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考虑充换电的模块化需求响应公交路径优化

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模块车能通过中途分离与合并来调整车队容量、实现无缝换乘,兼具规模化与"门到门"灵活性优势,但其轻量化的电池设计也在一定程度上限制了车辆续航能力.为了探索模块车在需求响应公交中的应用,并解决车辆中途充电问题,本文建立了模块化需求响应公交路径规划模型,优化车辆路径计划、车队编组策略、车内换乘策略以及换电和机会充电计划.针对模型特征设计了改进的自适应大邻域搜索算法,根据各车辆路径之间需要进行编组和协同交互的特点,定制化设计了车队类修复算子和能源类修复算子等.使用安徽宣城的出行数据进行实验,结果显示:与传统公交相比,模块化需求响应公交系统使乘客总出行用时降低48.81%;与车辆单独运行的方案相比,车队编组方案能够使系统总成本平均降低13.24%;相比仅充电策略,充换电结合策略能在少量增加备用电池固定成本的情况下,使能源成本减少21.09%;此外,企业可以通过调整等待时间惩罚系数来平衡企业经营成本与乘客时间成本,达到动态最优.
Optimization of modular demand-responsive transit routes considering charging and battery swapping
Modular vehicles can adjust their fleet capacity and achieve seamless transfers through mid-journey separation and merging by combining the advantages of scalability and"door-to-door"flexibility.However,their lightweight battery design limits their cruising range to a certain extent.To investigate the application of modular vehicles in demand-responsive transit and address the issue of mid-journey recharging,this study establishes a modular demand-responsive transit path planning model.It optimizes the vehicle-route planning,fleet-formation strategies,in-vehicle transfer strate-gies,battery swapping,and charging plans.An improved adaptive large neighborhood search algo-rithm is designed for the model characteristics,while fleet-type and energy-type repair operators are custom designed based on the requirement for coordination and interaction between vehicle routes.Experiments using travel data from Xuancheng,Anhui show that compared with the typical transit,the modular demand-responsive transit system reduces the total passenger travel time by 48.81%.Compared with the scenario where vehicles operate individually,the fleet formation of the proposed system can reduce the total cost by an average of 13.24%,whereas the combined charging and swap-ping strategy can reduce energy costs by 21.09%with a slight increase in the fixed cost of spare bat-teries.Companies can balance between business operating and passenger time costs by adjusting the waiting-time penalty coefficient,thus achieving dynamic optimization.

integrated transportationpublic transit route planningadaptive large neighborhood search algorithmmodular autonomous vehiclesin-vehicle transfer strategybattery swapping and charging plan

郭梅雪、靳文舟、巫威眺

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华南理工大学,土木与交通学院,广州 510641

综合运输 公交线路规划 自适应大邻域搜索算法 模块化自动驾驶汽车 车内换乘 充换电规划

国家自然科学基金项目国家自然科学基金项目

5207212872071079

2024

10.19961/j.cnki.1672-4747.2024.03.020

交通运输工程与信息学报
西南交通大学

交通运输工程与信息学报

CSTPCD
影响因子：0.446
ISSN：1672-4747
年,卷(期)：2024.22(3)