Speed Prediction of Urban Rail Transit Trains Based on Random Forest & Neural Network

In order to improve the punctuality and safety of urban rail transit trains during operation and achieve accurate parking, it is necessary to track and predict the speed curve during the train operation. This paper firstly calculates the instantaneous power of the train based on the measured data, and then uses the random forest model to classify the interval according to the power curve, and then establishes a real-time prediction method for the speed curve of urban rail transit trains based on neural network for different intervals. The train speed prediction model is tested. The results of model testing on the simulation data and actual line data show that the proposed algorithm can effectively predict the speed curve of the train in real time, improve the accuracy of speed tracking control. The error is reduced by 57.7% compared with the traditional neural network model, and the error is reduced by 73.9% compared with the random forest regression model.