Wei Zhang

Urban air mobility (UAM) can bypass congested ground corridors and provide substantial travel-time savings even before its supporting vertiport network is sufficiently dense to minimize ground access and egress. We study this mismatch between service attractiveness and infrastructure maturity in a setting where ride-sourcing provides both the direct ground alternative and UAM feeder service. Passengers differ in direct ride-sourcing travel time, so UAM selectively attracts travelers facing relatively time-consuming ground trips. We develop a two-stage model in which vertiport density is chosen before fares and passenger mode choice and compare decentralized air-ground provision with platform integration and a no-UAM benchmark. When pickup and repositioning are sufficiently efficient, UAM reduces ride-sourcing vehicle-hours traveled even with sparse vertiport coverage. Its effect on vehicle-kilometers traveled (VKT), however, changes sign at a unique vertiport-density threshold: below the threshold, long feeder movements increase VKT; above it, UAM reduces VKT. At a fixed network, integration lowers the door-to-door UAM fare and magnifies the prevailing VKT effect by shifting more passengers toward UAM. When vertiport investment is endogenous, integration also raises the private return to coverage and induces a denser network. For intermediate deployment costs, only the integrated network crosses the VKT threshold, reversing the sign of UAM's mileage effect relative to decentralized operation. A New York City application exhibits the same reversal: integration increases VKT on a fixed two-vertiport network but makes a third vertiport privately worthwhile, yielding lower VKT once infrastructure responds. Deployment support and a UAM fare instrument address the remaining coverage and passenger-allocation gaps. These results show that the transportation effect of platform integration cannot be assessed independently of the infrastructure investment incentives it creates.

Electric buses have limited driving range and must regularly charge their batteries at depots during daily operations. However, in multi-depot systems, some depots located in dense urban areas face severe space constraints and can accommodate only a limited number of chargers. Consequently, electric buses returning to such depots may have to wait for a period of time before accessing a charger, thereby delaying subsequent trips. Under such practical conditions, bus scheduling and charging decisions are tightly coupled and must be optimized jointly. Therefore, a multi-depot electric bus scheduling and charging problem with limited charging resources is formulated in this paper, in which bus trip assignments and charging schedules are optimized simultaneously.Owing to the route-based set-partitioning structure of the problem, where each feasible EB schedule can be represented as a column covering a subset of trips with embedded charging decisions, a tailored branch-and-price algorithm is developed to obtain exact solutions. However, conventional bidirectional labeling methods incorporating partial recharging cannot be directly applied to the pricing problem, as limited chargers and nonlinear charging profiles make it difficult to determine feasible charging and departure times during label extension. To overcome this challenge, the proposed BP algorithm embeds a forward–backward recursive extension method with tailored charging decision rules into the labeling process. By recursively linking battery level, charger availability, charging duration and EB readiness time in both directions, the method determines optimal charging and departure times without generating excessive non-dominated labels.Computational experiments based on realistic instances demonstrate that the proposed algorithm is more efficient than the conventional branch-and-price algorithm with the partial charging process. The impacts of battery capacity of EBs, number of chargers at the depot, and the charging speed of chargers on bus system performance are discussed to obtain some managerial insights.

Disruptions to high-speed train operations result in deviations from the official timetable and train delays, leading to scheduling conflicts. A conflict-free rescheduled timetable and corresponding executable speed profiles are essential to restore disrupted train operations. In this paper, we introduce an integrated framework for the train rescheduling and speed management problem (TRSMP) during disruptions caused by partial segment blockages. Under a flexible rescheduling strategy, affected trains can switch to the opposite track to bypass the blockage. A mixed-integer linear programming model is formulated to simultaneously optimize train times, orders, routes, and speed profiles, aiming to minimize the total deviation time and traction energy consumption. To reduce the complexity of the integrated model, we decompose the model into a reformulated integer programming model and a series of train operation plans. This decomposition approach transforms the TRSMP into a train plan selection problem, effectively decoupling both the interactions among trains and interdependencies between rescheduling and speed management. Moreover, we develop an adaptive algorithm with acceleration techniques to speed up the solving of the reformulated model. Case studies based on the Chinese high-speed railway show that our decomposition approach provides near-optimal solutions within acceptable computational time in large-scale instances. Our integrated method outperforms the sequential optimization method in both solution time and feasibility guarantees of speed profiles. The proposed rescheduling strategy exhibits enhanced effectiveness in mitigating delays while preserving line capacity during partial blockages. Funding: This research was supported by the National Natural Science Foundation of China [Grants 72288101, 72331001, 72571152, 72322002, and U2469201], and the 111 Center [Grant B20071]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2025.0137 .