Cluster-Based LSTM Network for Short-Term Passenger Flow Forecasting in Urban Rail Transit

• Published in: IEEE access Vol. 7; pp. 147653 - 147671
• Main Authors: Zhang, Jinlei, Chen, Feng, Shen, Qing
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; Cluster analysis; Clustering; Correlation; Deep learning; Forecasting; K-means clustering; LSTM; Mathematical model; Mathematical models; Passengers; Predictive models; Public transportation; Rails; Ridership; Road transportation; short-term passenger flow forecasting; Subway stations; Transportation networks; Transportation systems; Urban rail; urban rail transit; Vector quantization
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2941987

Short-term passenger flow forecasting is an essential component for the operation of urban rail transit (URT). Therefore, it is necessary to obtain a higher prediction precision with the development of URT. As artificial intelligence becomes increasingly prevalent, many prediction methods including the long short-term memory network (LSTM) in the deep learning field have been applied in road transportation systems, which can give critical insights for URT. First, we propose a novel two-step K-Means clustering model to capture not only the passenger flow variation trends but also the ridership volume characteristics. Then, a predictability assessment model is developed to recommend a reasonable time granularity interval to aggregate passenger flows. Based on the clustering results and the recommended time granularity interval, the LSTM model, which is called CB-LSTM model, is proposed to conduct short-term passenger flow forecasting. Results show that the prediction based on subway station clusters can not only avoid the complication of developing numerous models for each of the hundreds of stations, but also improve the prediction performance, which make it possible to predict short-term passenger flow on a network scale using limited dataset. The results provide critical insights for subway operators and transportation policymakers.