A Novel Real-Time Algorithm for Optimizing Train Speed Profiles Under Complex Constraints

Abstract

Energy-efficient speed profile optimization of train operations is challenging due to complex constraints including passage points and speed limits. Moreover, it is critical to perform real-time re-optimization of speed profiles within a short control period. To speed up the speed profile re-optimization with complex constraints, we introduce various train characteristics into the shortest path problem with time windows (SPPTW) from the literature, and formulate energy-efficient optimization as a train-based SPPTW (TRAIN-SPPTW). This reduces the solution space and leads to reduced computation complexity. To further reduce computational time for solving the Train-SPPTW, we take advantage of the preprocessing + query structure used in existing pulse algorithms (PA) on one hand, and further overcome their limitations in computation efficiency on the other hand by proposing a tour-adaptive partial-bounding pulse algorithm (TPPA) to shorten the total time of the preprocessing and query stages. We use a simulation study on the tracks of the Dongguan-Huizhou intercity railway in China to demonstrate that the proposed TPPA reduces the computational time by at least 60 % compared to three existing algorithms without sacrificing the quality of the solution.