Urban Air Mobility (UAM) systems offer a three-dimensional transportation alternative by using low-altitude airspace, with the potential to reduce travel times and improve access to mobility in regions underserved by current transportation systems. To support efficient design and operation of UAM systems, we develop an integrated optimization framework in response to three interrelated challenges: (i) land use, aeronautical feasibility, community acceptance and other factors that restrict the number of potential locations for vertiports, (ii) bidirectional demand–supply interaction that needs to be considered, as the level of service influences demand for UAM and operators adjust the level of service in response to demand, and (iii) strong interactions between strategic decisions on the distribution of ground infrastructure, tactical decisions on eVTOL fleet size and operational decisions on dispatching and repositioning. Analyzing the decisions in isolation can lead to poor estimates of the overall system performance. The framework consists of (1) a knock-off criteria analysis model for the identification of a realistic set of candidate locations for vertiports, (2) integer programming models in which strategic, tactical and operational decision levels are modeled, and (3) pre-processing techniques to generate near-optimal solutions for real-world instances. By applying the framework in a large-scale real-world setting in the Île-de-France region, we demonstrate complex interactions between strategic, tactical, and operational decision levels and customer demand, revealing various trade-offs between operator profit and traveler generalized travel costs.

Teleoperated driving complements automated driving and acts as transitional technology towards full automation. An economic advantage of teleoperated driving in logistics operations lies in managing fleets with fewer teleoperators compared to vehicles with in-vehicle drivers. This alleviates growing truck driver shortage problems in the logistics industry and save costs. However, a trade-off exists between the teleoperator-to-vehicle (TO/V) ratio and the service level of teleoperation. This study designs a simulation framework to explore this trade-off generating multiple performance indicators as proxies for teleoperation service level. By applying the framework, we identify factors influencing the trade-off and optimal TO/V ratios under different scenarios. Our case study on road freight tours in the Netherlands reveals that for any operational settings, a TO/V ratio below one can manage all freight truck tours without delay, while one represents the current situation. The minimum TO/V ratio for zero-delay operations is never above 0.6, implying a minimum of 40% teleoperation labor cost saving. For operations where a small delay is allowed, TO/V ratios as low as 0.4 are shown to be feasible, which indicates potential savings of up to 60%. This confirms great promise for a positive business case for the teleoperated driving as a service.

Empty trips, vehicle movements without payload, generate unsustainable externalities. This study addresses these challenges by identifying shortcomings in the literature on empty trip generation across all modes through a combined narrative and systematic review. The review revealed that the conventional definition of empty trips adopts a binary empty/non-empty perspective, which fails to capture the extent of the problem. In response, we propose a continuous measure based on the proportion of unused vehicle capacity. Our review also reveals that mitigation strategies are often case-specific, which limits their impact and transferability across modes. To address this limitation, we introduce a framework that facilitates the transferability of strategies by highlighting cross-modal similarities in generating empty trips. Finally, we reviewed these strategies, their methodologies, and the associated problems related to empty trips using the systematic review in driver-provided mobility, which includes public transportation, carpooling, taxi, and ride-hailing to support sustainable mobility through strategy generalization.

Vehicle-to-grid (V2G) technology enables electric vehicles (EVs) to function as both energy consumers and suppliers, potentially improving power grid stability while offering economic benefits to users. Before large-scale implementation, however, the viability of V2G must be assessed, specifically, the extent to which people are willing to adopt it. This study investigates people's willingness to switch from their current mode of transport to two V2G-enabled options: private EVs with V2G, and carsharing EVs with V2G. To achieve this goal, a stated choice experiment was designed and conducted in the Netherlands. For the data analysis, we introduce a random-parameters rank-ordered logit model to capture preference heterogeneity and unobserved correlations between the two V2G options. The results indicate strong inertia towards retaining the current (status quo) travel mode compared with the two V2G alternatives. Cost savings from V2G participation significantly increase the likelihood of choosing both V2G options, whereas the minimum guaranteed battery range has a limited effect once trip-range requirements are satisfied. Preferences also differ systematically between respondents with different status quo travel modes and across socio-demographic groups. These findings support targeted policy design to accelerate V2G adoption.

Shared electric vehicles (SEVs) have emerged as a promising solution for promoting sustainable urban mobility. However, ensuring the efficient operation and effective battery management of SEV systems remains a complex challenge. To address this issue, this paper introduces an integrated optimization framework for SEV systems that jointly considers Vehicle-to-Grid (V2G), Battery-to-Grid (B2G), plug-in charging, and battery swapping. The proposed approach is built on a space-time-energy network model that simultaneously optimizes battery charging and discharging scheduling together with SEV operations, such as relocations and battery swapping. The objective is to maximize profit while addressing operational constraints and the complexities of energy management within SEV systems. Given the substantial complexity of large-problem scales, the paper introduces a column generation-based heuristic algorithm. Additionally, a rolling horizon approach is employed to enable real-time decision-making under dynamic operational conditions. Extensive experiments are conducted to evaluate the effects of key parameters, such as the rolling horizon settings, charging rates, fleet sizes, and the number of stocked batteries. The effectiveness of various energy management strategies is also assessed, ranging from plug-in charging alone to its combinations with battery swapping, V2G, and B2G. Numerical results show that under low carsharing rental demand scenarios, plug-in charging alone is a cost-effective option. Moreover, battery swapping is found to be particularly effective as an auxiliary recharging method when the SEV fleet is limited, charging rates are low, or carsharing demand is high. Overall, this study provides theoretical foundations for the integration of vehicle operations and energy management in shared electric mobility systems.

Research on the supply–demand dynamics of Shared Automated Vehicle (SAV) services has grown rapidly. However, the resulting models are often highly complex nonlinear systems that are difficult to solve. This study has two main objectives: (1) to investigate profit-maximising pricing and fleet management strategies for SAV services while accounting for supply–demand interactions, congestion effects, and travellers’ heterogeneous income levels; and (2) to develop and compare several solution methods for determining these strategies. We formulate a mixed-integer nonlinear programming model for the SAV fleet management problem under three pricing strategies: a base fare plus a distance-based fare, a distance-based fare only, and an income class–based fare. To solve this model, we propose three algorithms designed to address its nonlinearities: a linearisation-based method (P1), a hybrid particle swarm optimisation method (P2), and a hybrid Bayesian optimisation method (P3). We also examine the feasibility of the solutions obtained by these methods with respect to the original problem, and introduce a correction procedure when infeasibility arises. The proposed methods are evaluated through a small case study and a larger case study based on Delft, the Netherlands. The results show that all three methods can find good-quality solutions within short computational times in the small case study, while P3 achieves the best objective value in the Delft case study. From a practical perspective, the Delft case study shows that the first pricing strategy can encourage active transport for short trips, the second leads to the lowest congestion but also the lowest profit, and the third achieves the highest profit by attracting more demand and exploiting the lower price sensitivity of high-income travellers, despite causing higher congestion. Additional analyses further examine the effects of income class segmentation, income distribution, and congestion.

The rapid growth of online food delivery services has transformed how consumers access meals in urban areas, offering the convenience of doorstep delivery. This growing demand has intensified pressure on platform operators to prioritise speed and efficiency, often using short delivery times as a key performance metric. As a result, delivery riders are subjected to time constraints in congested traffic conditions, which can compromise their safety. This study investigates whether consumers, as the end beneficiaries of such services, are willing to support safer delivery practices by making trade-offs in terms of cost and waiting time. A stated preference experiment on 350 regular users of the online food delivery system in Amsterdam and Copenhagen examined how safety labels indicating rider crash risk influence their behaviour. A hybrid latent class choice model was employed to capture preference heterogeneity and incorporate attitudinal factors. The analysis revealed two distinct consumer segments: one generally indifferent to road safety and another highly sensitive about it. Results suggest that while some consumers remain unaffected, a sizable segment is influenced by safety labels and exhibits a clear willingness to pay more or wait longer for safer deliveries. The study presents several managerial and policy insights, outlining practical strategies that could encourage consumers to make choices that help improve the safety of food delivery riders.’

Shared automated vehicles (SAVs) have the potential to transform travel by enabling users to engage in non-driving-related tasks (NDRTs), enhancing productivity and travel satisfaction. To explore this potential, we conducted a field experiment using a Wizard-of-Oz simulator-on-wheels replicating SAV services in urban areas. The study examined how engagement in work and leisure NDRTs influenced attitudes, preferences, and associated values of travel time (VoTTs) for SAVs versus conventional transport modes (public transport (PT), cars, and bicycles). A total of 104 participants completed two test rides while engaging in work and leisure activities, with engagement levels captured via video recordings. Results showed that travel costs for SAVs were perceived as less negative than those of PT and cars, and that participants preferring work over leisure in SAVs developed a more positive perception of travel time in them post-test. In contrast, full concentration on NDRTs during test rides increased the disutility of travel time of the car alternative. Pre-test results indicated that SAVs had the highest VoTTs compared to cars and PT. However, after the rides, VoTTs for SAVs decreased when used for work-related activities, underscoring their advantage for productivity-focused travel. For cars, the ability to fully concentrate on NDRTs increased VoTTs, reflecting heightened expectations of comfort and productivity. These findings highlight SAVs’ potential to enhance travel productivity, but also show how experience with NDRTs reshapes conventional modes perceptions. Finally, the experiment demonstrated the relevance of the Wizard-of-Oz approach for simulating realistic SAV experiences, with 74% of participants believing the setup was genuine.

Electric buses (EBs) play a crucial role in achieving global greenhouse gas emission targets. However, efficiently operating an electric bus fleet (EBF) requires a comprehensive approach that considers both mobility and energy systems, particularly when implementing opportunity charging strategies. Existing literature and many real-life implementations often focus on only one of these systems, oversimplifying the other, which can lead to inefficiencies, operational challenges, or even unfeasible implementations. To fill this gap, we propose a framework to assess the impact of bus opportunity charging strategies on the power grid by integrating a traffic simulation model (SUMO) and a power grid simulation model (Gaia). SUMO evaluates the energy consumption and charging needs of the EBs, while Gaia assesses the impact of the transformer load in the distribution grid. The integrated method is applied to Rotterdam’s bus line 36 to demonstrate the practicality of this approach. Results indicate that designing an electric bus route with opportunity charging is feasible only when both mobility and energy systems are carefully coordinated.

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

Shared automated electric vehicles (SAEVs) have the potential to transform regional transportation, particularly in low-density areas where accessibility and resource optimization are challenging. However, their integrated economic impact on operators, users, environment, and society have been little explored. This paper presents a cost-benefit analysis methodology, incorporating a flow-based integer programming model, to assess the viability of SAEV services in a regional interurban context. The case study is based on mobility data from the Aveiro and Coimbra regions (Portugal). We evaluate the replacement of all motorized intermunicipal trips with various SAEV configurations, including automated cars (with and without pooling), automated minibuses, and a mixed fleet (cars and minibuses). Results indicate that SAEV providers can achieve profitability with fares ranging from €0.08 to €0.36 per kilometer. Even at these rates, SAEV services generate economic benefits for users, particularly pooled car-based services, as private car expenses dominate current mobility costs. Additionally, all SAEV configurations contribute to cost reductions related to air pollution, noise, global warming potential, and road accidents, with pooled services offering the greatest savings. A series of SAEV transition scenarios using a fleet of pooled cars also demonstrated benefits for all stakeholders, albeit lower than those from fully replacing motorized trips. A second sensitivity analysis confirms that reducing vehicle acquisition costs is key to lowering fares and increasing user savings. This paper represents one of the first evaluations of large-scale SAEV services for intermunicipal trips with significant distances between urban centers, contributing insights into smart and sustainable transportation solutions for such contexts.

Amidst the pressing need for sustainable transportation, Shared Automated Electric Vehicles (SAEVs) emerge as an increasingly explored solution with the potential to revolutionize mobility. Yet, understanding the environmental impacts of operating this mobility solution at different scales remains sparse. This study addresses this by integrating Agent-Based Modelling (ABM) and Life Cycle Assessment (LCA) to assess the environmental impacts of SAEVs at municipal, subregional and regional scales. ABM simulates travellers’ behaviour and SAEVs deployment strategies, yielding dynamic patterns along a typical day, while LCA provides a structured framework for assessing the life cycle environmental impacts. This process involves creating an ABM that reflects a representative mobility scenario, and a modified ABM scenario where private car and bus trips are replaced with SAEV services. The analysis extends the different scales, providing both short-term and long-term perspectives on LCA impacts. Findings revealed significant reductions in global warming potential (up to 91%), but challenges include increased operational intensity, human toxicity (up to 240%), and mineral resource scarcity (up to 229%). Vehicle kilometres travelled, and fleet replacement needs are key factors influencing long-term environmental impacts. Larger-scale implementation yields greater environmental benefits compared to smaller-scale deployment.

Automated driving developments should be considered when making decisions about investments in physical and digital infrastructure. This paper proposes four scenarios for automated driving developments in the Netherlands in 2040 and 2060 taking into account uncertainties regarding future penetration rates, the level of connectivity, the operational design domain, and the expected impacts of automated driving: 1) Late transition, 2) Automated vehicles on main roads, 3) Car-topia, and 4) Share-topia. To derive these scenarios, an extended switchboard method is introduced in which multiple driving forces for automated driving can be varied. The main driving forces were identified based on expert surveys. For each scenario, a modelling approach is used to compute the impact of automated driving on vehicle kilometres driven and congestion. The extended switchboard method offered more flexibility than existing scenario methods. The model-based impact assessment provided more conservative and probably more accurate insights into the expected impacts of automated driving on vehicle kilometres driven and congestion than expert estimates from the literature. The results show that in all scenarios automation leads to an increase in the number of trips, vehicle kilometres driven and congestion. In the scenarios with autonomous vehicles, congestion is expected to increase up to 17%. The higher the penetration rates of connected automated vehicles, the smaller the increase in congestion (1.5%-11%). The results indicate that investments in digital infrastructure are needed to prevent capacity reduction due to autonomous driving. The scenarios “car-topia” and “share-topia” may require additional physical infrastructure on motorways and regional roads, and/or the implementation of demand management strategies.

This paper presents the first results of a Delphi survey aimed at eliciting experts' opinions on possible scenarios of integration between passenger and freight transport enabled by a Mobility as a Service (MaaS) platform. Combining freight shipments with passenger trips is a promising addition to the MaaS business model that could help to reduce the number of freight vehicles and contribute to a more efficient use of passenger transport services and modes. The research objective of this paper is therefore to explore the feasibility of an extended version of MaaS including freight, called “MaaS for Passenger and Freight” (MaaS4PaF). Passenger and freight transport experts were asked to express their opinions with respect to the potential market penetration, business ecosystem, and implementation of this concept. Results allow to elaborate on opportunities and barriers, especially related to uncertain business models and the multitude of actors involved, and propose a research agenda to further investigate the feasibility and potential of MaaS for passenger and freight transport integration.