Subway safety anti-collision timetable optimization method based on hybrid intelligent algorithm

Abstract

The invention relates to a metro safety anti-collision timetable optimization method based on a hybrid intelligent algorithm, and the method comprises the steps: extracting inter-station operation data of a target line according to metro operation track data; the extracted data are preprocessed, and an algorithm for simulating the regenerative electric energy absorption amount is designed; calculating a lower bound of a train departure interval according to a safety anti-collision constraint condition between trains on the basis of given train station dwell time; with the maximum regenerative electric energy absorption amount between the trains as a target, the dwell time and departure interval of the trains on a target line are optimized by adopting a genetic and simulated annealing hybrid intelligent algorithm; wherein the genetic algorithm optimizes the departure interval under the given dwell time in the inner layer, and the simulated annealing algorithm optimizes the dwell time in the outer layer; meanwhile, in order to improve the calculation efficiency of the hybrid intelligent algorithm, an algorithm of approximate regenerative electric energy absorption amount of a backward feedback neural network is adopted. According to the method, the metro regenerative electric energy absorption amount can be increased, and the operation safety of the metro train is ensured.

Description

technical field [0001] The invention relates to an optimization method for a subway safety anti-collision timetable based on a hybrid intelligent algorithm, and belongs to the field of urban public transport systems. Background technique [0002] The subway has the characteristics of large transportation volume, fast speed, safety and punctuality, etc., but it also generates a lot of energy consumption during the operation process. The purpose of energy saving is expressed by optimizing the train time, which is important for improving the economic benefits of subway operating companies and reducing energy consumption. significance. [0003] In the actual operation of trains, due to the short distance between adjacent stations, drivers frequently brake and tow during driving, which increases the operating energy consumption and makes the enterprise's demand for the utilization of regenerative electric energy more urgent. However, most of the current researches rarely borrow ...

AI Technical Summary

Problems solved by technology

However, most of the current research rarely borrows the actual operation data of the subway when optimizing the subway operation timetable, which leads to many problems. The difference between the curves, which leads to the problem of inaccurate evaluation of the utilization of regenerative power between trains

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