Optimization Of ATO Speed Curve Based On Genetic Algorithm

The most important thing of rail transit development is to make the trains operate automatically in a safe, smooth and efficient manner. Optimization of ATO speed curve is an effective guarantee of automatic train operation. Different train operating curve, corresponding to the speed of protection indicators, punctuality indicators, precise parking indicator, comfort indicators and energy consumption indicators are not the same. It is necessary to optimize the ATO speed curve in Automatic Train Control system. Currently, in order to improve the efficiency of train driving and train operating strategies and improve the train parking accuracy, train energy efficiency and passenger comfort under the premise of safe operation of trains, Various countries enterprises and scholars have begun to study the ATO system. However it is difficult to find the accurate model and the optimal speed curve to describe the operating of the train, because the train operating is multi-objective, complex, and non-linear. But the rapid development of optimization theory and intelligent algorithms provides strong support for the ATO speed curve optimization research.The paper focuses on the problem of optimization ATO speed curve. Firstly analyzes the train operation control strategies and optimization principles, combined with the performance evaluation indicators of automatic train operation curve, establish evaluation indicators model. Secondly, using the Analytic Hierarchy Process to establish the hierarchy structure for ATO system, through the method which combining with Entropy weight method to determine the objective weight and AHP to determine the subjective weight, obtain the weight allocation of the performance indicators which is based on AHP-Entropy method. Thirdly, through the description of the lines and train parameters, on the basis of the established evaluation indicators model, researching and designing algorithm which is optimization of ATO speed curve based on Genetic Algorithm has been optimized the ATO control strategy and speed curve; Finally, achieve the optimization of the ATO speed curve by simulation, make the train can automatically give the optimal speed curve which is most suitable for a given running environment and running lines. Ensure the train operating safety and smoothly, at the same time make the train satisfy the punctuality indicators, precise parking indicator, comfort indicators and energy consumption indicators. Verify the correctness and superiority of the idea through Tested by experiment and results analysis.