1.School of Economics and Management，Southwest Jiaotong University;2.School of Software，Jiangxi Normal University
The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)
China’s urban rail transit is developing rapidly. Short-term passenger flow prediction is of great significance for operational safety, network optimization, and then smart city building. While the urban rail passenger flow is cyclical and random in the aspect of temporal characteristics, passenger flows in certain time slots are similar and passenger flows at adjacent stations are spatially correlated. Considering the above spatiotemporal characteristics, this research proposes a deep learning model k-ConvLSTM for urban rail short-term passenger flow prediction based on ConvLSTM and the adaptive k-means clustering algorithm. Experiments are designed to optimize the key parameters of the model. Also, in order to examine the performance of proposed model, abundant experiments are conducted based on the real passenger flow data of the Shenzhen Metro IC card. The results show that proposed k-ConvLSTM model performs better than deep learning models that only consider spatiotemporal characteristics——parallel architecture comprising convolutional network(CNN)and long short-term memory network(LSTM), ConvLSTM, and deep learning models that only consider temporal characteristics——LSTM and bi-directional long short-term memory network(Bi-LSTM), and shallow learning models——back propagation neural network(BPNN) and support vector regression model ( SVR), in terms of root mean square error, mean absolute error and mean absolute percentage error.