Ridesplitting optimization is one of the hot issues in the studies of on-demand ride services. An efficient ridesplitting optimization model can provide an insightful basis for the platform’s decision-making and management. In reality, on-demand ride service orders include pre-trip reservation orders and real-time orders. This paper proposes a grouping approach to ridesplitting optimization for pre-trip reservation orders in a large-scale urban road network. In order to improve the ridesplitting rate, we combine reservation orders into travel groups which can be dispatched to vehicles and model this process as an integer programming problem. Both greedy algorithm and CPLEX are used to solve the problem and get the travel groups. Furthermore, we adopt the CPLEX to solve the dispatching problem between vehicles and travel groups by minimizing the total waiting time of passengers. Finally, the proposed approach is applied to a numerical experiment in a large-scale network based on real-world trip order data of DiDi Chuxing. The results show that our model can effectively improve the ridesplitting rate.

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