Trajectory Prediction of Urban Rail Transit Based on Long Short-Term Memory Network

Despite the development of rail transit, urban transport capacity has increased substantially. It still faces severe jams during peaking operation periods. In this paper, inspired by artificial intelligence methods, we proposed a new data-driven method for the dynamic trajectory prediction of the train in front based on the long short-term memory(LSTM) network. we extracted train operation data from the ATO equipment of Chengdu Metro Line 8 and used three evaluation indicators for predicting the loss of the trajectory and the accuracy analysis. The experiments indicate that compared with the Kalman filter model, our method shows more robust stability and higher accuracy.