Deep Irregular Convolutional Residual LSTM for Urban Traffic Passenger Flows Prediction

• Published in: IEEE transactions on intelligent transportation systems 2020-03, Vol.21 (3), p.972-985
• Main Authors: Du, Bowen, Peng, Hao, Wang, Senzhang, Bhuiyan, Md Zakirul Alam, Wang, Lihong, Gong, Qiran, Liu, Lin, Li, Jing
• Format: Article
• Language: English
• Subjects: Convolution; Data models; Deep learning; importance sampling; irregular convolutional neural network; LSTM; Machine learning; Passengers; Periodic variations; Predictive models; Public safety; Public transportation; Safety management; Subway stations; Taxicabs; Traffic information; Traffic passenger flows prediction; Transfer stations; Transportation management; urban computing
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2019.2900481

Urban traffic passenger flows prediction is practically important to facilitate many real applications including transportation management and public safety. Recently, deep learning based approaches are proposed to learn the spatio-temporal characteristics of the traffic passenger flows. However, it is still very challenging to handle some complex factors such as hybrid transportation lines, mixed traffic, transfer stations, and some extreme weathers. Considering the multi-channel and irregularity properties of urban traffic passenger flows in different transportation lines, a more efficient and fine-grained deep spatio-temporal feature learning model is necessary. In this paper, we propose a deep irregular convolutional residual LSTM network model called DST-ICRL for urban traffic passenger flows prediction. We first model the passenger flows among different traffic lines in a transportation network into multi-channel matrices analogous to the RGB pixel matrices of an image. Then, we propose a deep learning framework that integrates irregular convolutional residential network and LSTM units to learn the spatial-temporal feature representations. To fully utilize the historical passenger flows, we sample both the short-term and long-term historical traffic data, which can capture the periodicity and trend of the traffic passenger flows. In addition, we also fuse other external factors further to facilitate a real-time prediction. We conduct extensive experiments on different types of traffic passenger flows datasets including subway, taxi and bus flows in Beijing as well as bike flows in New York. The results show that the proposed DST-ICRL significantly outperforms both traditional and deep learning based urban traffic passenger flows prediction methods.