Energy‐Efficient Train Operation Optimization Method for Urban Rail Intervals Based on Curve Splicing

Urban rail transit trains consume a significant amount of energy; therefore, reducing the operational energy consumption is of great importance for train energy‐saving efforts. To address this issue, and to avoid the limitations of solutions constrained by operational condition combination strategies and the combination explosion of schemes with interval‐by‐interval position searches under unrestricted operational strategies, an operation control scheme solution method based on curve splicing is proposed. This method involves selecting curve segments and continuously splicing and recombining them to efficiently generate new running curves without being restricted by the basic energy‐saving curve framework. Based on this optimization concept, a splicing strategy is developed that includes four parameter variables: the splice point location, splice point speed, splice relationship type, and control force magnitude. On this basis, a curve splicing optimization model is established, with the objective function being the minimization of the train’s operational energy consumption while meeting interval running time requirements. A two‐layer iterative optimization algorithm is designed based on the simulated annealing framework. Utilizing the data of Guangzhou Metro Line 2, the optimized scheme achieves energy savings of 11.713% in the Baiyun Cultural Square–Baiyun Park interval and 9.115% in the entire downward intervals.