Optimizing high-speed train tracking intervals with an improved multi-objective grey wolf

With the rapid advancement of China’s high-speed rail network, the density of train operations is on the rise. To address the challenge of shortening train tracking intervals while enhancing transportation efficiency, the multi-objective dynamic optimization of the train operation process has emerged as a critical issue.

Train dynamic model is established by analyzing the force of the train in the process of tracing operation. The train tracing operation model is established according to the dynamic mechanical model of the train tracking process, and the dynamic optimization analysis is carried out with comfort, energy saving and punctuality as optimization objectives. To achieve multi-objective dynamic optimization, a novel train tracking operation calculation method is proposed, utilizing the improved grey wolf optimization algorithm (MOGWO). The proposed method is simulated and verified based on the train characteristics and line data of CR400AF electric multiple units.

The simulation results prove that the optimized MOGWO algorithm can be computed quickly during train tracks, the optimum results can be given within 5s and the algorithm can converge effectively in different optimization target directions. The optimized speed profile of the MOGWO algorithm is smoother and more stable and meets the target requirements of energy saving, punctuality and comfort while maximally respecting the speed limit profile.

The MOGWO train tracking interval optimization method enhances the tracking process while ensuring a safe tracking interval. This approach enables the trailing train to operate more comfortably, energy-efficiently and punctually, aligning with passenger needs and industry trends. The method offers valuable insights for optimizing the high-speed train tracking process.