ST-LSTM: A Deep Learning Approach Combined Spatio-Temporal Features for Short-Term Forecast in Rail Transit

• Published in: Journal of advanced transportation Vol. 2019; no. 2019; pp. 1 - 8
• Main Authors: Tang, Qicheng, Yang, Ying, Yang, Mengning
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2019; Hindawi; Hindawi Limited; Wiley
• Subjects: Accuracy; Airports; Deep learning; Feature extraction; Information systems; Intelligent transportation systems; Long short-term memory; Mathematical models; Methods; Neural networks; Passengers; Rail transportation; Researchers; Short term; Temporal variations; Time series; Transportation; Transportation networks; Transportation planning
• ISSN: 0197-6729; 2042-3195
• DOI: 10.1155/2019/8392592

The short-term forecast of rail transit is one of the most essential issues in urban intelligent transportation system (ITS). Accurate forecast result can provide support for the forewarning of flow outburst and enables passengers to make an appropriate travel plan. Therefore, it is significant to develop a more accurate forecast model. Long short-term memory (LSTM) network has been proved to be effective on data with temporal features. However, it cannot process the correlation between time and space in rail transit. As a result, a novel forecast model combining spatio-temporal features based on LSTM network (ST-LSTM) is proposed. Different from other forecast methods, ST-LSTM network uses a new method to extract spatio-temporal features from the data and combines them together as the input. Compared with other conventional models, ST-LSTM network can achieve a better performance in experiments.