Abstract:

With the rapid growth of China's economy and the continuous urbanization, rail transit plays an increasingly important role in residents' travel. As a key factor affecting the operation efficiency and service level of urban rail transit,accurate passenger flow prediction has attracted increasing attention from operation managers and researchers. To improve the prediction accuracy of the urban rail transit passenger flow, this paper combines sparrow search algorithm (SSA) and long short-term memory network (LSTM) and proposed a SSA-LSTM combined model. Based on the passenger flow data obtained from four stations of Hangzhou Metro Line 1 and the selected factors affecting the rail transit passenger flow, we used the proposed SSA-LSTM model to predict the short-term passenger flow of relevant stations. Then, we compared the predicted results with those estimated by the LSTM, GA-LSTM, and PSO-LSTM models. Results show that the prediction accuracy of the proposed model is 16.0%, 8.8%, and 2.3%, higher than the aforementioned models, respectively; furthermore, the proposed method exhibited better performance in terms of the root mean square error. Thus, the proposed model has potential applicationin predicting the urban rail transit passenger flow. Moreover, it can assistoperation managers in improving the operation efficiency and service level of urban rail transit.

Key words: urban rail transit, short term passenger flow prediction, sparrow search algorithm, long short term memory network, combined model