ABSTRACT. Cooperative, Connected and Autonomous Mobility (CCAM) promises to improve transport efficiency and sustainability, yet people’s intention to use them remains unclear. This study investigates socio-cultural and psychological determinants of CCAM adoption across six European countries, focusing on factors such as Technological Affinity and Trust in automation. It examines both shared and non-shared autonomous mobility solutions using an integrated model based on the Technology Acceptance Model (TAM) and the Theory of Planned Behaviour (TPB). First survey responses show that the core TAM mechanisms generalise across both modes. However, psychological concepts differ be-tween both modes as extending the model with Subjective Norms revealed significant effects for both groups, with a stronger influence for non-shared autonomous mobility. These findings suggest that while core TAM dynamics generalise across AV types, non-shared autonomous mobility adoption is more sensitive to social influence and additional factors beyond TAM.

ABSTRACT. Accurately predicting cycling demand and understanding its determinants are vital for promoting sustainable transportation. This study uses a large-scale revealed-preference dataset of over 54 million bicycle passages collected from 51 automated counters in Montreal at hourly resolution. A comprehensive set of variables, including temporal factors, weather conditions, accessibility, infrastructure, land use, and socio-demographics, is integrated to develop a comprehensive dataset. An interpretable ensemble learning approach is used for modeling. Among the prediction models, CatBoost achieved the best performance (R² = 0.78). Subsequently, two interpretation techniques are used to capture the influence of different factors on cycling demand. The results show that temporal, weather-related, and accessibility factors have the strongest relative influence on cycling demand. Further, the nonlinear relationship between these factors and the demand is illustrated, showing that maximum demand is associated with commuting peaks, higher temperatures, lower humidity, greater proximity to employment and childcare centers, and working days.

ABSTRACT. Innovative Air Mobility (IAM) is expected to enhance regional connectivity. However, its successful deployment depends on understanding who is willing to adopt IAM, how much demand it can realistically capture, and the extent to which it can generate new trips. To address this, this paper develops a hybrid behavioural–big data framework to model IAM demand. A stated preference survey is used to estimate a multinomial logit model. The behavioural model is then transferred to large-scale origin–destination flows derived from mobile network data. The methodology is applied to different OD pairs in the Catalonia region, Spain. Results show that IAM uptake varies significantly across corridors, reaching up to 22.6% in intra-regional trips but remaining below 7% in longer corridors, with demand primarily substituting car travel. Induced demand emerges only under low pricing scenarios and is concentrated among high-income, time-sensitive users, highlighting IAM’s role as a premium, niche mobility service rather than a mass transport solution.

ABSTRACT. This paper estimates how changes in road supply, non-car mode supply, and destination densi-ty affect individuals’ vehicle kilometres travelled (VKT). Using population-wide Swedish reg-ister panel data for 1998, 2005, and 2017 matched to historically reconstructed transport net-works and simulated generalized travel times, we construct logsum-based accessibility measures by mode. Methodologically, we combine long-horizon first-difference models with an instrumental-variable strategy based on accessibility changes around the initial residential zone to address spatial sorting and time-varying endogeneity. We show that pooled cross-sectional models substantially overstate behavioural responses relative to first-difference and IV estimates. The most robust result is a modest reduction in VKT from improvements in non-car accessibility, while direct effects of road supply and density become small once sort-ing is accounted for. Improved non-car accessibility modestly reduces VKT: a one-standard-deviation increase in relative accessibility lowers car use by 1–3.5%.

ABSTRACT. This study provides a first psychometric examination of the Perceived Mobility Need Satisfaction (PMNS) scale across multiple transport modes and tests its predictive validity for transport use and willingness to pay. Using data from an online mobility survey at the University of the Bun- deswehr Munich, 143 respondents with complete PMNS ratings across five transport modes con- tributed 715 mode-specific evaluations. Depending on the degree of between-person dependency, subsets of PMNS items were analysed using CFA or MCFA. The results indicate that the original PMNS structure required minor refinements, but yielded interpretable and psychometrically ac- ceptable measurement models. Safety and comfort showed meaningful within- and between-per- son variance, whereas autonomy, competence, and money primarily reflected mode-specific eval- uations. PMNS-derived factors further predicted cumulative travel distance and willingness to pay. Overall, the findings support the feasibility of assessing perceived mobility need satisfaction across transport modes and underline its relevance for understanding use and acceptance beyond behavioural indicators alone.

ABSTRACT. Autonomous taxis and shared autonomous mobility are expected to transform urban transport systems, yet their adoption depends on user acceptance, particularly of ride-sharing services. This study investigates preferences for conventional taxi, autonomous taxi, and shared autonomous taxi for home-to-airport trips in Luxembourg, with a focus on preference heterogeneity. Using data from a stated preference experiment with 1,373 respondents, we estimate both a Multinomial Logit (MNL) model and a Latent Class Choice Model (LCCM) to identify distinct behavioral segments. The results show that cost, travel time, and waiting time significantly influence choices, with waiting time having a stronger impact than in-vehicle time. The LCCM reveals two segments: a privacy-sensitive group that is more cost-sensitive and strongly reluctant to use shared autonomous taxis for longer trips, and a sharing-oriented group that is more open to shared autonomous mobility. Trip length plays a critical role, with shared options becoming less attractive for longer journeys. These findings highlight the importance of accounting for preference heterogeneity and suggest that shared autonomous mobility may face adoption barriers for longer, purpose-driven trips such as airport access.

ABSTRACT. The 15-minute city is commonly operationalized through objective proximity measures, yet less is known about perceptions about which activities matter most to have nearby, especially in hot-arid settings. This paper examines accessibility priorities in Abu Dhabi through an importance-based ranking of nearby amenities. We estimated a hybrid choice model with an exploded logit structure and latent variables capturing neighbourhood satisfaction and lifestyle. The model incorporates purpose-specific willingness to walk under nice and hot weather. Results show that groceries and work/school dominate overall rankings and prioritizing nearby access is associated more strongly with existing walkability practice than with activity frequency. Weather also matters: greater willingness to walk in pleasant weather reduces the urgency of nearby location, whereas willingness to walk under hot conditions identifies purposes that remain essential despite heat. The findings support a perceived-accessibility interpretation of the 15MC in which local accessibility is purpose-specific, socially differentiated, and climate-conditioned.

ABSTRACT. Micro-mobility emerges as a promising solution for reducing car dependency in urban en-vironments, providing an alternative and environmentally friendly way of commuting. This study investigates individuals’ preferences for using micro-mobility modes within a micro-hub-based mobility system and identifies the behavioural and attitudinal factors in-fluencing their adoption. A survey with stated preference experiments was distributed in the United Kingdom, resulting in 500 responses. A Hybrid Choice Model with two latent variables, representing digital comfort and perceived micro-mobility hub attractiveness, was estimated and provided significant insights into individuals’ choice behaviour. Results show that waiting time, helmet provision, charging level, and contextual conditions such as weather and bicycle lane coverage significantly influence individuals’ final choice. Final-ly, digital comfort and micro-hub perceived attractiveness significantly influence commut-ers’ behaviour, with higher digital comfort and more positive perceptions of micro-mobility hubs exhibiting a greater propensity to adopt micro-mobility services.

ABSTRACT. The aviation industry faces increasing pressure to decarbonize as air traffic growth out-paced past efficiency gains. Sustainable Aviation Fuels could reduce emissions, yet adoption remains limited due to currently high costs and infrastructure constraints. As such technological progress alone will not achieve short-term results, behavioural interventions, particularly those targeting passenger choices, characterize an underrepresented lever. Existing research has focused mainly on nudges, while incentive mechanisms remain largely unexplored. This study empirically investigates the impact of incentives on passengers' willingness to choose more cost intensive sustainable flight options. Building on previous qualitative findings, a large-scale Discrete Choice Experiment with 1250 recent passengers from the DACH region quantifies trade-offs between green premiums and incentive pack-ages. Hypotheses predict that time saving and socially visible rewards increase uptake better than monetary incentives. The present study aims to identify how incentive-based interventions can enhance voluntary climate engagement in air travel and complement ongoing regulatory and technological efforts.

ABSTRACT. Designing public transport systems resilient to unplanned disruptions requires understanding mode and route choice preferences during such events. This study, for the first time, uses Automatic Fare Collection data to analyse multimodal public transport route choice adaptations to unplanned metro disruptions. Methodologies are developed for identifying commuters and their usual commute route, approximating home/work locations, and identifying disrupted trips and onward post-disruption routes. These methodologies are operationalised in a case study of Lyon, France, where 4569 post-disruption routes are identified, including routes taking bridging buses (bus replacement services). A novel Latent Class Conjunctive Bounded Path Size (LC-CBPS) model is employed to model the route choices, inaugurally exploring both a) whether there are any latent classes of choice preferences, and b) whether travellers impose bounds upon different route attributes in their route consideration. Goodness-of-fit of the LC-CBPS model is benchmarked against other models, and estimated attribute preferences and bounds are analysed.

ABSTRACT. Decarbonising long-haul road freight requires suitable on-road charging infrastructure, yet plug-in charging, battery swapping, and electric road systems (ERS) differ substantially in service time, throughput, cost structure, and their relative value across corridors. Existing studies tend to evaluate these alternatives in isolation or treat them as broad substitutes, without capturing the freight-specific operational constraints and market interactions that shape their allocation in practice. This paper develops a corridor-level equilibrium framework in which carriers minimises general cost and facility providers maximises profit, with queueing effects, the hours-of-service rule, and ERS as a corridor layer that reduces stop-based charging demand. Using a case study for Bavaria, Germany, and Austria, the model yields three main findings. First, the baseline equilibrium is strongly plug-in oriented in aggregate, but swapping remains active on a limited set of short and dense corridors. Second, a higher value of time broadens the corridor range in which swapping remains competitive. Third, higher charging power mainly affects longer corridors, where it further strengthens plug-in charging. Overall, the results suggest that charging allocation is corridor-specific rather than universal, and that infrastructure decisions should be evaluated as part of an interacting corridor system rather than as isolated technology choices.

ABSTRACT. Autonomous vehicle (AV) crowdsourcing service enables privately-owned AVs to rent out their vehicles to serve other travelers during idle periods. Existing literature suggests that this business model holds potential benefits for travelers, AV owners, and the operator, yet the analysis has mostly overlooked the heterogeneity in the spatiotemporal supply pattern. Motivated by this gap, we propose a time-expanded network model that jointly optimizes the AV rental price and vehicle dispatching, where the private AV supply is characterized by the idle periods and available locations of different classes of AV owners. With the proposed model, we numerically investigate the operational feasibility of an AV crowdsourcing service and identify key factors that influence service performance using a stylized two-region model. We further conduct a case study of Chicago based on the household travel survey and ride-hailing trip data. The results not only offer practical insights for service operators but also provide regulators with a better understanding of the potential impact of AV crowdsourcing on the citywide travel patterns and traffic dynamics.

ABSTRACT. Urban logistics frequently relies on two-level distribution networks, where large vehicles deliver goods to intermediate facilities from which smaller vehicles complete last-mile deliveries in access-restricted city centers, where travel times vary considerably throughout the day. Fluctuating traffic conditions directly affect route planning and time window feasibility. Yet most studies on vehicle routing simplify or ignore this effect, assuming static travel times throughout the planning horizon. In this paper, we propose a branch-and-price algorithm for the two-echelon vehicle routing problem with time windows and time-dependent travel times. A baseline model is formulated and evaluated through a preliminary computational study, yielding promising results that demonstrate the viability of the approach. We further discuss the limitations of the current model and outline targeted improvements aimed at solving instances of practical size to optimality, as well as establishing solid benchmarks for emerging heuristics.

ABSTRACT. Urban freight distribution faces highly variable traffic conditions, where recurring congestion and unexpected disruptions can undermine delivery reliability and efficiency. This study develops a traffic-aware optimization framework for middle-mile urban logistics through a Stochastic Time-Dependent Vehicle Routing Problem with Time Windows (STDVRPTW). The model integrates time-varying traffic conditions, stochastic travel-time disruptions, and customer service requirements within a unified framework. It is formulated as a two-stage stochastic program in which first-stage routing and scheduling decisions are evaluated across multiple traffic scenarios. To solve the large-scale problem efficiently, we propose an improved Benders decomposition algorithm with problem-specific valid inequalities that exploit the structure of time buckets and vehicle capacity constraints. Numerical results show that the proposed algorithm outperforms Gurobi and standard Benders decomposition. The findings also provide operational insights into how congestion pricing, delivery time-window flexibility, and fleet deployment interact, showing that pricing policies can encourage off-peak deliveries and changes in fleet utilization.

ABSTRACT. Conventional last-mile accessibility assessments often assume isotropic motion, neglecting the impact of physical barriers and cognitive pathfinding on accessibility. This research investigates the last-mile mobility by assessing how urban barriers and frictions disrupt last-mile accessibility. The study presents a hybrid framework integrating high-resolution LiDAR data with large language modeling (LLM). LiDAR data locates barriers and measures barrier impedance, while LLMs serve as generative navigational agents to simulate barrier-adaptive decision-making. This two-tiered methodology transforms unprocessed spatial limitations into accessibility metrics within space-time prisms. Findings based on two comparative case studies reveal that conventional models omitting physical barriers have significant accessibility overestimations, which advocates for human-centered designs in non-idealized urban areas.

ABSTRACT. In this paper, we integrate route choice models into capacitated line planning with endogenous demand. The resulting problem is formulated as a bilevel mixed-integer program, where route choices are modeled using sampling-based techniques, yielding a flexible but computationally challenging framework. We propose two single-level reformulations: an arc-based formulation relying on strong duality, and a path-based formulation. Surprisingly, numerical experiments indicate that, under specific assumptions, the path-based formulation consistently outperforms the arc-based one.

ABSTRACT. Public transport (PT) is a key component of sustainable urban mobility. While the effect of perceived safety on satisfaction has been widely studied, its impact on actual passenger behaviour remains largely unexplored. This study combines observed route choices from smart card data in Eastern Denmark with responses from a large-scale survey of 3,583 train passengers to examine how perceived station safety influences route choice. Discrete choice models estimated separately for morning and evening trips show that safety has a significant behavioural impact, equivalent to a perceived reduction of approximately 6–10 minutes of bus in-vehicle time. Demographic interactions reveal no significant differences between men and women, whereas young adults display varying sensitivity to safety throughout the day. Overall, the findings highlight the importance of perceived safety in shaping route choice within multi-modal PT networks.

ABSTRACT. The presence of micro-mobility vehicles (MMVs), such as bicycles and e-scooters, is rapidly increasing in spaces shared with pedestrians. Previous studies have mainly focused on pedestrian interacting with MMVs on sidewalks. However, more complex interactions in areas such as pedestrian streets remain underexplored. This study examines pedestrians interacting with multiple MMVs in pedestrian streets. Data were collected from a virtual reality (VR) experiment under scenarios that varied in initial lateral distance, MMV type, number of MMVs, and density levels. Lateral clearance, longitudinal distance, perceived difficulty, risk, and expected accidents were analyzed using statistical tests and a linear mixed-effects model. The results show that the lateral clearance is larger with an initial lateral distance of 0.6 m than 0 m, with bicycles than e-scooters, and with a single MMV than multiple MMVs. The longitudinal distance is larger in single MMV scenarios. These findings can inform the design of safer and more comfortable pedestrian streets.

ABSTRACT. This study presents a trajectory-based validation framework for cyclist platoon detection using high-resolution data from the DLR Urban Traffic Dataset. Cyclist trajectories are analyzed to capture arrival patterns at an urban signalized intersection. A fuzzy C-means (FCM) clustering algorithm is applied to identify cyclist platoons based on spatial and temporal proximity.

To enable quantitative evaluation, a reference dataset of cyclist platoons is constructed using deterministic geometric and temporal criteria derived from upstream crossing events. The detected clusters are compared with the reference platoons to assess the accuracy and robustness of the clustering approach.

The results provide insights into cyclist group formation and highlight the relationship between upstream spatial clustering and platoon formation at the intersection. The study establishes a practical framework for validating clustering-based platoon detection using empirical trajectory data.

ABSTRACT. As e-bike ownership and use grow in Europe and beyond, mass adoption could represent a substantial shift in how people travel and how they perceive cycling. While much of the research on e-bikes has focused on substitution effects with other modes, notably the car, this paper investigates how adoption affects cycling practices. Using three waves of the Swiss Mobility Panel from 2020 to 2025, we apply a random-intercept cross-lagged panel model (RI-CLPM) to estimate the effects of switching from a conventional bike to an e-bike on cycling frequency and cycling attitudes. Because the RI-CLPM disentangles between-person and within-person effects, the results confirm that those who adopt e-bikes increase their cycling frequency and develop more positive cycling attitudes. Furthermore, these effects grow stronger over time. These findings suggest that even among current cyclists, e-bikes can further expand cycling practices.

ABSTRACT. Car sharing is frequently promoted as a means to reduce private car ownership, yet empirical evidence on its actual effects remains mixed. This study examines the relationship between car sharing and household car ownership using a pseudo-panel approach based on four waves (2022–2025) of the Dutch Landelijk Reizigersonderzoek (LRO). A first-order Markov transition model is estimated to capture both baseline car ownership and changes over time, while accounting for state dependence and observed heterogeneity. The results indicate that car sharing adoption is associated with lower baseline levels of car ownership. At the same time, adopters exhibit substantially higher transition rates in car ownership compared to non-adopters. Importantly, the direction of these transitions depends on initial car ownership. Among multi-car households, car sharing is associated with a higher likelihood of reducing the number of cars, whereas among car-free and single-car households it is associated with an increased likelihood of acquiring cars. Distinguishing between Business-to-Consumer (B2C) and Peer-to-Peer (P2P) car sharing reveals heterogeneous effects, with B2C car sharing showing stronger associations with both upward and downward transitions. These findings suggest that car sharing does not uniformly substitute for private car ownership, but rather contributes to increased volatility in car ownership trajectories.

ABSTRACT. This study aims at identifying which mobility solutions are most suitable for addressing the particular needs of people with psychiatric disabilities. We developed a 3-step evaluation framework: (1) development of a dataset (operator data and qualitative monitoring), (2) analysis of supply and demand of mobility solutions for people with psychiatric disabilities, and (3) processing of a multi-criteria decision analysis. This framework is used to analyze the ‘PAM’ service, an on-demand mobility solution specifically intended for people with disabilities in Seine-Saint-Denis (Paris region, France). The results highlight the strong performance of on-demand transport services in supporting routine mobility needs, while underscoring the importance of robustness to preference and evaluation uncertainty in service assessment. From a policy perspective, the framework provides local authorities with evidence-based recommendations to compare mobility solutions and support the design or renewal of services better aligned with the specific needs of people with psychiatric disabilities.

ABSTRACT. Urban road networks generate increasingly rich long-term traffic datasets, yet their full temporal depth remains underexploited. Most retrospective studies either aggregate data over long periods or perform before–after comparisons tied to pre-identified events, missing any unanticipated change. This paper proposes a general unsupervised framework for automatically detecting all dates at which a structural or relevant change occurred in urban traffic performance. The method combines a set of MFD-based macroscopic indicators and microscopic indicators — capturing the distribution of speed, flow, and density across individual road segments — with a hierarchical kernel-based change point detection algorithm operating at two complementary spatial scales. Applied to seven years of loop detector data in Rotterdam (2016–2023), the framework identifies both major network-wide disruptions — including the COVID-19 mobility collapse — and subtler, spatially localized reorganizations invisible to global aggregation, each assigned an objective impact score. The resulting dated and impact-scored record of network behavioral shifts offers urban practitioners a systematic tool to audit their traffic data, evaluate the effect of past interventions, and identify stable periods suitable for further modelling.

ABSTRACT. As automated vehicles (AVs) enter mixed traffic, proactively anticipating human driving behavior during interactions like lane changes is essential. Classical Evolutionary Game Theory (EGT) assumes driver independence, predicting unrealistic full cooperation that contradicts the stable 42% cooperation rate observed in real-world Waymo Open Motion Dataset (WOMD) trajectories. To resolve this discrepancy, this study introduces a Quantum Game Theory (QGT) framework utilizing the Marinatto-Weber quantization scheme. By embedding latent correlations between drivers into a single interaction's payoff structure via an entanglement parameter, our model accurately reproduces the observed mixed equilibrium. Furthermore, simulations of various AV deployment strategies reveal that human adaptation depends critically on the underlying AV algorithm. This framework enables stakeholders to simulate repeated interactions and anticipate how human drivers will evolve in response to specific AV software designs, ensuring safer mixed-traffic integration.

ABSTRACT. This study investigates the use of Maximum Entropy Inverse Reinforcement Learning (IRL) to infer the underlying objective functions of Variable Speed Limit (VSL) systems. The objectives of such systems are often unknown or not explicitly accessible, limiting systematic analysis, comparison, and further development. Using a controlled simulation environment with a Model Predictive Control-based upstream speed reduction scheme, we evaluate whether IRL can recover the underlying objective from observed control behavior. Different reward function formulations are considered to assess the approach's robustness. The results show that IRL achieves high policy imitation accuracies across all tested settings. While the control policy can be recovered from only observed traffic-state features, incorporating domain knowledge via engineered features improves the interpretability and accuracy of the learned reward structure. Overall, the findings demonstrate that IRL is a promising tool for uncovering implicit traffic control objectives, enabling systematic analysis and simulation of VSL strategies.

ABSTRACT. We propose a probabilistic game-theoretic controller (PGC) for autonomous vehicles on lane-free roads in decentralized and communication-free environments. At each time step, each vehicle solves a nonlinear Model Predictive Control problem and predicts the behavior of nearby vehicles through multi-level game-theoretic reasoning. We represent interaction uncertainty using structured trajectory hypotheses, and we incorporate collision risk through a Conditional Value-at-Risk-based cost term and iterative risk allocation constraints. We then compare the proposed PGC with the deterministic counterpart. The results show that the PGC handles uncertainty more effectively. In particular, it maintains safety while reducing unnecessary hesitation, avoiding overly conservative maneuvers, improving overtaking behavior, and increasing speeds along track. This study establishes a novel framework for probabilistic lane-free autonomous vehicle control under uncertainty.

ABSTRACT. Supply chains exposed to long-run climate stress face a governance problem that conventional resilience frameworks are not well-suited to address. This paper proposes a viability-theoretic framework for supply chains under heterogeneous external stressors, illustrated through the arabica coffee supply chain. The framework combines system dynamics with viability theory to specify a four-layer state space, physical, economic, social, and compliance, a non-compensatory constraint set, and a bounded firm-level control set. Rather than computing the viability kernel numerically, the framework establishes the structural conditions under which such computation is defined and well-posed. The Arabica coffee supply chain is used to instantiate each component with domain-specific variables, feedback structures, and control instruments. The paper concludes that stressor heterogeneity, asymmetric erosion dynamics, and margin-constrained control capacity are structural properties that composite resilience indices cannot capture, and that the viability kernel represents a more analytically adequate governance target for supply chains under prolonged climate stress than a recovery trajectory or resilience index.

ABSTRACT. Rising maritime trade demand has exacerbated the challenges faced by drayage operators in inland container transportation. Drayage operators need to make precise truck scheduling decisions daily under truck appointments. Hydrogen-powered container trucks (HCTs) are promoted in container drayage, which brings further constraints for scheduling because of hydrogen refueling. A Mixed Integer Linear Programming (MILP) model for the container truck scheduling problem is proposed, which considers hydrogen refueling and soft time windows, aiming to minimize the total operational cost. To solve the proposed model, an improved Adaptive Large Neighborhood Search (ALNS) algorithm is designed, which is integrated with the Stochastic Local Search (SLS) and customized operators. Case studies demonstrate the effectiveness of the proposed model and algorithm. A novel perspective on container truck scheduling problems will be offered by the proposed model.

ABSTRACT. The electrification of bus fleets is a systemic transition that goes beyond the acquisition of vehicles and charging infrastructure, requiring new operational practices and planning approaches for effective implementation. At the tactical level planning, vehicle scheduling must at least consider depot, charging, and time-window constraints, as well as electric-vehicle-specific characteristics such as battery capacity, energy consumption, and charging power rates. This study extends the Electric Bus Multi-Depot Vehicle Scheduling Problem with Time Windows (EB-MD-VSP-TW) by introducing continuous-time modeling at charging stations, aiming to improve the fidelity of charging operations in the calculated solutions. The proposed model is evaluated using both synthetic instances and real-world bus lines from Athens, Greece. Given the continuous-time EB-MD-VSP-TW formulation, a heuristic solution approach is also proposed to produce feasible schedules for a set of four operational bus lines.

ABSTRACT. In activity-based models with household-level schedule choice, estimating discrete choice models requires generating realistic alternative schedules beyond those observed in finite samples. We propose a Metropolis–Hastings sampling algorithm for household schedules that is theoretically justified as a Markov chain Monte Carlo method. We define a compact schedule state space and a minimal set of elementary schedule-editing operations that jointly respect (i) spatio-temporal continuity, (ii) household-specific time-window constraints, (iii) asymmetry between licensed drivers and unlicensed passengers in pick-up/drop-off behavior, and (iv) limits on the number of privately owned vehicles. Using tools from stochastic process theory, we prove that the Markov chain induced by these operations is irreducible, aperiodic, and positive recurrent, thereby establishing ergodicity. The proposed framework provides a rigorous foundation for importance sampling of household schedules in OASIS-type activity-based models and paves the way for more efficient estimation and robust policy analysis.

ABSTRACT. As a low-carbon transportation mode, e-bikes are promising for the ecological transition of the transportation sector. Even though they are fairly common in several regions of the world, they are mainly used by "early adopters" in North America, particularly in the Quebec province of Canada. To evaluate the effectiveness of an e-bike trial in shifting people's attitudes towards e-bikes and the barriers that prevent them from e-cycling, we use the data from the Velovolt project in Québec. These include both pre- and post-surveys as well as GPS data. We find that the way participants actually used their e-bikes and the characteristics of their cycling environment had much more impact on their intention to buy an ebike than their socioeconomic characteristics. This shows that transport planners should favor the development of infrastructure and e-bike-trial programs to encourage e-bike adoption.

ABSTRACT. Designing climate-resilient public transportation requires data-driven tools that can identify vulnerabilities, measure infrastructure performance, and support targeted policy decisions, yet such tools remain scarce. This study addresses that gap by developing novel indices for climate-adaptive shared mobility planning: the Weather Resilience Transportation Index (WRTI), which quantifies station-level sensitivity to climate stress, and the Weather Resilience Infrastructure Factor (WRIF), which measures infrastructure-mediated resilience under varying weather conditions. Both indices are developed within the Hybrid Dynamical Systems Thinking Approach (HDSTA), integrating systems thinking, Object-Process Methodology (OPM), machine learning (ML), and causal inference. Applied to two contrasting case studies: bus ridership along Israel's Highway 2 and bike-sharing in New York City's Citi Bike system, the framework reveals nonlinear, location-specific climate responses and quantifies the ridership and resilience returns of infrastructure investment. The results demonstrate how combining data science with system-level thinking can translate complex climate-behavior interactions into actionable, evidence-based policy.

ABSTRACT. E-scooter use has grown sharply in recent years, with riders frequently engaging in risky and illegal behaviours. Previous naturalistic studies with e-scooters have primarily focused on safety-critical events. However, few studies have analyzed the movement of e-scooter riders in natural riding conditions. This study analyses the speed behaviour of 16 e-scooter riders in non-motorised shared spaces. The data were collected in a naturalistic riding experiment in France using e-scooters instrumented with smartphones. Vulnerable road users were automatically detected from video data, and their characteristics were manually annotated. A linear mixed-effects regression model that accounts for unobserved heterogeneity among riders and among trips was estimated. The results show that e-scooter riders tend to ride significantly faster in the morning, when they are male, when they are not at intersections and when there are fewer cyclists in their field of view. These findings offer concrete inputs for stakeholders aiming to improve mobility management and safety in shared spaces.

ABSTRACT. Public transport networks are complex dynamical systems in which supply and demand continuously interact. Mobility interventions are introduced to address market failures and critical operating conditions, such as congestion, yet their behavioural effects are often difficult to evaluate causally. Interrupted Time Series provides a useful econometric framework for intervention analysis, but offers limited insight into causal mechanisms and heterogeneous responses over time. This study proposes a flexible multivariate causal learning framework based on graphical models to dynamically estimate intervention effects at both the population and individual levels. The framework generalizes the classical Interrupted Time Series model, which can be recovered as a marginal case. We apply the approach to the public transport fare incentive programme introduced in Hong Kong in 2014. The results show that the proposed method can identify intervention effects, estimate the associated parameters, and uncover the direct influence of user characteristics on incentive adoption.

ABSTRACT. Policy evaluation for urban flood resilience requires high-fidelity simulations of interdependent flood and transportation dynamics, but the computational cost of these models—the “Simulation Bottleneck”—prevents their use for scenario analysis and policy optimization. Standard machine learning surrogates offer the needed speed but are typically trained on observational data and fail in interventional scenarios, i.e. they are not interventionally consistent. This paper uses an Interventionally Consistent Surrogate framework grounded in causal abstraction theory. Using Batched Multi-Task Gaussian Processes structured to respect the simulator’s causal topology, we train a surrogate that approximately preserves the simulator’s response under policy interventions. We demonstrate the framework on Copenhagen’s inner city using a flood-transport simulator, achieving R2 > 0.92 across all three prediction tasks while providing calibrated uncertainty estimates.

ABSTRACT. This study explores how transport modes are emotionally and symbolically represented in popular music, a cultural form that reflects collective values and shapes mobility meanings often invisible to conventional travel surveys. Analyzing 27,017 Billboard Hot 100 songs (1958–2025) through a hybrid NLP pipeline combining transformer-based affect classification and LLM coding across 18 symbolic dimensions, we find that emotional differences between modes are modest, while symbolic distinctions are pronounced: cars align with status and self-presentation, trains with escape and nostalgia, and less frequent modes with self-expression. Active modes remain underrepresented and symbolically marginal. As widely circulated cultural texts, lyrics help shape and reproduce shared meanings around mobility. Our findings suggest mobility cultures are driven more by symbolic meaning than emotional association, with implications for sustainable transport promotion.

ABSTRACT. Synthetic population generation is essential for activity-based modeling, where individual level data are needed to simulate travel behavior. However, such data are often limited due to high costs and privacy concerns. A realistic synthetic population should capture both tabular and sequential attributes while preserving diversity (the ability to represent a wide range of plausible attribute combinations) and feasibility (the consistency of generated attributes with logically valid combinations). Despite recent advances in generative modeling, most existing approaches focus primarily on tabular attribute synthesis, and do not model sequential attributes. Moreover, diversity and feasibility of generated attribute combinations are often not explicitly addressed within the modeling framework. In this paper, we propose a unified generative framework for synthetic population generation that integrates tabular and sequential attributes. In the first stage, a Generative Adversarial Network (GAN) is used to learn the distribution of tabular attributes, augmented with an Inverse Gradient Penalty (IGP) regularization term to improve diversity while maintaining feasible attribute combinations. Since tabular attributes such as socio-demographic characteristics influence mobility patterns, the second stage uses a Transformer-based model to generates sequential attributes conditioned on the synthesized tabular attributes. The proposed framework is evaluated using distributional similarity, diversity, and feasibility metrics for tabular attributes, and unigram, bigram, and trigram pattern comparisons for sequential features. Results show that the regularized GAN significantly improves diversity coverage while maintaining high feasibility, and the Transformer model successfully reproduces dominant behavioral patterns observed in the real data.

ABSTRACT. Travel demand models typically produce single point estimates of trip flows, discarding the uncertainty inherent in behavioral heterogeneity and external factors. This paper extends the utility-based Bayesian MCMC framework of Scheffer (2021) in three ways. First, the full MCMC posterior is propagated through the forward model via posterior predictive sampling, yielding a distribution of hourly trips — characterised by a mean and 95% credible band — rather than a single point estimate. Second, the aggregate trip distribution is decomposed into activity-specific components estimated from aggregate trip counts and activity participation data, without individual travel records. Third, scalability is evaluated on random subsamples (5%, 10%, 25%) and demographic strata, with outputs linearly rescaled to population size. Applied to a dataset synthetically derived from the Luxembourg national travel survey (27,490 respondents, 13,714 qualifying tours, four activity types), the posterior predictive mean closely matches observed demand (R² = 0.963). A 5% random sample rescaled by a factor of 20 achieves R² = 0.969. Scalability is bounded by sample representativeness, not data volume.

ABSTRACT. Previous studies on joint household trips were often limited by data availability and focused just on specific types of joint trips. This study employs an iterative matching process with flexible criteria, successfully identifying travel party composition for over 90 % of trips with stated household company in the two large German household travel surveys SrV and MiD. Household-level weights are calculated to enable robust comparison between the two datasets. More than one third of joint trips on weekdays in Germany are parent-child trips. Joint household trips of multiple adults are predominantly performed on weekends. The prevailing purpose of joint household trips is leisure, with education purposes being equally or even more relevant for parent-child travel. The extensive matched dataset offers valuable opportunities for further research and can be a foundation for data-driven behavioural models that capture interdependencies within households.

ABSTRACT. Discrete Choice Modeling (DCM) serves as a cornerstone in understanding decision-making behaviors across transportation, economics, and marketing. While random utility methods offer interpretability, they often suffer from rigid functional form assumptions. Conversely, machine learning approaches maximize predictive power at the cost of interpretability and the risk of overfitting. This research introduces a novel framework integrating Symbolic Regression (SR) with domain knowledge to automate the discovery of utility functions that are both accurate and interpretable. Within the optimization of this framework, we propose a hybrid evolutionary algorithm where an LLM acts as an intelligent operator for population initialization, mutation, and crossover. While the overarching framework remains a Symbolic Regression search, we incorporate the LLM specifically to enhance the generation of candidate solutions. Results on the Swissmetro and Synthetic datasets demonstrate that our SR-DCM implementation is on par with existing machine learning and hybrid models in predictive performance, while significantly reducing overfitting. Furthermore, it outperforms existing interpretable models, with LLM integration serving to further accelerate convergence.

ABSTRACT. Dynamic Discrete Choice Models (DDCMs) provide a theoretically rigorous, welfare-consistent foundation for activity-based travel demand modeling, but the curse of dimensionality has prevented exact estimation at a practical scale. This study develops a GPU-accelerated computational framework that resolves this barrier through reachability-based state space pruning and sparse backward induction, enabling exact nested fixed point (NFXP) estimation without alternative sampling or approximate value function updates. Because the vast majority of states in a realistic activity-travel state space are physically unreachable, pruning them prior to dynamic programming makes exact backward induction tractable on a single GPU. The framework is demonstrated using travel diary data from Higashi-Hiroshima, Japan, estimating activity-behavioral parameters via exact NFXP with a scheduling preferences parameterization that captures time window elasticity, defined as how discretionary activities respond when mandatory activity timing shifts.

ABSTRACT. Discrete choice models are used to analyze individual decision-making in contexts such as consumer behavior, transportation choices, and political elections. Whereas traditional discrete choice models offer high economic interpretability, these models often underperform in predictive tasks compared to deep learning. Deep learning models remain largely underutilized in discrete choice due to concerns about their lack of interpretability, unstable parameter estimates, and the absence of established methods for uncertainty quantification. In this work, we introduce a deep learning model architecture that exploits approximate Bayesian inference methods via Stochastic Gradient Langevin Dynamics. Our proposed model collapses to behaviorally informed hypotheses when data is limited, mitigating overfitting and instability in underspecified settings while retaining the flexibility to capture complex nonlinear relationships when sufficient data is available. We demonstrate the value of our approach in a Monte Carlo simulation study, evaluating both predictive metrics --such as out-of-sample balanced accuracy-- and inferential metrics --such as empirical coverage for marginal rates of substitution interval estimates. Additionally, we present results from two empirical case studies: one using revealed mode choice data in NYC, and the other based on the widely used Swiss train choice stated preference data.

ABSTRACT. Energy-efficient transport is critical to reach the climate targets. This study examines the in-troduction of monetary measures (toll for cars, incentives for public transport) in a regional transport network with the aim to influence the modal split, shifting travel demand from cars to public transport in order to save energy. Public transport supply is adapted for increasing demand, allowing for additional pull effects through increased frequencies. The toll rate is varied and the concept of Pareto optimality is employed to draw conclusions about the effi-ciency of the resulting scenarios. A distance-dependent toll and refraining from paying a bo-nus to public transport users outperforms the alternative scenarios. Also, the modal shifts achieved by increasing the toll rate to more extreme values turn out to be less favorable than the shifts caused by the initial introduction of toll, as the tradeoff between energy reduction and additional travel time is less advantageous.

ABSTRACT. Public transport (PT) complements walking and cycling to help cities reaching for sustainability. However, strategic PT models often lack the spatial granularity to reveal the local-level connectivity to support proximity-based planning. This study proposes a method that enhances the strategic model travel times by attaching them to a hyperlocal spatial unit using the street network and network analysis. With London as the case study, we have refined the granularity of the travel time results between 4,400 modelling zones to between 160,000 hyperlocal spatial units. We then applied the detailed travel times to reveal the hyperlocal connectivity from each spatial unit to measure the access to schools and time taken to reach a fixed number of schools. This framework can be flexibly adapted for strategic models of other cities and to test the future transport and urban scenarios.

ABSTRACT. Urban traffic congestion represents a significant contemporary challenge, attracting considerable research interest in its mitigation. In light of the inherent difficulty and expense involved in infrastructure expansion, enhancing the exploitation level of existing infrastructure is deemed highly advantageous. This paper proposes a strategy to share the lanes dedicated to public transport fleets, e.g. buses or trams, with the passenger cars to improve the traffic condition. Employing the Cell Transmission Model (CTM), an Optimal Control Problem (OCP) is formulated to determine the lateral flow between the main road and the dedicated lane without any disruption to the public transport schedule. The OCP cost function is defined to minimize total travel time and unnecessary lateral movement, while CTM equations and the public fleet timetable are implemented as the OCP constraints. The preliminary findings from macroscopic simulation experiments indicate that the proposed approach enhances traffic conditions.

ABSTRACT. With continuing growth of electric vehicle (EV) sales and intermittent renewable baseload power supply, EVs can help balance future power grids, through charging and vehicle-to-grid discharging. Understanding charging behaviour will assist flexibility incentive and policy design. We gathered strategic and tactical charging choice attribute preferences and risky choice charging scenario data from 203 EV and petrol/diesel vehicle users. Empirical analysis revealed a risk-averse majority, particularly around range, with varying, evolving risk attitudes across heterogeneous groups. The most context relevant risk attitude predictor was time sensitivity. Flexibility incentives can target range, cost, time and charge location feature aspects of ‘where’, ‘when’ and ‘how much’ to charge/discharge choices, segmenting EV users by risk attitude. Policy makers can target EV adoption interventions at risk-averse groups. Non-expected utility theory methods, focusing on heterogeneous risk attitudes across attributes and segments appear appropriate for explaining risky choices in this context.

ABSTRACT. This study presents a backcasting approach applied to the decarbonization of the passenger car fleet in Metropolitan France. Three policy measures are considered, namely, EV purchase incentive, Malus CO$_2$, and eco-driving (ED) incentives. Through a forecasting model, their impacts are separately evaluated in terms of market shares and emissions.

An optimal control problem is then formulated to determine the combined policy trajectory that minimizes monetary cost and reach a defined CO$_2$ target. A Pareto front is evaluated, along which costs to achieve lower CO$_2$ targets increase.

The results suggest that EV incentives should decrease over time and be inversely proportional to the income class, the Malus CO$_2$ should be kept for a longer time at its maximum value, while the more effective ED incentive should cover as much population as possible.

ABSTRACT. The technological advancement of electric vehicles and charging stations emphasizes the requirements for more effective deployment strategies. To this end, a two-stage stochastic programming model is proposed to determine the location and capacity of charging sta-tions, aiming to minimize the cost investment while maximizing expected revenues under uncertainty. The proposed methodology integrates spatial clustering techniques to identify candidate sites and incorporate demand patterns across the urban network. The outcomes of the model, applied to a case study of the city of Milan, supports a more balanced de-ployment by highlighting the importance of under-served areas to improve charging net-work efficiency, and encourage EV adoption. These findings support allocation decisions as preventing excessive concentration of charging stations in specific areas, reducing the number of chargers with inefficient utilization, and lowering operational costs for service providers in large-scale urban environment.

ABSTRACT. This study evaluates the impact of Scotland’s 2014 reduction in the legal blood alcohol concentration limit from 0.08 to 0.05 g/dL on road traffic accidents. Using yearly data aggregated at the Middle-layer Super Output Area (MSOA) level for Engalnd and Wales and Intermediate Zone (IZ) level for Scotland, a balanced panel dataset of 7,940 spatial units across Great Britain from 2008 to 2019 is constructed. The analysis employs the Synthetic Difference-in-Differences (SDID) method to estimate a credible counterfactual. The results indicate a significant reduction in crash rates following the policy, with an estimated decline of 12.00% in total crashes. Stronger effects are observed during periods associated with higher alcohol-related risk, with reductions of 31.63% in night-time crashes and 25.28% in weekend crashes. Robustness checks, including placebo tests, confirm the statistical significance of these findings.

ABSTRACT. This study proposes a reward-based framework for public transit demand control under partial controllability, where only a subset of users can be directly influenced by incentives. The framework integrates a mean-field model of day-to-day behavioral adjustment with a budget-feasible reverse auction based on congestion externalities. Controllable and uncontrollable users are coupled through aggregate network congestion, while the operator allocates limited rewards using class-level externality weights computed from observed flow states. The proposed mechanism satisfies per-day strategy-proofness, individual rationality, and budget feasibility in the induced single-parameter environment. The resulting system is formulated as a feedback-perturbed population dynamic, in which uncontrolled users follow inertial logit revision and controllable users are additionally shifted by auction-induced reallocation. Numerical experiments show that the mechanism reduces peak-period congestion even when the controllable fraction is limited, while also revealing a trade-off between externality-based targeting and budget efficiency under heterogeneous weights.

ABSTRACT. Road traffic crashes claim 1.19 million lives annually and remain the leading cause of death for people aged 5–29 years. Safe road infrastructure is among the most effective countermeasures for reducing this burden. The International Road Assessment Programme (iRAP) offers a globally recognised framework that requires trained coders to manually identify up to 50 physical road attributes every 10-metres from survey images for road infrastructure assessment and improvements. While the iRAP methodology provides a robust and globally validated framework for infrastructure safety assessment, the current reliance on manual coding can be resource-intensive and may limit scalability, particularly in low- and middle-income contexts. Although computer vision approaches have been proposed for automated attribute classification, few studies have systematically examined ensemble learning to enhance performance across multiple safety attributes. This paper addresses this gap by evaluating ensemble voting strategies for road attributes classification task. Results show that ensemble methods outperform individual baseline models.

ABSTRACT. Ensuring safe interactions between vehicles and vulnerable road users (VRUs) is a critical challenge in mixed traffic environments. This study investigates the use of multimodal trajectory predictions to anticipate potential hazards involving VRUs and generate actionable risk warnings. A structured framework is proposed to extract safety indicators from predicted trajectories and evaluate multiple trigger strategies. Experimental results show that OR-logic aggregation achieves the highest overall performance by balancing hazard detection and false alarm control, while other strategies exhibit more conservative or imbalanced behavior. Furthermore, the choice of the number of aggregated trajectories (K) introduces an additional trade-off, where increasing K enhances risk coverage but may lead to reduced reliability due to more frequent warnings. These findings provide practical guidance for the design and deployment of risk warning systems.

ABSTRACT. This paper presents the spatiotemporal prediction results of unsafe road conditions (URC) across the Dutch highway network, based on sparse, historical multi-source data. Climate change increases the risk of extreme weather-related URCs on road networks, which can cause disruptions and road user safety risks. Hence, the objective of this paper is to develop a data-driven model for the spatiotemporal prediction of URCs for highway networks. We propose a Graph Convolutional Network – Bidirectional Long Short Term Memory Network (GCN-BLSTM) for this goal, and benchmark this model against state-of-the-art deep learning algorithms. The GCN-BLSTM shows overall, higher predictive performance, with road ponding and roadside fires being predicted most accurately among the URCs. This study highlights the predictive potential of a hybrid spatiotemporal model for multi-URC predictions, compared to state-of-the-art data-driven methods, and proposes implications for future research on multi-URC prediction.

ABSTRACT. Traffic Incident Management (TIM) is essential for ensuring roadway safety and reducing congestion. However, issues including delayed responses, inefficient resource use, and limited access to incident sites negatively affect TIM. Unmanned Aerial Vehicles (UAVs), offer a via-ble solution by enabling rapid incident assessment, improving situational awareness, and de-livering real-time data for decision-making. This study investigates the design and deployment of UAV-(Service Safety Patrol (SSP) fleet on an interstate by developing an optimization-based framework. Formulated using a 58-mile segment of I-10 in Louisiana, two mixed-integer linear programming models were developed. The objective of the first model was to minimize the number of UAV-SSP units needed to meet a maximum response time threshold, and while the second model sought to minimize the total average response time for a fixed number of units. The results of this study will be used to improve incident response on inter-states in Louisiana.

ABSTRACT. Cities are more at risk of experiencing flooding as climate change worsens, disrupting transport infrastructure and the social and economic networks that depend on it. Though flood impacts are typically studied by focusing on extreme events, ‘nuisance flooding’ — high-frequency, low-impact flood events that cumulatively disrupt the functioning of everyday activities — remains underexplored. We study how nuisance flooding affects everyday accessibility by defining a quality of life (QOL) index based on the accessibility of services and amenities, identifying flood-related changes in QOL, and mapping distributional effects of impacts. Focusing on Copenhagen (Denmark) and rainfall scenarios that cause nuisance flooding, our results suggest nuisance flooding 1) meaningfully lowers QOL, 2) lowers active mode-based QOL more, and 3) affects QOL the most in wealthier neighbourhoods. Our findings suggest nuisance flooding can pose a challenge to policymakers, and that the impacts of nuisance flooding on active transportation modes merit further attention.

ABSTRACT. End-to-end autonomous driving systems generate control signals directly from sensor inputs, implicitly assuming that the surrounding environment is exogenous. However, at unsignalized intersections, pedestrians actively adjust their actions in response to approaching vehicles, possibly leading to inefficient or unsafe control. To accurately capture interactions across all agents, we propose Mean-Field Adversarial Inverse Reinforcement Learning with Continuous Actions and Kernel Decomposition (MF-AIRL-C+K) to quantitatively evaluate pedestrian reactive behavior in the presence of surrounding pedestrians and vehicles, by extending adversarial inverse reinforcement learning to continuous action spaces and incorporating mean-field-based interaction. Specifically, restricting the utility function to a negative-definite quadratic form guarantees a unique and well-defined Gaussian optimal policy. In addition, the parameterization of the reward function based on kernel decomposition further enforces the condition of potential Mean-Field Games and enables interpretable spatial analysis. Experiments on pedestrian trajectories collected at JR Matsuyama Station show that MF-AIRL-C+K outperforms all baselines on all metrics.

ABSTRACT. The aim of this work is to improve the explanatory power of models of transport mode choice and thus contribute to the mobility transition. The authors develop a new model of mobility behaviour called xMooBe: It incorporates elements from attitudinal and choice models and combines them with a sociological theory of action, which has its roots in analytical sociology. xMooBe is based on a simple model of decision-making (with a manageable number of variables) and expands it by taking into account additional contextual factors such as car ownership and public transport availability.

The study uses a mixed-methods approach that combines statistical analysis of survey data (including regression analysis), theory-based modelling of (bounded-rational) everyday decision making and thought experiments to identify options for behavioural change. Instead of relying on manifest statements of behav-ioural intentions, xMooBe applies an extended version of the subjective expected utility theory, which refers to latent preferences and subjective perceptions (plus contextual factors). The mixed-methods approach was used to validate xMooBe and to test different assumptions about (policy) measures that could influence transport mode choice in terms of sustainability.

xMooBe achieves up to 80 percent accuracy in explaining behaviour - and thus differs from many other studies with partly inconsistent results. xMooBe helps to understand why people behave in ways that are inconsistent with their attitudes, e.g. in the case of car-using cyclists, and thus helps to bridge the gap between attitude and behaviour. In most cases, known contextual factors (such as car ownership, state of the cycle network, etc.) help to explain this gap. At the same time, they serve as a starting point for interventions whose potential impact has been tested through experimentation.

ABSTRACT. Intra-household joint home-based tours — trips in which household members depart together, engage in shared activities, and return together — represent a significant share of daily travel, yet are systematically ignored in transport simulations. Conflating joint and solo tours within a single mode choice framework introduces bias in preference parameter estimates. This paper proposes a three-step methodology to simulate joint tours in agent-based transport models: a Random Forest classifier to identify joint tours, a Multinomial Logit model estimating mode choice specific to joint tours, and a Penalized Logistic Regression for driver/passenger assignment. Applied to the Paris region using household travel survey data, the methodology successfully replicates observed joint tour shares and mode distributions in a synthetic population. The proposed framework enables more reliable evaluation of policies whose impacts differ between joint and solo travel, such as HOV lanes or family transit fare discounts.

ABSTRACT. This study examines children’s school travel mode choice and independent travel in Vallensbæk Municipality, Denmark. Previous research has mainly focused on objective determinants, while giving less attention to parental perceptions as behavioural drivers. Three discrete choice models were estimated based on parental survey data of three primary schools in 2024 and 2025: a Multinomial Logit model for travel mode choice, a Binary Logit model for independent travel, and an Integrated Choice and Latent Variable (ICLV) model, incorporating parental perception of children’s cycling skills as a latent variable. Distance and grade are the strongest predictors of mode choice and independent travel. The ICLV reveals that latent parental perception of cycling skills enables both cycling and walking, a finding the standard MNL does not detect, and that parental confidence improved between 2024 and 2025, coinciding with the Green Mobility Shift interventions. These results highlight parental behavioural perspectives in school travel research.

ABSTRACT. This paper investigates how picture-based and virtual reality (VR) experiment formats influence individuals’ valuation of bus crowding in stated preference settings and explores the cognitive mechanisms underlying perception heterogeneity. Thirty-eight participants completed 40 choice scenarios in each format. We estimate individual-level crowding multipliers, that is, the ratio of travel time valuation in crowded versus uncrowded scenarios, across both formats and investigate their correlations with neural biomarkers. Our analysis reveals no differences in crowding multipliers across experiments. However, EEG analyses indicate distinct cognitive processes: the picture-based experiment is associated with enhanced top-down control, reflecting greater reliance on internal beliefs and prior experiences (elevated frontal alpha-band activity). This finding is further supported by the association between crowding multipliers and self-reported willingness to avoid crowding in the picture-based experiment, whereas no such association is observed in the VR-based experiment.

ABSTRACT. This paper develops a rational inattention (RI) model that separates exogenous information pushed by external platforms from travelers’ endogenous costly information processing. A platform first publishes a signal that shifts the traveler’s prior into an interim belief; the traveler then solves an RI problem conditional on this interim belief. The RI value function is convex in the interim belief, generating non-learning regions where the traveler optimally ignores additional information. Following Caplin et al. (2019) and Matyskova & Montes (2023), we derive closed-form threshold beliefs that partition the belief space into learning and non-learning regimes, and show that pushed signals improve welfare only when they shift interim beliefs across these thresholds, demonstrating an information paradox. In an illustrative example with garbled signals parameterized by noise level epsilon, we obtain analytical conditions on epsilon for welfare improvement. We further show that the sensitivity of unconditional choice probabilities to epsilon varies with the information cost $\lambda$, implying that systematic variation in signal precision can screen travelers’ information costs.

ABSTRACT. This study examines how travellers’ preferences differ across connected choice contexts, including stated preference (SP), app-based travel planning, and real-world revealed preference (RP). To do so, we designed the Leeds-EXPERIENCE field experiment, in which 100 participants completed a pre-journey SP survey, an initial travel plan, a four-leg real-world journey under binding time and budget constraints, and a post-journey SP survey. A joint modelling framework was developed to analyse the four linked datasets. Preliminary results show clear shifts between planned and realised choices, indicating that information-based plans do not fully reflect real-world decisions. The joint estimates also suggest significant scale differences, with RP choices exhibiting lower scale than SP and planning data. The study highlights the value of connected multi-context data for improving behavioural realism and model transferability.

ABSTRACT. Accurate estimation of ridership and fare evasion is essential for monitoring public transport networks. The combination of Automated Fare Collection and Automated Passenger Counting (APC) data delivers good results in fraud detection but its estimation on unequipped vehicles remains challenging. This paper proposes a new method to estimate fraudulent boardings and alightings on vehicles that are not equipped with APC, using transfer learning from equipped vehicles. Constrained linear regressions are fitted at multiple spatio-temporal granularities. To address data sparsity, a K-Nearest Neighbors approach transfers information across close spatio-temporal granularities. The method is evaluated on four public transport networks showing a significant improvement in onboard occupancy reconstruction compared to other existing approaches, and highlighting the importance of incorporating temporal dynamics in fraud estimation.

ABSTRACT. Public transport transfers impose additional waiting time, physical effort, and uncertainty on passengers, commonly referred to as the “transfer penalty”. Despite the importance of transfers in public transport networks, the microscopic transfer environment (including station design, facilities, and service coordination) remains underexamined. This paper investigates how transfer-related factors affect passenger travel experience and subjective wellbeing in Hong Kong, one of the world’s most transit-oriented cities. Drawing on an online survey of 119 validated respondents across bus-bus and metro-metro transfer contexts in 2025, regression analysis was used to examine the determinants of travel satisfaction, while discrete choice modelling was applied to analyse passenger preferences for transfer improvements. Results show that perception of facing many barriers and travel delay during transfer and negatively associated with travel satisfaction. Timetable coordination, seat availability, and basic facility provision are the most preferrable improvements. Policy implications for infrastructure design and regulatory frameworks are discussed.

ABSTRACT. Travel experience in public transport networks is negatively affected by passenger overcrowd-ing and incomplete travel information. In this study, we introduce a concept of path-based crowding information (CI), i.e. a personalized CI on cumulative travel comfort conditions, generated from the APC passenger flow data. It extends the classical CI concept by accounting for both passenger loads (between stops) as well as the dwelling exchanges (at stops) and emerging seat probability along the O-D travel path. The path-based CI framework is first ap-plied on the toy network and then on the APC data of Rzeszów case study (Poland), showing its potential to reveal additional seating possibility in up to 25% of all travel instances, and in over 40% of such cases – along the majority of their O-D travel route. Our findings underscore the potential of personalised, path-based CI systems to improve travel experience, especially in congested PT networks.

ABSTRACT. Urban multimodal transport systems suffer from inefficiencies arising from spatial and modal misalignment between demand and supply. Public transit (PT) is typically dense in city centers but sparse in peripheral areas, creating a spatial imbalance in access to mobility services. Ride-hailing (RH) can help address this imbalance by providing flexible coverage and first-and-last-mile connectivity to the PT network. Yet profit incentives concentrate RH supply in transit-rich urban centers, where RH diverts demand for short trips from high-capacity PT and consumes scarce road space, while underserving transit-poor peripheries. This misallocation reduces efficient use of road and transit resources and exacerbates accessibility imbalances. To address this, we propose a supply-side, spatially differentiated Tradable Credit Scheme. By assigning higher credit requirements to central zones, the scheme shifts relative profitability, incentivizing RH relocation to peripheries. This supply-side intervention reshapes passenger assignment across modes by altering RH service availability and wait times, reducing inefficient substitution away from PT in transit-rich areas, and improving PT access in peripheral areas through better RH–PT integration. We develop an integrated modeling framework linking the credit market with endogenous multimodal demand and transport operations. We evaluate system performance at a long-term stationary equilibrium in which, given the prevailing credit price, assignments, and travel times, no driver can increase earnings by changing participation or license levels. Numerical results show that the proposed policy weakens RH substitution away from PT, expands RH availability in suburban areas, improves first- and last-mile connectivity, and helps mitigate spatial imbalances in PT-based accessibility across urban and suburban areas.

ABSTRACT. This study analyzes user activity in a five-week field trial of a Tradable Credit Scheme (TCS) designed to reduce commuting-related CO2 emissions at a university. Participants in the treatment group used a MobilityCoin app to trade credits, while commuting behavior was tracked with a GPS-based mobility app. We first analyze which users actively engage with the system using logistic regression models for app logins and manual trading. We then estimate how activity relates to emission reductions with fixed-effects models and instrumental-variable regressions to address the endogeneity of login behavior. The results show substantial inactivity, but also strong heterogeneity in engagement. Older participants and women were less likely to log in, while a more rational decision-making style was associated with manual trading. Most importantly, early app engagement, especially in the first treatment week, is linked to stronger emission reductions. The findings highlight the importance of simple and user-friendly TCS design.

ABSTRACT. Tradable Credit Schemes (TCS) are a promising market-based instrument to manage transport demand and reduce emissions, yet empirical evidence on behavioral responses and market performance remains limited. This study examines individual behavior in a tradable credit market using a lab experiment that captures endogenous price formation and strategic trading within an active central credit market in realistic travel contexts.

The results show that a TCS faces important market challenges, as prices exhibited substantial volatility and concentrated trading intensity, suggesting market power and difficulties in achieving a stable equilibrium, although it can also reduce driving and encourage shifts toward lower-emission alternatives. Market performance is strongly influenced by allocation rules, pricing mechanisms, market size, and participant composition, with heterogeneous characteristics contributing to imbalances in trading behavior and price formation.

Overall, the study highlights both the potential and design challenges of TCSs, emphasizing the importance of robust market design for effective real-world implementation.

ABSTRACT. This paper operationalizes the Capabilities Approach (CA) to inform resource allocation decisions in a transport network modeling framework. While existing studies recognize the role of the CA in equity-oriented policies and investments, integrating the normative theory into tractable analytical models remains limited. We address this gap by formulating a multi-period mixed-integer nonlinear network design problem that explicitly captures the relationships between resources, conversion efficiency, capability formation, and well-being. Conversion factors are represented through individual-specific travel costs and cost thresholds, enabling heterogeneous transformation of resources into capabilities. The framework further incorporates dynamic feedback, where achieved well-being lowers travel cost thresholds, thereby expanding spatial reach and influencing future capability formation. We compare feedback-aware and feedback-agnostic decision-makers, demonstrating how accounting for endogenous feedback alters optimal investment strategies. Additionally, we identify a critical capability threshold that provides an analytical interpretation of sufficiency-based justice. Sensitivity analysis highlights how population heterogeneity shapes long-term equity outcomes.

ABSTRACT. This paper studies a Stackelberg pricing competition between regulated public curb parking, subject to congestion, with a nearly guaranteed-access private parking garage alternative. The public authority regulator sets the curb price, anticipating the response of a profit-maximizing garage operator.

We assume that users differ in both their valuation of the service and their sensitivity to waiting, leading to endogenous sorting across the two parking options. We show that pricing regulation shapes market power in a counterintuitive way. When the curb is underpriced, congestion intensifies, causing the operator to raise garage prices and to target high-value, time-sensitive users. In contrast, higher curb prices may lead to lower garage prices and welfare.

The results help explain the common phenomenon of low curb prices and high garage fees, highlighting the role of curb pricing in shaping market outcomes.

ABSTRACT. This paper proposes a gradient-assisted approach to model specification in discrete choice analysis. The key idea is that, although the sample-level gradient of the log-likelihood is zero at the maximum likelihood optimum, respondent-level gradients are not, and therefore contain valuable information about underlying heterogeneity. Using a well known Stated Preference (SP) dataset, we show that these gradients reveal systematic variation in preferences and can be exploited in several ways. First, they help identify omitted systematic heterogeneity and guide improved multinomial logit specifications. Second, in mixed logit models, gradients are strongly related to posterior means and can be introduced directly into random coefficient distributions, leading to major improvements in fit and a substantial reduction in residual heterogeneity. Third, gradients provide a natural basis for clustering respondents and improving class allocation in latent class models. Across all model structures, the use of gradients yields better fit, more behaviourally meaningful heterogeneity patterns, and lower willingness-to-pay measures. The paper also highlights endogeneity concerns as an important next step for future research.

ABSTRACT. Transport cost-benefit analysis (CBA) is a key tool in policy-making, yet its methodology has remained largely unchanged for decades. This paper integrates transport appraisal into a quantitative spatial model (QSM) with endogenous time valuation, enabling welfare analysis in both partial equilibrium (PE) and spatial general equilibrium (SGE) settings. We quantify welfare using two competing approaches: a conventional PE method based on direct user benefits and approximated agglomeration effects, and a structurally consistent SGE framework that captures the full reorganisation of economics activity in space. The latter is implemented via a welfare decomposition that aligns closely with standard CBA components. Empirically, the resulting welfare estimates are of similar magnitude across approaches, with SGE estimates falling within the range of PE outcomes. This suggests that adopting a general equilibrium framework is unlikely to fundamentally alter project rankings. Monte Carlo simulations confirm a strong correlation between methods, with differences emerging primarily for smaller interventions.

ABSTRACT. Karma is a non-monetary resource-allocation mechanism that prioritizes users' needs rather than their financial power. Monetary pricing can effectively reduce congestion by imposing charges on specific road segments, but it may be unfair by favoring higher-income individuals. Prior work has shown that in this context, Karma can achieve similar efficiency while yielding fairer outcomes; however, demonstrated only in a deterministic setting. Demonstrating Karma's applicability under more realistic traffic conditions is therefore important for real-world implementation. Additionally, experimental evidence suggests that humans may struggle to execute optimal bidding strategies in Karma economies. In this paper, we demonstrate the use of Multi-Agent Reinforcement Learning (MARL) to train automated bidding agents for travelers. In a microscopic traffic simulation case study, we show that MARL agents learn effective bidding strategies that yield fairer travel outcomes for drivers than those achieved under monetary pricing schemes.

ABSTRACT. This study investigates the strategic interactions between competing transport network companies (TNCs) within a multi-modal transportation system at both operational and informational levels. A bi-level model is developed, where at the upper level, each TNC independently optimizes its trip fare to maximize profit, and at the lower level, travelers select among available services according to a multinomial logit (MNL) model. To better capture real-world uncertainty, the model incorporates an incomplete-information setting, where TNCs do not have exact knowledge of travelers’ value of in-vehicle time (VTT), value of waiting time (VWT), and the total demand. Instead, they must learn and iteratively update their beliefs based on observed outcomes, following a Bayesian framework. Numerical experiments demonstrate that the market size plays a decisive role in the accuracy of information inference. The benefits of ignorance are also shown in our results, that less information may help to mitigate the intensity of competition.

ABSTRACT. Efficient on-street parking management is a critical challenge for modern cities due to its direct impact on traffic congestion, energy consumption, and driver satisfaction. The dispersed nature of on-street parking across large regions makes monitoring difficult using traditional sensors, severely limiting their scalability in dynamic environments. This paper presents a novel, fully automated drone-based framework to extract on-street parking locations and monitor occupancy directly from raw aerial video. Our approach eliminates the need for predefined maps or inaccurate crowd-sourced data. Instead, it infers parking masks from observed vehicle trajectories. The framework detects vehicles and tracks their trajectories to isolate stationary parking behavior from active traffic. We then use a spatial-temporal dwell-time scoring technique combined with a clustering algorithm to generate precise parking masks. A real-world case study in Galatsi, Greece, demonstrates the effectiveness of this method, capturing two-day parking occupancy dynamics with high autonomy. Results highlight the potential of drone-based aerial systems to deliver scalable, cost-effective, and adaptive solutions for studying urban mobility and monitoring on-street parking without reliance on any prior information.

ABSTRACT. In future cities every driver may own a vehicle which could be either independently driven (HDV), or autonomously routed and piloted (CAV). The autonomous operations could be handled by a few competing companies. What is the market structure which would make this market aligned with city goals? In this paper we discuss a variant of the emerging market of collectively routed fleets of CAVs, where revenue for fleet operators is proportional to market share. We provide benchmark scenarios to compare the routing algorithms. We present several routing algorithms and demonstrate that, when the attitudes of human drivers towards CAVs exhibit significant diversity, randomised CAV routing, resulting in unpredictable travel times for HDVs, is more efficient than routing proportional to system optimum/user equilibrium. Based on this, we propose to improve the design of the market by augmenting the market-share objective with mean systemwide travel time in order to limit antisocial randomised strategies of fleet operators and drive the competition towards social welfare oriented cooperation.