Modular vehicles (MV) possess the ability to physically connect/disconnect with each other and travel in platoon with less energy consumption. A fleet of demand-responsive transit vehicles with such technology can serve passengers door to door or have vehicles deviate to platoon with each other to travel at lower cost and allow for en-route passenger transfers before splitting. A mixed integer linear programming (MILP) model is formulated to solve this “modular dial-a-ride problem” (MDARP). A heuristic algorithm based on Steiner-tree-inspired large neighborhood search is developed to solve the MDARP for practical scenarios. A set of small-scale synthetic numerical experiments are tested to evaluate the optimality gap and computation time between exact solutions of the MDARP using commercial software and the proposed heuristic. Large-scale experiments are conducted on the Anaheim network with up to 75 vehicles and 150 requests considering 378 candidate join/split nodes to further explore the potentials and identify the ideal operation scenarios of MVs. The results show that MV technology can save up to 52% in vehicle travel cost, 41% in passenger service time, and 29% in total cost against existing on-demand mobility services in the scenarios tested. Results suggest that MVs best benefit from platooning by serving “enclave pairs” as a hub-and-spoke service.

模块化车辆 (MV) 具有相互物理连接/断开连接的能力，并以较少的能源消耗成排行驶。拥有这种技术的需求响应型运输车辆车队可以挨家挨户地为乘客提供服务，或者让车辆相互排成一列，以较低的成本出行，并允许在分路之前在途中进行乘客换乘。制定了混合整数线性规划 (MILP) 模型来解决此“模块化拨号问题”(MDARP)。开发了一种基于受斯坦纳树启发的大邻域搜索的启发式算法来解决实际场景中的 MDARP。测试了一组小规模的综合数值实验，以评估使用商业软件的 MDARP 精确解与所提出的启发式之间的最优性差距和计算时间。考虑到 378 个候选加入/拆分节点，在 Anaheim 网络上进行了多达 75 辆车辆和 150 个请求的大规模实验，以进一步挖掘潜力并确定 MV 的理想运行场景。结果表明，在测试的场景中，与现有的按需移动服务相比，MV 技术可以节省高达 52% 的车辆出行成本、41% 的乘客服务时间和 29% 的总成本。结果表明，通过将“飞地对”作为轴辐式服务提供服务，MV 可以最大程度地受益于编队行驶。在测试的场景中，现有按需移动服务的总成本为 29%。结果表明，通过将“飞地对”作为轴辐式服务提供服务，MV 可以最大程度地受益于编队行驶。在测试的场景中，现有按需移动服务的总成本为 29%。结果表明，通过将“飞地对”作为轴辐式服务提供服务，MV 可以最大程度地受益于编队行驶。