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.
关键词
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
Key words
integrated transportation/public transit route planning/adaptive large neighborhood search algorithm/modular autonomous vehicles/in-vehicle transfer strategy/battery swapping and charging plan
维普
万方数据