关键词: 城市交通, 扰动恢复, 梯度搜索, 电动公交运营, 时空多维度, 排班一致性

Abstract: Electric bus operations are vulnerable to disruptions, creating complex recovery challenges that necessitate balancing economic costs, battery constraints, and the consistency of driver schedules. To address this, this study proposes a spatiotemporal optimization model for electric bus disruption recovery. Unlike traditional rescheduling frameworks for fuel-based buses, this model accounts for dynamic battery consumption and charging decisions under disrupted conditions. A key innovation of the study is introducing the "spatiotemporal consistency" as a constraint, which is designed to maintain the stability of driver schedules in both time and space. To solve the associated Mixed-Integer Non-Linear Programming (MINLP) problem efficiently, the study proposes a customized gradient heuristic search algorithm. Simulation results based on the real-world bus network of Hengshui City show that the model produces feasible recovery plans rapidly across three typical scenarios: severe weather, sudden accidents, and cumulative disrupted events. The recovery duration was successfully controlled within a range of 95 to 153 minutes. Ultimately, the results demonstrate that the proposed model reduces operating costs and quickly generates recovery plans while safeguarding the spatiotemporal consistency of schedules, proving its adaptability in complex operational environments.

Key words: urban transportation, disruption recovery, gradient search, electric bus operation, spatio-temporal dimensions; schedule consistency