Emerging reservation-based travel technologies offer a promising solution to mitigate supply-demand mismatches in metro systems. This paper presents a framework to support metro operators by optimizing time-varying reservation slot allocation plans, passenger flow control strategies, and train schedules. The proposed approach ensures that passengers with reservations can directly access platforms and board the first available train services, while those without reservations are managed through effective passenger flow control strategies to optimize train capacity utilization. To address this, an integer nonlinear programming model is formulated, incorporating constraints that capture interactions between passengers with and without reservations, with the objective of minimizing passengers’ waiting time and line congestion. A hybrid algorithm is developed to improve computational efficiency, combining the adaptive large neighborhood search method with a commercial solver and incorporating valid inequalities tailored to the properties of the model. The effectiveness of the proposed approaches is demonstrated through numerical experiments using real-world operational data from the Beijing metro Batong line. Computational results indicate that the integrated optimization approach reduces the objective value by 6.19 % compared to a step-by-step optimization method, achieving better alignment of capacity with dynamic passenger flows. In addition, the extreme unfairness between reserved and unreserved passengers, where passengers with reservations have a 100 % service ratio compared to less than 20 % for unreserved passengers, is mitigated by increasing passenger waiting times by 3.51 % and line congestion by 0.51 %. Furthermore, the proposed algorithm efficiently solves large-scale and real-world instances, outperforming the state-of-the-art commercial solver.

Service design for shared micromobility services, such as e-mopeds, involves decisions on service location, fleet size, pricing, and resource allocation, which are typically based on potential demand estimates. In practice, however, potential users adopt the service gradually, leading to market penetration over time. Ignoring this gradual penetration can lead to suboptimal decisions and service failures. We propose a phased optimization model to maximize profitability over a planning horizon divided into discrete periods. The model jointly determines service locations, fleet size, and spatio-temporal resource allocation while ensuring a targeted service level. To model gradual adoption, we apply an extended Bass diffusion framework in which adoption depends on service quality, defined by accessibility and availability, and social influence, reflecting the impact of current users on others’ adoption. The phased deployment strategy aligns service expansion and investment decisions with adoption growth (market penetration) over multiple periods. Applied to a bike-sharing case in Manhattan, the proposed approach improves resource allocation and increases profitability by up to 47% compared with an upfront strategy under parameter misestimation. Furthermore, ignoring service quality in the adoption model leads to a profit loss of up to 54% when user expectations are high The results shed light on the value of synchronizing service design decisions with user adoption dynamics.

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.

This paper studies a coordinated service planning problem for public transport-oriented Mobility-as-a-Service (MaaS) systems under time-varying passenger demand. We consider the integrated optimization of schedules, vehicle compositions, stop patterns, pricing, the rebalancing strategy of modular units, and passenger routing in a multi-modal public transport network with metro and modular bus services. A public transport-oriented MaaS platform is modeled as a planning and coordination tool that recommends scheduling and pricing decisions to operators, rather than directly operating services or setting fares. To capture the interaction between supply-side service design and demand-side time-dependent passenger routing, we formulate a bi-objective mixed-integer nonlinear programming model that balances public welfare and financial sustainability. The model is reformulated as a single-objective optimization formulation via the ε-constraint method, and solved using a hybrid algorithm that combines Adaptive Large Neighborhood Search (ALNS) with GUROBI. Computational experiments on both small-scale and real-world instances demonstrate the effectiveness of the proposed approaches in supporting scalable, coordinated, and sustainable public transport planning within the MaaS framework and provide managerial insights.

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.

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.

The evolving field of electric moped sharing systems is shaped by various determinants influencing user preferences, including range anxiety, pricing strategies, and regulatory changes. Utilizing a stated preference approach with a hybrid choice model, this research explores how these factors, along with attitudinal constructs, impact user decisions. The findings reveal that remaining driving range plays a critical role, with significant individual variability in its sensitivity, while perceived range anxiety did not significantly influence choices. Recent changes in helmet regulations have shifted preferences towards faster vehicles. Furthermore, dynamic pricing strategies, such as adjusting ride or unlock fees, can incentivize the use of less desirable vehicles with lower battery range or aid in user-based relocation. Nevertheless, low-range vehicles are less likely to be chosen, even with incentives. These insights provide valuable guidance for operators of electric moped sharing system to improve fleet management and optimize user satisfaction through strategic pricing and battery management.

This paper addresses the challenges of charging infrastructure design (CID) for electrified public transport networks using Battery Electric Buses (BEBs) under conditions of sparse energy consumption data. Accurate energy consumption estimation is critical for cost-effective and reliable electrification but often requires costly field experiments, resulting in limited data. To address this issue, we propose two mathematical models designed to handle uncertainty and data sparsity in energy consumption. The first is a robust optimization model with box uncertainty, addressing variability in energy consumption. The second is a data-driven distributionally robust optimization model that leverages observed data to provide more flexible and informed solutions. To evaluate these models, we apply them to the Rotterdam bus network. Our analysis reveals three key insights: (1) Ignoring variations in energy consumption can result in operational unreliability, with up to 55% of scenarios leading to infeasible trips. (2) Designing infrastructure based on worst-case energy consumption increases costs by 67% compared to using average estimates. (3) The data-driven distributionally robust optimization model reduces costs by 28% compared to the box uncertainty model while maintaining reliability, especially in scenarios where extreme energy consumption values are rare and data exhibit skewness. In addition to cost savings, this approach provides robust protection against uncertainty, ensuring reliable operation under diverse conditions.

Ride-sharing services operated by transportation network companies (TNCs) have the potential to expand capacity and accommodate increasing urban mobility demands, presenting an alternative to traditional ride-hailing services. This study introduces a high-capacity ride-sharing (HCRS) system that leverages user-specific travel choices and incentive-based pricing schemes. This innovative system enhances the dynamic matching problem of HCRS by incorporating a nested choice model and dynamic fare adjustment strategies to boost profitability while encouraging shared travel behaviours. Additionally, a rolling horizon solution approach is employed, including a shared choice set generation algorithm for creating shared alternatives and an Adaptive Large Neighborhood Search (ALNS)-based method for optimal matching. By leveraging a real dataset from Beijing's ride-hailing services, this research underscores that the HCRS service can significantly improve system efficiency and service quality, achieving more than 10.44% reduction in operating costs, and reducing average fares (¥3.31) and emissions (3.49 kg) across various users, compared to traditional ride-hailing services. The findings also demonstrate that users' decision-making is profoundly affected by changes in incentives, highlighting the importance of incentive settings in enhancing user engagement and system performance.

Supply chain networks face the critical challenge of enhancing resilience to disruptions while controlling the costs associated with resilience improvements. In this paper, we introduce an adaptive resilience improvement framework designed to sustain material flow by responding dynamically to emerging network vulnerabilities. Our framework centers on the production chain as a core element in resilience planning, integrating vulnerability assessment and reinforcement strategies through a tri-level optimization model. This model adapts to the network's changing conditions by (i) incorporating disruption scenario generation as an integral part of the decision-making process, allowing for the dynamic identification of vulnerabilities, and (ii) optimizing reinforcement strategies in response to them. We demonstrate the framework's effectiveness through two distinct case studies: a steel supply chain, where production flexibility improves resilience by 30%, and a pharmaceutical supply chain affected by climate-related disruptions. Our computational results confirm the scalability and effectiveness of this approach in strengthening network-wide resilience as vulnerabilities evolve.

The rapid growth of the online food delivery industry has led to a significant increase in the number of delivery riders navigating urban streets, predominantly using bikes and e-bikes. This growth has been accompanied by a concerning rise in crashes involving these riders, posing a critical challenge for city authorities and policymakers. Promoting safer riding behavior, such as choosing safer routes while delivering food, can potentially reduce crash risks. With this motivation, this paper aims to evaluate the effectiveness of strategies that encourage riders to choose safer routes and estimate the value riders place on reducing the risk of road crashes. The paper presents a stated preference experiment conducted with food delivery riders in Amsterdam and Copenhagen to assess two targeted strategies: ’safety information’ and ’monetary incentives’, designed to encourage riders toward selecting safer routes. The results from the route choice model show that presenting information about safety against crashes on different routes and offering monetary incentives can effectively motivate riders to choose safer routes, even if these are longer. The trade-offs riders make between safer and shorter routes were quantified by calculating the Value of Risk Reduction (VRR) and Willingness to Accept (WTA) indicators, which offer valuable insights into riders’ safety preferences. These indicators highlight how much riders value risk reduction and the compensation required to choose safer routes. Furthermore, the findings reveal that factors related to riders’ working arrangements and socio-demographic profiles significantly influence their route choice decisions. The paper concludes with a discussion about the practical challenges associated with implementing the strategies to enhance rider safety and proposing potential solutions that can be useful for food delivery platforms and policymakers.

The shift from private vehicles to public and shared transport is crucial to reducing emissions and meeting climate targets. Consequently, there is an urgent need to develop a multi-modal transport trip planning approach that integrates public transport and shared mobility solutions, offering viable alternatives to private vehicle use. To this end, we propose a preference-based optimization framework for multi-modal trip planning with public transport, ride-pooling services, and shared micro-mobility fleets. We introduce a mixed-integer programming model that incorporates preferences into the objective function of the mathematical model. We present a meta-heuristic framework that incorporates a customized Adaptive Large Neighborhood Search algorithm and other tailored algorithms, to effectively manage dynamic requests through a rolling horizon approach. Numerical experiments are conducted using real transport network data in a suburban area of Rotterdam The Netherlands Model application results demonstrate that the proposed algorithm can efficiently obtain near-optimal solutions. Managerial insights are gained from comprehensive experiments that consider various passenger segments, costs of micro-mobility vehicles, and availability fluctuation of shared mobility.

This study introduces an optimization framework for deploying Mobile Fleet Inventories (MFIs) to address operational inefficiencies in on-demand delivery systems. Traditionally, these systems rely on stationary facilities to organize operations and manage resources. While stationary facilities provide stability and structured coverage, they are inherently rigid and struggle to adapt to the spatial and temporal fluctuations characteristic of urban service demand. By leveraging urban waterways, MFIs act as dynamic, mobile facilities, enabling real-time resource redistribution and offering greater flexibility to meet evolving demand patterns efficiently. We formulate the problem as a mixed-integer linear programming model to optimize MFI deployment, minimizing total system costs. The model incorporates both capital investments (e.g., MFI leasing and docking infrastructure) and operational expenses (e.g., rider idle time). Key decisions include determining the optimal number, placement of MFIs, and fleet size. To validate the approach, we apply it to a meal delivery platform in Amsterdam, demonstrating its practicality and scalability. Results show that implementing MFIs reduces overall system costs by 17% and decreases rider idle time by 35% compared to stationary facility operations. These findings underscore the transformative potential of MFIs to enhance the efficiency, sustainability, and adaptability of on-demand delivery systems in urban settings.

We study a class of assortment optimization problems where customers choose products according to the cross-nested logit (CNL) model and the number of products offered in the assortment cannot exceed a fixed number. Currently, no exact method exists for this NP-hard problem that can efficiently solve even small instances (e.g., 50 products with a cardinality limit of 10). In this paper, we propose an exact solution method that addresses this problem by finding the fixed point of a function through binary search. The parameterized problem at each iteration corresponds to a nonlinear binary integer programming problem, which we solve using a tailored Branch-and-Bound algorithm incorporating a novel variable-fixing mechanism, branching rule and upper bound generation strategy. Given that the computation time of the exact method can grow exponentially, we also introduce two polynomial-time heuristic algorithms with different solution strategies to handle larger instances. Numerical results demonstrate that our exact algorithm can optimally solve all test instances with up to 150 products and more than 90% of instances with up to 300 products within a one-hour time limit. Using the exact method as a benchmark, we find that the best-performing heuristic achieves optimal solutions for the majority of test instances, with an average optimality gap of 0.2%.