Chijia Liu

We investigate a prospective Ride‐Sharing Mobility‐on‐Demand system designed to replace a significant portion of private car usage in European cities within a few years with affordable services operated by Shared Autonomous Electric Vehicles. A key feature of such a system is its ability to handle a very large number of transportation requests. We address both vehicle routing and energy management from a strategic perspective. Our goals are to estimate the fleet size, design prototype routes, and manage energy costs, while accounting for statistical demand patterns, various charging modes, and time‐dependent electricity prices. To achieve this, we introduce a decomposition scheme, DECO, which separates routing from recharge scheduling but allows their interaction via an aggregated fleet activity profile. This approach helps maintain energy feasibility under daily demand variations. We conduct numerical experiments on the urban network of Clermont–Ferrand, France, using 1400 designated pickup and drop‐off nodes. The simulated system serves up to 300 000 passenger requests with 10‐seat SAEVs having a range of 200 km. Results show that over 1700 vehicles are needed to serve all requests while accounting for energy constraints, under an average occupancy of around five passengers during peak hours. Analysis of charging behavior highlights off‐peak recharging preferences and cost‐sensitive mode selection. We further validate our heuristic DECO by comparing it with a baseline heuristic and with exact Mixed Integer Linear Program (MILP) solutions on small instances, demonstrating its ability to produce high‐quality results.

In this paper, we study a prospective transportation system in which shared autonomous vehicles provide dial-a-ride service for large passenger demands in urban and rural areas. We address this very large-scale problem using a greedy insertion heuristic. Accompanying this heuristic, we propose a filtering system to speed up routing process. The approach includes two specific data structures used as filtering devices, three different filtering modules, and a halt mechanism to control the computation effort spent in exploring the search space for candidate vehicles. The approach also relies on an original route encoding that exploits the topology of the road network. Experimental results show that en-route vehicles can be significantly reduced in such a system, decreasing the vehicle number by more than . And thanks to the filtering system, the execution time can be reduced by almost compared to the classic best-fit insertion heuristic while maintaining a comparable level of solution quality.