Dynamic Scheduling Method of High-speed Trains Based on Improved Particle Swarm Optimization

During the operation of high-speed trains, high-speed rail trains often run turbulently due to bad weather, foreign objects intrusion, train failures, and other emergencies, resulting in the inability of high-speed trains to operate in accordance with the basic operation diagram. Aiming at the dynamic dispatching problem of late arrivals of high-speed rail trains, a mathematical model for the dynamic dispatching of high-speed rail trains at late train arrivals was established with the aim of minimizing the late arrival time of trains, late departures from stations, and the number of train delays. A dynamic weighted particle swarm optimization algorithm is proposed to solve the model. Taking Nanjing South to Cangzhou section as an example, simulation experiments were conducted under the conditions of changes in the number of trains, train delays, and late train stations. The experimental results show that the proposed algorithm can quickly restore the train to normal operation and has strong real-time and high efficiency.

I. Introduction

With the development of the world, high-speed railways in China have developed rapidly [1] [2]. However, due to the impact of sudden events such as heavy rain, blizzards, and dust storms, high-speed trains will run out of order during the course of operation, causing the actual state of the train inconsistent with the predetermined value. This requires the dynamic dispatching of trains, so dynamic scheduling is the core of railway traffic dispatching and commanding work, and determines the quality of the work organization in the section. It has important research significance.