A new type of sustainable operation method for urban rail transit: Joint optimization of train route planning and timetabling

As key components of the train operation plan, the train route and timetable have significant impacts on the transportation service offered by urban rail transit. To ensure the sustainable development of urban rail transit and address the mismatch between train capacity and passenger demand that arises from the staged preparation of train operation plan, this paper proposes a joint optimization solution for both train route planning and timetabling. Firstly, the set of candidate routes is generated based on the location of the turnaround stations on the line. Secondly, considering the time-varying passenger flow characteristics and the constraints of operation, a mixed integer nonlinear programming model is established for the joint optimization of train route planning and timetabling to minimize the operating cost and enhance passenger service level. A linearization method for the model is proposed. To solve large-scale problems, an improved adaptive large neighborhood search algorithm (ALNS) is designed accordingly. The proposed method's solution efficiency is compared with genetic algorithms (GA) and commercial solvers (CPLEX) through small case studies. Finally, the effectiveness of both the model and algorithm has been validated via a case study of Chongqing Metro Line 3 in China. Results show that the joint optimization model demonstrates more effective adaptability to the passenger flow characteristics while enhancing the quality of train operation plan compared with traditional methods. It further reduces the operating costs of enterprises and enhance the operational sustainability of urban rail transit.

Abstract licence: CC BY-NC-ND

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