous vehicle (CAV) fleets are likely to transform urban route choice by replacing individual, behaviourally grounded decisions with centrally controlled, algorithmic assignment. This paper argues that such dynamics can no longer be adequately represented within the classical Wardropian traffic assignment paradigm, which assumes selfish but non-collaborative users seeking individually optimal routes. We propose a Post-Wardropian traffic assignment framework that extends day-to-day traffic assignment to mixed systems of human drivers and competing CAV fleets. In the framework, fleet operators may define choice sets, optimise arbitrary utility functions, predict the behaviour of humans and rival fleets, and assign routes strategically to maximise objectives such as efficiency, profit, or market share. By formalising these interactions, it provides a basis for analysing stability, competition, and system performance in future mobility systems where algorithmic routing will drive congestion patterns, dynamics and equilibria.

ABSTRACT. Accurate traffic flow estimation from origin-destination matrices is challenging when historical and future conditions diverge significantly — a common occurrence during network disruptions such as road closures, flooding, or public demonstrations. These are precisely the scenarios where reliable predictions matter most, yet standard machine-learning models struggle to quickly adapt to unseen graph structures with different demand patterns. This paper presents a meta-learning framework combined with a graph convolutional neural network (GNN) to address this limitation. Rather than requiring exhaustive training datasets covering all possible disruption scenarios, the architecture is trained to rapidly adapt to new network topologies and OD matrices with minimal data. Applied to simultaneous changes in road network structure and travel demand, the proposed model achieves an R² of approximately 0.85 on unseen network closures, significantly easing the burden on transport practitioners designing training datasets.

ABSTRACT. This study investigates a hybrid quantum–classical framework for urban last-mile delivery under highly constrained vehicle routing settings. Because near-term gate-based quantum devices are not yet well-suited to the end-to-end solution of constrained routing problems, we embed quantum circuits within a classical Adaptive Large Neighbourhood Search (ALNS) heuristic as local insertion operators. In the proposed method, the destroy phase remains classical, while a reduced repair subproblem is encoded and sampled using a quantum circuit. Across numerical experiments under matched shot-count settings, the classical ALNS baseline remained the most robust overall, while hybrid quantum insertion was competitive and outperformed simpler classical repair operators in selected reduced neighbourhoods when applied selectively. These findings suggest that near-term quantum value in last-mile logistics is more likely to arise from adaptive, problem-specific quantum repair mechanisms than from stand-alone end-to-end optimization.

ABSTRACT. The rapid growth of e-commerce has intensified pressures on urban last-mile delivery systems, prompting interest in alternative delivery modes such as autonomous ground robots, drones, and cargo bikes. However, the viability of these modes ultimately depends not only on logistics providers but also on consumer acceptance. This study examines stated acceptance of emerging delivery modes - autonomous ground robots, drones, and cargo bikes - using questionnaire data collected from 2012 respondents across four European cities: Dortmund, Paris, Trondheim, and Zurich. A multivariate ordered probit model is estimated jointly across the three modes, incorporating socio-demographic characteristics, attitudinal constructs, shopping behaviour, delivery-related preferences, and urban context. Results show differences in acceptance across modes and cities. Cargo bike delivery consistently receives the highest acceptance. In contrast, acceptance of autonomous ground robots and drones is lower and varies significantly across cities, with Paris showing notably lower acceptance of automated modes. Privacy concerns are a significant barrier to acceptance of automated modes, but not cargo bikes, while environmental concerns positively shape cargo bike acceptance, but not robot or drone delivery. Shopping frequency and virtual shopping engagement emerge as the most consistent positive predictors of acceptance across all three modes. The findings highlight the importance of accounting for behavioural heterogeneity and urban context when evaluating emerging delivery solutions and offer insights for policymakers and logistics providers seeking to promote sustainable and publicly acceptable last-mile innovations.

ABSTRACT. The large-scale deployment of UAV-based cargo delivery in urban environments is constrained by safety concerns, limited airspace availability, and complex regulatory requirements. While prior research has explored airspace structuring and risk-based routing independently, an integrated and operationally viable framework for UAV corridor planning remains lacking. This paper proposes a comprehensive, risk-aware framework for the design of fixed UAV cargo corridors in highly constrained airspace environments. The framework integrates regulatory compliance, spatial feasibility, and operational efficiency within a unified corridor-level planning methodology. UAV corridors are modelled as graph-based networks aligned with linear infrastructure and natural corridors, while considering varying ground risk. The framework is evaluated through multiple experimental case studies within the Dutch airspace. Results demonstrate that fixed UAV corridors can be feasibly designed under existing regulatory constraints, achieving substantial reductions in third-party risk with limited efficiency loss.

ABSTRACT. This study develops an integrated line planning framework for long-distance rail by treating passenger demand as endogenous, recognizing that both ridership level and distribution depend on service frequency and travel time. To address station heterogeneity, the methodology employs latent-class clustering to group stations into probabilistic typologies, which informs a gravity-based demand model. This is integrated into a mixed-integer linear program that builds upon hierarchical line generation and optimizes service planning while accounting for frequency-dependent transfer times. Applying this framework to the rail network of Great Britain, we conclude that demand-aware planning significantly enhances economic efficiency and service relevance compared to benchmark line generation methods and traditional fixed-demand models, respectively. Our findings demonstrate that capturing travellers' behavioural responses and structural roles of network stations leads to more realistic, cost-effective service designs that better align infrastructure supply with passenger needs.

ABSTRACT. This paper introduces a multimodal urban parcel delivery framework that integrates drones with scheduled bus services. We formulate the Pick-up-and-Delivery Problem with Drones and Scheduled Lines (PDP-DSL), in which drones serve first- and last-mile legs while parcels may use public transport for middle-mile transfers. The problem is modelled as an arc-based mixed-integer linear program. To address computational complexity and overcome well-known limitations to commercial solvers, this work develops tailored metaheuristics and feasibility checks based on Bellman–Ford for overall feasibility and dynamic programming for battery-related constraints. While a commercial solver drops in solution quality beyond 40 tasks in a synthetic case study, our ALNS with dynamic programming feasibility checks finds good quality solutions for up to 125 tasks. Results further show that in terms of routing distances, the PDP-DSL can be a suitable alternative to truck delivery and drone delivery.

ABSTRACT. On-demand meal delivery (OMD) platforms influence customer choices by how restaurants are ranked in their app interface. Dynamically adjusting these rankings offers a powerful tool for real-time demand shaping. At the same time, ensuring efficient deliveries and fair courier workload distribution requires the platform to make coordinated decisions across both demand and supply sides, including restaurant ranking and courier routing. Since demand-shaping affects order distribution---which in turn impacts courier availability---jointly optimizing these decisions can enhance overall system performance. We propose a multi-stage optimization framework that determines restaurant rankings and courier routing decisions throughout the day while accounting for endogenous customer demand, congestion effects, the order bundling strategy, and the workload fairness of couriers. Equity is incorporated into dispatch through workload-based dominance constraints. We derive the structural properties that characterize when ranking adjustments improve a congestion-aware objective and use these properties to guide a learning-to-optimize algorithm. The proposed algorithm uses offline-trained machine learning models to predict whether a candidate solution will improve the objective and by how much. Dozens of candidate solutions are evaluated in parallel, without relying on commercial solvers. This enables real-time decision-making at scale. Additionally, equity-aware order-courier matching and couriers' routing decisions are constructed using a fast insertion-based algorithm with dominance rules that balance efficiency and courier workload fairness. Computational experiments on a city-scale meal delivery network covering the entire city of Delft, the Netherlands, show that dynamic ranking considerably improves system performance relative to fixed-ranking and non-learning benchmarks, particularly under constrained courier capacity. Our method produces higher-quality solutions within seconds, and achieves at least a 7.7% improvement in the platform’s profit over the benchmark heuristic algorithm. Compared with fixed rankings, the dynamic ranking strategy increases the number of fulfilled orders by at least 9.9%. Relative to the flexible ranking approach that does not incorporate congestion effects, our method improves delivery efficiency, measured by the average click-to-door time, by 4.9% to 13.8% and achieves a more balanced citywide distribution of congestion.

ABSTRACT. The growth of e-commerce has increased parcel volumes and packaging waste, creating new challenges for sustainable urban logistics. This study examines stated acceptance of three return mechanisms: central collection, back mailing, and return during next delivery using questionnaire data from 2,012 respondents across four European cities: Dortmund, Paris, Trondheim, and Zurich. Latent class cluster analysis and a multivariate ordered pro-bit model identify consumer acceptance profiles and their attitudinal, behavioural, and con-textual determinants. Return during next delivery receives the highest acceptance, followed by central collection; back mailing is substantially less accepted. Three profiles emerge: total acceptors, selective acceptors (the majority, whose acceptance varies by mechanism), and non-acceptors. Environmental concern, social activity engagement, and shopping be-haviour consistently explain acceptance across mechanisms, while socio-demographic ef-fects remain limited. These findings suggest that packaging return systems can achieve meaningful recovery rates by targeting frequent delivery users and embedding return with-in existing delivery interactions.

ABSTRACT. Recent advances in mathematical programming have enabled exact solutions of the Capacitated Vehicle Routing Problem (CVRP) for hundreds of customers, while metaheuristics scale to thousands. A defining property of CVRP solutions is their sparsity: optimal solutions use only a small fraction of edges in the complete graph. We exploit this through a reduce-then-optimize pipeline in which a Graph Neural Network predicts which edges are likely to appear in the optimal solution, pruning the graph before solving. On 100-customer benchmark instances, retaining just 20% of edges yields optimal solutions within 1% of optimality while cutting runtime by up to 40%. Graph reduction further enables exact solution of 15% of test instances otherwise intractable on the complete graph. On instances with up to 2,000 customers, GNN-guided candidate lists improve metaheuristic efficiency by 40% without quality loss. The approach extends naturally to the CVRP with Time Windows, showing consistent gains across both solver types.

ABSTRACT. Activity-based transport models are expensive. New models or scenarios require expensive data collection, re-specification, re-calibration and estimation. Adopting models in new locations or adapting them for novel scenarios is challenging. We collect and consolidate 3.6 million worldwide household travel surveys. We train a deep generative scheduling model on these schedules. We show that this model can transfer learning about human scheduling behaviours temporally and, to a very limited extent, spatially. Potentially enabling predictions in novel settings without new data collection, re-specification, re-calibration, or re-training.

We share experiments demonstrating (i) transfer learning temporally and spatially, allowing models to perform better given additional data from other years and, to a limited extent, other places, and (ii) the ability to make out-of-context predictions in novel scenarios, such as for missing data or a new location. Our work demonstrates a promising route towards foundational behavioural models that can make high-quality predictions in novel scenarios. For certain situations, such as where standard models are infeasible due to cost, time or data constraints, such models would be valuable.

ABSTRACT. Population synthesis is a core component of agent-based transport modeling, requiring realistic individual-level populations that preserve both statistical consistency and behavioral heterogeneity. Existing methods primarily rely on structured attributes and marginal constraints, limiting their ability to capture high-level behavioral semantics. We propose SemaPop-GAN, a semantic-conditioned generative framework that integrates large language model (LLM)-derived persona representations into population synthesis. To ensure statistical validity, we introduce marginal regularization during training and a post-hoc marginal calibration procedure for deployment. Extensive experiments demonstrate that SemaPop-GAN substantially improves generative performance, achieving closer alignment with both target marginal and joint distributions while preserving sample-level feasibility and diversity under semantic conditioning. Furthermore, through post-hoc marginal calibration, we show that SemaPop-GAN remains robust under additional calibration constraints. Overall, our framework provides a principled approach to bridging semantic expressiveness and statistical consistency in population synthesis, enabling more realistic and scenario-aware transport modeling.

ABSTRACT. In this study, a generative Multi-Dimensional Transformers (MDT) framework is introduced for activity-based travel demand modelling to enable the joint modelling of daily activity sequences and Time-of-Day (TOD) choices. The limitations of traditional econometric models in capturing high-dimensional correlations and sequential dependencies are addressed by this approach. Built on the Transformer architecture, multi-head attention and masking operations are leveraged to capture nonlinear relationships while customized embedding strategies and activity-temporal sequential matrices are designed to support variable-length sequences. Then the 2019 Household Travel Survey of Shanghai is utilized for the empirical application where real-world activity sequence patterns and TOD distributions are successfully estimated and simulated. Furthermore, the heterogeneous effects of exogenous variables such as gender and household structure on activity-temporal decisions are revealed. Overall, the MDT framework provide a scalable generative tool for simulating complex activity-travel behaviours and supporting transport policy evaluation.

ABSTRACT. Mobility as a Service (MaaS) integrates multiple transport modes within a unified digital platform, yet its implementation requires coordinated decisions among users, transport service providers (TSPs), the MaaS operator, and government authorities. Existing studies typically analyze these stakeholders separately or simplify their interactions, leaving the combined effects of pricing strategies, competition, and public incentives insufficiently understood. This study proposes a bi-level optimization framework that models the economic interactions among MaaS stakeholders in a unified multimodal system. The framework integrates wholesale pricing decisions by TSPs, retail pricing by the MaaS operator, user travel choices, and government incentive mechanisms within a single optimization structure. The model is evaluated on an extended Sioux Falls multimodal network under multiple policy scenarios combining dynamic pricing and alternative incentive schemes. Results show that MaaS adoption increases from approximately 28% under static pricing without incentives to nearly 40% when dynamic pricing and coordinated incentives are implemented. Importantly, a performance-based incentive scheme achieves adoption levels comparable to fixed subsidies while requiring significantly lower public expenditure. These findings highlight that the design of incentives is more important than the total subsidy amount for achieving efficient and scalable MaaS deployment.

ABSTRACT. Freight Mobility as a Service (FMaaS) is a freight procurement platform concept that seeks to improve the efficiency and sustainability of the international freight transportation system by increasing the visibility of multimodal transport options. However, it remains unclear whether shippers and carriers are willing to adopt such a platform. Existing research is limited, often focusing on a single transport mode or a specific market segment. To examine adoption behaviour for FMaaS, we conducted 36 semi-structured interviews across diverse industries and supply chains. Thematic analysis revealed hierarchical adoption factors: Long‑term strategic decisions strongly influence shippers’ and carriers’ willingness to join multimodal freight procurement platforms, whereas tactical benefits and drawbacks are weighed when deciding whether to sign up and use them. When comparing specific platform offerings, potential users evaluate functionalities in the operational context. Overall, the study highlights multiple drivers but also significant barriers to widespread adoption of FMaaS.

ABSTRACT. Despite its potential to reduce congestion, peer-to-peer ridesharing adoption remains limited, pointing to an incomplete understanding of the behavioral determinants shaping travelers’ willingness to participate. To address this, the present study develops a stated preference survey to estimate willingness to engage in ridesharing as drivers or passengers under varying attributes. Findings indicate that students and academically educated commuters are more likely to rideshare, whereas higher income and flexible arrival times are associated with lower passenger participation. Additional time burdens like detours, walking, and waiting generate greater disutility than origin-to-destination travel time. Drivers are less sensitive to time and fuel costs when driving alone, but these factors become salient when considering ridesharing with a passenger, suggesting role-specific behavioral barriers. Results imply that platforms should prioritize matching algorithms that minimize detours, access, and waiting times. From a policy perspective, driver-targeted incentives can strengthen supply, while effective passenger-focused mechanisms remain an open challenge.

ABSTRACT. The rise in urban cycling is a promising avenue for improving both sustainability and public health in future cities. The BikeZ-ETH project seeks to deepen the understanding of cyclists' behaviour overall and, more specifically, to provide quantitative tools to evaluate, from a traffic engineering standpoint, how large-scale and massive cycling influences urban transport systems. Despite existing research, current knowledge on macroscopic bicycle flow dynamics and microscopic cycling behaviour is still lacking, signalling the need for high-resolution empirical trajectory data. To bridge this gap, BikeZ-ETH has assembled a dataset from naturalistic and controlled experiments conducted at several locations in Zurich (Switzerland) using drone-based observations. Additionally, a microscopic bicycle model built on SUMO is being actively developed to better simulate cycling by incorporating all its key characteristics, as well as interactions with other road users.

ABSTRACT. Indoor pedestrian route choice analysis in large three-dimensional spaces is difficult because Wi- Fi-based observations are noisy and path-level map matching is often unreliable. This paper proposes a unified framework based on a probabilistic link-based person-time measure derived from uncertain Wi-Fi observations. The model jointly estimates pseudo-OD-specific directed flow and perturbed-utility-based behavioral parameters, while LLM-derived semantic attributes are included as contextual covariates. Using the Shibuya Station pedestrian network, the framework closely reproduces the observed person-time field and yields stable, interpretable route choice parameters, indicating that person-time-based flow estimation provides a practical basis for indoor pedestrian behavior analysis under observational uncertainty.

ABSTRACT. Examining the route choices of shared e-scooter users allows planners to identify infrastructure priorities, understand emerging mobility patterns, and evaluate the performance of the transportation network. Studies exploring shared e-scooter route choices are, however, limited, especially when compared with bicycle route choice. In fact, no study has explored the habitual route choice behaviour of e-scooter users, for example from those who commute via e-scooter. This study addresses this by first identifying commuters from shared e-scooter GPS trajectory data in Lyon, France, and then analysing their route choice preferences. In particular, through the estimation of novel route familiarity measures, we explore i) whether shared e-scooter commuters have preferences for taking familiar routes, ii) whether preference for familiarity is experience-dependent (i.e. dependent on the travel cost of the route taken), and iii) whether commuters have decaying memory (i.e. whether they care more about recent commutes than older commutes)

ABSTRACT. With increasing usage of electric vehicles, an expansion of charging infrastructure is required. To investigate the individual’s reliance on public infrastructure expansion, we modelled charging behaviour at home, at work and at public fast charging stations based on vehicular activity profiles from a desire-based transportation model. In a high electrification future scenario for the year 2045 most drivers can incorporate vehicle charging within their usual daily schedules as most charging events take place at the home location of the driver. Only 6.3% of vehicles are reliant on public fast charging infrastructure and therefore may require the driver to adapt their schedules. Our modelling results also show the spatial and temporal distribution of charging demand over the course of one week and pointing out the potential for smart charging strategies such as vehicle-to-grid charging or peak-shaving to reduce demand on the underlying grid infrastructure.

ABSTRACT. Attitudinal change is an important factor in increasing electric vehicle (EV) acceptance and accelerating their market adoption. This paper aims to understand the processes underlying changes in attitudes and preferences towards EVs by examining individual responses to narrative-based persuasion messages, which psychological research has shown to be an effective technique to change attitudes. To represent the diversity of information individuals may encounter, we investigate the role of appeal (rational vs. emotional) and type (positive vs. negative) in narrative messages. We analyse data from a “before-and-after” stated choice (SC) experiment administered to a sample of vehicle owners in England. Participants first completed an SC experiment and an attitudinal questionnaire. In a follow-up survey, participants allocated to experimental groups were exposed to a narrative-based message about EVs before repeating the SC experiment and attitudinal questionnaire, while a control group received no messages. Our results demonstrated that narrative messages can be effective in changing attitudes and preferences toward EVs. These results can therefore help shape policy measures to support the transition toward sustainable mobility.

ABSTRACT. The rapid development of electric vehicle (EV) has driven the electrification of taxis in several major cities, which also brings a challenge to the charging infrastructure planning. Understanding the drivers’ charging behavior is crucial for optimizing infrastructure planning and improving charging efficiency. In this paper, we analyze the charging behavior characteristics of a fully electrified taxi fleet in Shenzhen, China, based on taxi GPS data and public station data. Specifically, we classify stations into commercial stations dedicated to taxi services and public stations available for all vehicles. By leveraging the trajectory and the station data, we identify charging activities and estimate the real charged energy, thereby constructing the state-of-charge (SOC) trajectories. Our results reveal that charging at commercial stations and at public stations exhibits different spatiotemporal characteristics. Although partial charging occurs at both types of stations, it is more often observed at public stations. Besides, taxis operate with battery levels above 50% for most of the time. We further classify charging stations based on their nearby POI and uncover several classes of charging stations that are more appealing to drivers. These findings provide valuable insights into charging infrastructure planning aiming at efficient utilization of charging resources.

ABSTRACT. Urban Air Mobility (UAM) operations in structured airspace face a key challenge in managing congestion. This paper proposes a departure control framework with limited rerouting that identifies high-risk paths using a conflict-based risk metric and shifts part of their demand to lower-risk existing paths through short detours. The approach is evaluated in the LAAT-Flow simulation environment under three demand allocation strategies, namely full, fixed, and dynamic allocation. The results show that all strategies reduce overall network risk. Dynamic allocation provides the most stable improvement as more paths are rerouted, while full allocation initially achieves strong risk reduction but may create new bottlenecks when demand is concentrated on alternative paths. These safety improvements come at the cost of increased energy consumption due to longer alternative paths. The findings demonstrate that limited rerouting with adaptive allocation offers a practical approach for improving safety in corridor-based UAM operations.

ABSTRACT. This study addresses the problem of estimating waiting time for vehicles queuing outside saturated parking garages, which is more relevant to drivers than traditional parking occupancy prediction. We propose a cascaded queuing framework to model the interaction between the external queue and the parking system under saturation. A saturation balance equation is derived to estimate saturation duration and individual waiting times using only vacancy data and real-time queue length. To enhance flexibility and robustness, a Physics-Informed Deep Learning (PIDL) approach is introduced to learn key system parameters while preserving physical consistency. The method is validated through both simulation and real-world data collected at the Kennedy Town Car Park in Hong Kong. Results show that the proposed approach achieves accurate waiting time prediction and outperforms baseline methods, while maintaining strong interpretability.

ABSTRACT. Urban road networks are primarily car-oriented, however, societal challenges necessitate a redesign of these systems. Cycling offers a more sustainable alternative for mobility. Here, we propose a methodological framework to design bimodal road networks for cars and bicycles. This methodology utilizes traffic assignment and mode choice models based on bikeability indicators. The design is conducted using an Inverted Network Growth method. This approach is validated on toy networks before being applied to a case study. Finally, we use this method to propose infrastructure upgrade plans for planners. The results demonstrate that the framework identifies a threshold where additional infrastructure yields diminishing returns for modal shift. Furthermore, the results show that in safe situations, the framework prioritizes aesthetics over distance when selecting roads for infrastructure upgrades. This hierarchical output provides planners with a prioritized sequence for bicycle infrastructure development that balances bikeability with network efficiency.

ABSTRACT. This study proposes a dynamic modeling framework that characterizes spatial heterogeneity as a key state variable in urban traffic systems. By using the dispersion parameter of the negative binomial distribution as a bridge, the framework captures the temporal evolution of spatial dispersion of link accumulation, interpreted as a proxy measure of network homogeneity, and its interaction with network accumulation. This relationship is further extended to describe the evolution of accumulation heterogeneity across links. The model is evaluated using simulation data under both uncongested and congested traffic conditions. Results suggest that the proposed framework can capture the dynamic evolution of network heterogeneity in both regimes. Moreover, the model provides a physically interpretable mechanism for explaining hysteresis phenomena and establishes a foundation for improved aggregate traffic modeling and control.

ABSTRACT. Optimizing trajectories of Connected and Automated Vehicles (CAVs) and signal plans requires precise real-time prediction of the Time of Arrival (ToA) at signalized intersections. Although CAVs feature advanced sensors that capture high-fidelity microscopic dynamics, their limited spatial coverage restricts the continuous observation of macroscopic queue profiles. To overcome this limitation, this paper proposes a real-time data fusion method integrating these mobile sensor data with a conventional stopline loop detector to jointly estimate queue length and predict ToA in mixed traffic environments. Specifically, the proposed method first initializes a macroscopic queue state using historical arrival patterns and data from stop line sensors. Subsequently, as the leader of the CAV decelerates, an Intelligent Driver Model-integrated Unscented Kalman Filter is triggered to continuously fuse the data of high-fidelity onboard sensors and shockwave dynamics. Under oversaturated conditions, the proposed method improves ToA prediction by reducing errors by 20.2% and 8.6% against pure macroscopic and moving-average constant deceleration baselines, respectively. By resolving the scale gap between macroscopic queue dynamics and microscopic car-following dynamics, this framework provides reliable predictions of queue length and ToA of CAVs.

ABSTRACT. The growth of Low-Altitude Air Transport (LAAT) systems calls for effective macroscopic modeling and control tools to manage dense drone traffic. Although the Macroscopic Fundamental Diagram (MFD) provides a useful framework for large-scale ground traffic analysis, its conventional formulations do not directly account for the three-dimensional interactions and path deviations that characterize drone operations. To address this issue, we introduce the concept of projected speed, which maps drone motion onto the shortest origin–destination path while preserving time-invariant trip lengths. Under this setting, we establish, with suitable assumptions, the theoretical validity of both accumulation-based and trip-based models for LAAT systems. In particular, we show that the accumulation-based model is exact at steady state, and that its mismatch under congestion is mainly caused by its inability to correctly capture transient dynamics rather than by trip-length heterogeneity itself. We then evaluate these models through extensive numerical experiments in a decentralized conflict-avoidance environment. The results verify that the proposed macroscopic models formulated in the projected space can be successfully applied to LAAT traffic and reproduce behaviors similar to those observed in ground transportation under a range of operating conditions, including hysteresis. These findings clarify the theoretical basis of macroscopic LAAT models and support their use for real-time estimation and airspace operations.

ABSTRACT. This study investigates the impact of non-traffic-induced vehicle stops - such as pick-up/drop-off activities, illegal parking, and delivery operations - on urban traffic congestion. We propose a novel methodology for identifying these stops using high-resolution drone data and machine learning techniques, including Random Forest and XGBoost. Stops are detected from vehicle trajectories and classified as traffic-induced or non-traffic-induced based on contextual features, such as stop duration and location characteristics. While non-traffic-induced stops are predominantly observed in the rightmost lane - as expected - the results further provide a quantitative characterization of their temporal and spatial patterns, enabling the identification of critical hotspots and the estimation of their impact on overall traffic dynamics. In addition, we find that short-duration non-traffic-induced stops increase lane-changing activity, circulating vehicles change lanes to avoid the stopped vehicle, which can lead to shockwave formation and reduced traffic flow efficiency.

ABSTRACT. Service areas in demand-responsive feeder systems are often defined using simple geometric shapes or existing administrative boundaries, without considering how their geometry affects system performance. This paper approaches service area design as a spatial decision problem and examines how different geometric assumptions influence operational outcomes. We propose a simulation-based optimization framework in which the service area is constructed iteratively by expanding the region based on its contribution to system performance. This allows us to compare the optimized configuration with commonly used ad-hoc geometries, including circular, rectangular, and administrative areas.

The framework is demonstrated in a case study of a feeder service in the Skotniki district of Kraków, Poland. The results show that the optimization-driven configuration outperforms the baseline geometries in key performance indicators. This indicates that simple geometric shapes may lead to less efficient designs, and that service area geometry should be treated as a decision variable in the planning of demand-responsive feeder systems, rather than as a fixed construct.

ABSTRACT. This paper examines the relationship between car accessibility and regional growth across all 290 Swedish municipalities, using accessibility data from the PIPOS platform for 2014 and 2020 and population and gross regional product data for 2020–2023. We estimate a range of regression models, varying the inclusion of control variables, the treatment of outliers, and the separate estimation for municipalities in high-growth and low-growth counties. Simple linear models show a strong positive correlation between accessibility and regional growth. However, this relationship is not robust: adding control variables reverses the effect of accessibility, remov-ing outliers severely weakens the explanatory power, and the relationship disappears entirely in municipalities in low-growth regions. Our main conclusion is that researchers using statis-tical approaches to analyse the relationship between accessibility and regional development should demonstrate that their results are robust across model specifications, as conclusions can depend heavily on methodological choices.

ABSTRACT. Older adults in rural areas are vulnerable to transport exclusion, particularly when they lose access to a car. We assess whether Demand-Responsive Transport (DRT) can mitigate these inequities in the largely rural Gunma Prefecture, Japan. We introduce two methodological extensions to the econometric accessibility module in the agent-based transport model "MATSim." First, accessibility is reformulated from a location-based to a person-based framework, allowing outcomes to depend on individuals’ home locations and sociodemographic characteristics. Second, the DRT accessibility calculation is refined to capture spatial variation in service levels, especially waiting times. We analyze supermarket accessibility for residents aged 85+, and observe substantial disadvantages among older adults, particularly rural women. Introducing a pooled DRT system (1000 vehicles) can substantially reduce disparities in accessibility across sociodemographic groups (age, gender, home location); this holds even when driver’s licenses are revoked at age 85 to improve road safety.

ABSTRACT. Access to railway stations plays a crucial role in people’s decision to travel by train . In this study, we investigated the relation between perceived station accessibility and people’s likelihood to travel by train. We use stated-preference mode choice data from the Swiss Mobility Panel, which collects data from a representative sample of Swiss residents. An integrated choice latent variable model was used to estimate the influence of perceived station accessibility on the utility of taking the train. [HE1.1] Our results show that perceived station accessibility significantly influences people’s likelihood to prefer the train over the car. Additionally, perceived station accessibility also partially moderates the effect of calculated station accessibility on mode choice, namely the effect of walking time to the station. These findings support the idea that improving station accessibility can be an alternative way to increase train ridership instead of focusing solely on improving the train network.

ABSTRACT. This study investigates how immigrant status and integration level shape transportation mode choice in Canadian cities. The analytical framework combines four elements: a continuous multidimensional integration index, mixed logit models with systematic immigrant-linked taste heterogeneity, joint revealed–stated preference estimation, and a GPS-based custom mobile app panel dataset of approximately 80,000 trip observations and 622 stated preference scenarios from 100 participants in Toronto and Montréal. A multi-stage data processing pipeline transforms raw GPS traces into estimation-ready choice sets with decomposed transit journey attributes. The mixed logit specification reveals that sampled immigrants exhibit approximately 66% lower sensitivity to in-vehicle travel time than Canadian-born residents, while higher integration is associated with reduced transit use. Counterfactual simulations demonstrate that access time reductions are approximately three times more effective than fare reductions in shifting car commuters to transit. Five-fold cross-validation yields 80–82% prediction accuracy across specifications.

ABSTRACT. Shared autonomous mobility (SAM) may exacerbate congestion by increasing traveled distances, but dedicated congestion management strategies remain relatively underexplored. This study proposes a deep reinforcement learning framework to dynamically adjust congestion charges under SAM. To represent mixed traffic conditions (privately owned and shared autonomous vehicles), we integrate a multi-region Macroscopic Fundamental Diagram with a spatial dial-a-ride model. Building on this environment, a double deep Q-learning algorithm is applied to optimize congestion charges for a fixed cordon. The results show that the proposed framework can efficiently manage congestion. Furthermore, application results indicate that the introduction of SAM leads to higher congestion, as it increases vehicle total traveled distance.

ABSTRACT. This study develops a large language model (LLM) based behavioural simulation framework to examine how tradable credit schemes perform when car use depends on a qualification threshold in daily operation. The framework combines traveller agents with LLM based decision policies, repeated intraday credit trading, and MFD based network loading in a repeated daily setting. Using a 5/8 credit regime, we show that the system approaches a stable operating state, but realised car use remains below the theoretical upper bound implied by perfect reallocation. Agent level tracing and follow up interviews indicate that threshold inefficiency is sustained by option value holding and optimistic threshold chasing, while car access is redistributed unevenly across traveller groups. These findings provide a mechanism based understanding of daily scheme performance and offer a basis for policy design and future testing of credit endowments, eligibility thresholds, and market rules.

ABSTRACT. While previous studies have examined the fairness and equity implications of alternative pricing schemes, designing an equitable congestion pricing methodology for complex multi-modal traffic systems that explicitly accounts for modal interactions and traveller heterogeneity remains a significant challenge. This study proposes a constrained, data-driven congestion pricing framework based on reinforcement learning (RL) to maximise network outflow while maintaining equity. A trip-based three-dimensional Macroscopic Fundamental Diagram (3D-MFD) simulation model is developed to serve as the RL environment, capturing multi-modal interactions among cars, buses, and walking. Traveller heterogeneity is incorporated through variations in origin–destination pairs, trip lengths, departure times, and individual values of time (VoT). Results indicate that the RL agent significantly mitigate congestion and reduce total travel time by up to 50\%, while distributing cost burdens more equitably among travellers.

ABSTRACT. By providing convenient on-demand mobility, ride-hailing services from Transportation Network Companies (TNCs) allow authorities to supplement public transport in areas with limited transit access. This paper examines local government's subsidizing strategies on ride-hailing trips under different policy goals. We develop a spatial equilibrium model that captures the interactions between commuters, drivers, and TNCs, which is further incorporated into two subsidy strategy optimization problems to maximize system efficiency and fairness, respectively. The problem is calibrated and solved using a real-world dataset from New York City through a tailored Alternating Direction Method of Multipliers (ADMM) approach with an embedded fixed-point algorithm. Our results show that fairness-driven policies prioritize subsidizing inaccessible OD pairs, whereas efficiency-driven policies are largely determined by the utility differences between competing modes. The results demonstrate an asymmetric trade-off: prioritizing fairness incurs a smaller efficiency penalty (1.0%) than the fairness loss resulting from efficiency optimization (1.6%).

ABSTRACT. Multimodal trip planners that combine public transport and shared e-micromobility face a fundamental reliability challenge: a vehicle that appears available at recommendation time may be unusable when the traveler arrives, due to battery depletion, competing demand, or individual range anxiety. This paper proposes a reliability-aware multimodal trip planning framework that embeds battery uncertainty and passenger behavioral heterogeneity directly into the recommendation problem. The model operates on a flow-based, time-expanded network and maximizes user welfare while conditioning micromobility transfers on predicted usability probabilities derived from Markovian fleet forecasts. Transfers or trips whose predicted success probability falls below an operator-defined threshold are excluded from the feasible set. The framework is evaluated on a multimodal network in Rotterdam through event-based simulation that separates planning-time beliefs from realized system conditions. The results reveal that introducing reliability thresholds reduces micromobility-leg failure rates from roughly 25\% under myopic planning to below 5\%, at the cost of about four additional minutes of walking. For longer trips, stricter thresholds reposition e-scooters as feeders to public transport rather than eliminating them.

ABSTRACT. Seamless multimodal transport systems play a critical role in developing sustainable urban mobility. We propose an incentive-based strategy as a tool for modal shift towards sustainable mobility choices in a multimodal setting under a constrained budget. A key feature of the proposed framework is that individual origin--destination trips may be completed through a genuine combination of car and public transport, reflecting the park-and-ride travel patterns observed in real urban networks. By developing an optimization framework, we seek to maximize social benefits and network efficiency, while adhering to a budget constraint, ensuring cost-effective and scalable solutions for sustainable urban mobility. The methodology is tested on a network simulating the Jätkäsaari area, a peninsula in the city of Helsinki, Finland. The results show that, at the break-even point of the cost-benefit analysis, i.e., approximately 1500 hours, the total travel time is reduced by more than 30%, while it peaks at 56.02% for a budget of 10\,000 hours. Such results correspond to an increase in total usage of public transport from approximately 11 to 23.43 percentage points, respectively. This indicates that even a small investment in the incentive policy can result in a significant improvement in the network efficiency.

ABSTRACT. This paper proposes a multi-agent deep reinforcement learning (DRL) framework for coordinating dynamic pricing and incentivization strategies in multimodal transportation networks. Two competing DRL agents are deployed: a shared mobility service provider that maximizes revenue through adaptive fare adjustment, and a public authority that allocates spatio-temporal transit subsidies to improve equity, reduce emissions, and enhance system efficiency. Unlike prior work that treats pricing and incentivization separately, the proposed multi-agent DRL formulation captures their dynamic interdependencies within a multimodal network, enabling more balanced and system-level optimal outcomes. Experiments on the Sioux Falls benchmark network demonstrate that dynamic incentivization reduces commuters’ costs by around 20%, cuts emissions by approximately 10%, and nearly doubles public transport profit during the morning peak, while the joint dynamic pricing-incentivization strategy successfully balances conflicting objectives between private and public actors. The proposed approach can support the analysis of sustainable and equitable multimodal mobility strategies.

ABSTRACT. Neighbourhoods are central to residents' well-being, yet the conceptualisation and operationalisation of their boundaries remain contested. Although prior studies have examined various determinants of neighbourhood boundary scales, empirical evidence on the role of public transport—particularly service satisfaction and inclusiveness for vulnerable groups—is limited, especially in relation to older adults who often face mobility constraints. Using the 2024 Seoul Survey and statistical modelling, this study examines associations between public transport satisfaction, service inclusiveness, and perceived neighbourhood boundary scale, and whether these differ between the young-old (65–74) and old-old (75+). Transport inclusiveness for vulnerable groups was consistently and positively associated with perceived neighbourhood scale across both age groups; overall service satisfaction was not. Older adults in areas with high local accessibility tended to perceive smaller, more localised boundaries. These findings indicate that targeted inclusiveness improvements—such as barrier-free design, step-free vehicles, and priority seating—are more effective in expanding older adults' perceived neighbourhood boundaries than general service quality enhancements.

ABSTRACT. This paper proposes a model predictive control approach to alleviate traffic congestion in urban arterials within a positive systems framework. The prediction model is based on a cumulative store-and-forward formulation, representing arterial network dynamics through cumulative link inflows and outflows. A piecewise affine outflow function is adopted, with switching conditions determined solely by current and predicted cumulative flows, ensuring the active mode is independent of the optimization variables. The network-wide traffic signal control problem, including all constraints, is formulated as an optimization problem with a linear objective function maximizing cumulative arterial outflow, with green times as control variables. Numerical results indicate that the proposed method reduces average travel time by 39.3\% compared to fixed-time control and decreases mean solver computation time by 42 seconds per cycle compared to a quadratic objective formulation, confirming real-time applicability on the Nicholson arterial in Melbourne, Australia.

ABSTRACT. Climate-related hazards, operational incidents, and extreme disruptive events are increasing the exposure of transport systems to localized and systemic failures. This paper proposes a framework for assessing the vulnerability of urban road networks to major breakdowns (such as incidents, accidents, flood-related disruptions, and extreme weather conditions). We use a static traffic assignment model here. The approach distinguishes between local effects on individual links and broader systemic impacts transmitted through rerouting and network-wide congestion spillovers. We introduce a simple composite link vulnerability indicator to capture both congestion amplification and network resilience across different demand segments. The methodology is first applied to the city of Minneapolis. Then, other urban areas are investigated and compared. The article situates this framework within a broader reflection on resilience, demand and supply shocks, information regimes, and traveller adaptation under extreme conditions.

ABSTRACT. Connected and Autonomous Vehicles (CAVs) open the possibility for centralised routing with full compliance, making System Optimal traffic assignment attainable. However, as System Optimum makes some drivers better off than others, voluntary acceptance seems dubious. To overcome this issue, we propose a new concept of Wardropian Cycles, which make the assignment fair on top of being optimal, satisfying both Wardrop’s principles simultaneously.

To reach the system optimum on a given day, drivers shall coordinate (e.g. via centralised CAV fleet dispatcher), yet to make the assignment fair, this coordination needs to span over several days. We call such multi-day coordination a Wardropian Cycle if, after a given number of days, average travel times among travellers equalise (like in User Equilibrium), while simultaneously preserving everyday optimality of total travel times (like in System Optimum). Our findings demonstrate that, under strict macroscopic conditions, such cycles always exist and complement with a greedy heuristic applicable in more general settings, asymptotically converging to fairness under optimum. We postulate a new equilibrium state of the network: Cyclical User Equilibrium, and argue that it is Pareto-optimal in the multi-day context (and dominates over the User Equilibrium).

We demonstrate the concept with large-scale simulation in Barcelona, where 670 vehicle-hours of Price-of-Anarchy are eliminated daily using cycles with a median length of 11 days—though 5% of cycles exceed 90 days. Whereas, in Berlin, only five days of applying the greedy assignment rule significantly reduces initial inequity of system optimal solution. In Barcelona, Anaheim, and Sioux Falls, less than 7% of the initial inequity remains after 10 days, demonstrating the effectiveness of this approach in improving traffic performance with more ubiquitous social acceptability.

We posit that this concept paves the way for new routing paradigms that capitalise on CAV technological advances while maintaining the Wardropian fairness of urban traffic systems.

ABSTRACT. This paper proposes the perturbed utility Markovian equilibrium (PUME) framework for stochastic traffic assignment. The demand side, the perturbed utility Markovian choice model (PUMCM), characterizes route choice as an undiscounted infinite-horizon Markov decision process in which a convex surplus function at each state replaces the additive random utility shock; its gradient yields link choice probabilities on the full simplex, and corner solutions arise endogenously at dominated actions. Fenchel--Young duality links the state-wise surplus on the policy space to a flow-space perturbation, and strict stage negativity ensures every Bellman-consistent policy proper, so policy evaluation is well posed without the need for discounting. The framework accommodates recursive logit and network GEV as special cases, and extends to α-entmax and sparsemax, which assign zero probability to dominated actions. The equilibrium is formulated as a monotone variational inequality (VI) in cost space, which accommodates asymmetric supply and reduces to the classical Beckmann-type characterization when the supply is integrable. A modified policy iteration solves the inner network loading with a global convergence guarantee; and an accelerated VI algorithm solves the outer equilibrium problem. Numerical experiments illustrate endogenous corner solutions and approximately linear runtime scaling in network size.

ABSTRACT. This study examines how Vehicle-to-Grid (V2G) can either strengthen or weaken the resilience of electric demand-responsive transport (e-DRT) under blackout risk. We develop a blackout-aware resilient operation framework that evaluates fleet decisions over a 16-hour horizon under blackout uncertainty. Before blackout realization, the operator chooses routing, charging/discharging, reserve state of charge, and vehicle pre-positioning; after the disruption materializes, the fleet adapts to emergency power supply and priority transport under the realized outage conditions. The underlying formulation uses a preparedness-response decomposition with scenario-based post-disruption evaluation. Numerical experiments on a 20-node system with four EVs and four critical facilities report a representative 14:00--18:00 blackout realization, in which a resilience-aware V2G policy outperforms a profit-oriented V2G strategy. Compared with profit-oriented control, the proposed policy increases passenger service from 46.8\% to 54.8\%, priority-trip fulfillment from 60.0\% to 80.0\%, and critical-load supply from 38.4\% to 42.6\%, while also improving blackout-onset fleet SOC and the day-level objective value through faster recovery. The results indicate that V2G supports urban resilience only when reserve energy and vehicle positioning are explicitly coordinated across preparedness and post-disruption response.

ABSTRACT. Charging for on-street parking is a common policy instrument to regulate car use in cities, yet its effectiveness depends on drivers’ responses to prices. We study an innovative policy reform, an emission-based parking prices implemented in Lyon in June 2024. Exploiting this natural experiment, we combine ten million parking transactions with vehicle registration data before and after the reform. We use a continuous DiD framework, building on recent econometric advances for heterogeneous treatment effects with continuous doses. Results show a significant and persistent reduction in parking demand. Responses are heterogeneous and increase with the magnitude of the price changes. More polluting vehicles are disproportionately exposed to higher price increases and account for a larger share of demand reduction. To our knowledge, this paper provides the first empirical evidence on emission-based parking pricing, suggesting it can curb high-emission vehicle use and mitigate environmental externalities.

ABSTRACT. Personal carbon budgets have been discussed as a measure to reduce CO2 emissions, but can face resistance by the public, if perceived as unfair. One approach to increase fairness is social strat- ification, where carbon budgets are allocated based on individual circumstances. However, social stratification creates additional administrative costs, which are often overlooked by the public. This study investigates the perceived fairness of different designs of personal carbon budget schemes. Using a vignette experiment, respondents evaluate six scenarios that vary in allocation (equal vs. stratified) and amount of budget that can be traded, both before and after receiving information about the costs of stratification. By using a hierarchical Bayesian cumulative logit model, we find that on average scenarios with social stratification are perceived as fairer than equal allocation ones and that making respondents aware of the administrative cost of social stratification does not change the perceived fairness of carbon budgets.

ABSTRACT. To further improve the development of electric vehicles (EVs), this research proposes a model based on continuous approximation (CA) for the optimal planning of shared charging stations. By considering spatial heterogeneity and multi-types of chargers, the planning optimization problem is formulated as a mixed-integer nonlinear optimization problem (MINLP) with objec-tive to minimize the total time spent on charging related activities, which are composed of two parts named travel time and waiting time, under both budget and power supply constraints. A new planning variable, number of fast chargers per station has been incorporated with density and capacity of the charging station. Results from Barcelona case study show strong potential of the proposed planning model on reducing overall total time spent on charging related activi-ties with less investment budget compared with the current layout, highlighting the significance of considering spatial heterogeneity when planning shared charging facilities.

ABSTRACT. The decarbonisation of long-distance freight transport represents a considerable challenge, particularly in those transnational corridors characterised by high levels of road traffic. In this context, hydrogen-powered heavy-duty vehicles are increasingly regarded as a potential solution to this challenge. However, this is contingent upon the availability of hydrogen production pathways. This study uses a stochastic modelling approach to evaluate the deployment of hydrogen for freight corridors in uncertain conditions and its associated decarbonisation potential. The approach combines freight activity data at the corridor level with technology penetration and emission factor data in a Monte Carlo simulation model. The approach is employed to model the Brenner corridor, a significant freight corridor in Europe. The results indicate that emission reductions are contingent on hydrogen production pathways, varying from 13.5% to 20.5%, with the most likely value at 17%. The findings highlight the importance of explicitly accounting for uncertainty when evaluating hydrogen-based decarbonisation strategies.

ABSTRACT. This study assesses how vehicle integrated photovoltaics (VIPV) affect urban electric vehicle (EV) charging demand and public charging infrastructure planning. It combines an activity-based MATSim model of cost aware and adaptive charging behavior with a separate mixed-integer optimization model for planning public chargers, stationary photovoltaics, and battery storage. A case study for Gothenburg, Sweden compares a baseline without VIPV to a scenario in which 50% of EVs have onboard solar generation. VIPV reduces annual grid electricity demand by about 6.0% and total charging demand by about 5–6%, with the largest effects during high-solar periods. The optimization results imply an average annual reduction in public charging expenditure of about 1,300 SEK per VIPV. VIPV therefore lowers grid dependence and user charging costs, while highlighting trade-offs between decentralized energy provision and charging network revenues.

ABSTRACT. This study aims to advance the methodological capabilities of agent-based transport simulation models to improve the accuracy of simulated traffic volumes and generate electric vehicle travel demand for further charging studies. It presents an extension to the synthetic population and activity-based demand generation of an existing MATSim model for Greater Melbourne by incorporating a household-level car availability and assignment for both regular vehicles and electric vehicles and for people living in different types of dwelling and household. These contributions are needed because growing electric vehicle adoption and dwelling-type differences drive heterogeneous charging behaviour. Within-household interactions shape individual travel demand and, in turn, electric vehicle energy consumption, both of which are key to charging demand estimates and infrastructure planning. The findings highlight the potential significant benefits in travel demand generation and power grid load prediction using transport models when accounting for the complex mechanisms of household resource constraint, assignment and behaviour.

ABSTRACT. Truck operators replacing diesel fleets with electric alternatives face a sequential, irreversible investment problem whose structure makes a single optimal rollout plan inadequate for strategy. This paper develops a framework that distinguishes two sources of decision variance: within-scenario flexibility, the room to resequence or defer commitments inside a given future, and cross-scenario sensitivity, the degree to which the preferred strategy changes across futures. We show theoretically, using real options and mean-preserving spread arguments, that flexible operators with independent adoption choices should exhibit more within-scenario latitude, while inflexible operators constrained by bundled commitments should face more cross-scenario exposure. A Probabilistic Mixed-Integer Programming (PMIP) formulation makes the decomposition observable. Stylized experiments confirm the prediction: the inflexible operator's cross-scenario variance is twelve times larger than the flexible operator's, with virtually no within-scenario latitude, while the flexible operator retains meaningful room for manoeuvre within each future.