Multisensor Track Occupancy Detection Model Based on Chaotic Neural Networks

Abstract

Bad shunting of track circuit is one of the major risks for railway traffic safety. The occupancy of track will not be correctly detected due to bad shunting, which could severely degrade the efficiency of the train dispatching command, sometimes even causing serious accidents, such as train collision and derailment. To handle the bad shunting problem, the Three Points Test Method is commonly used for detecting track occupancy. However, this method completely relies on manual confirmation and it thus usually leads to low detection efficiency and high labor intensity. In order to improve the detection efficiency and involve as less human labors as possible, this paper proposes a multisensor track occupancy detection model which is based on chaotic neural networks. This model uses the detection results of track occupancy collected by multiple sensors as the fundamental data, and then it calculates their weights using chaotic neural networks for data fusion, and finally the model determines whether the track is occupied. Experimental results and field tests demonstrate that the proposed model is able to provide track occupancy detection with high effectiveness and efficiency. Moreover, the accuracy of detection reaches 99.9999%, which can help to greatly reduce the labor intensity of manual confirmation.