DeepSTD: Mining Spatio-Temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction

• Published in: IEEE transactions on intelligent transportation systems Vol. 21; no. 9; pp. 3744 - 3755
• Main Authors: Zheng, Chuanpan, Fan, Xiaoliang, Wen, Chenglu, Chen, Longbiao, Wang, Cheng, Li, Jonathan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Context; Deep learning; Disturbances; Intelligent transportation systems; Machine learning; Meteorology; Modelling; Neural networks; Predictive models; spatio-temporal disturbances; Three-dimensional displays; Traffic flow; Traffic flow prediction; Traffic models; Urban areas; Weather
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2019.2932785

Deep learning techniques have been widely applied to traffic flow prediction, considering underlying routine patterns, and multiple context factors (e.g., time and weather). However, the complex spatio-temporal dependencies between inherent traffic patterns and multiple disturbances have not been fully addressed. In this paper, we propose a two-phase end-to-end deep learning framework, namely DeepSTD to uncover the spatio-temporal disturbances (STD) to predict the citywide traffic flow. In the STD Modeling phase, we propose an STD modeling method to model both the different regional disturbances caused by various region functions and the spatio-temporal propagating effects. In the Prediction phase, we eliminate the STD from the historical traffic flow to enhance the leaning of inherent traffic patterns and combine the STD at the prediction time interval to consider the future disturbances. The experimental results on two real-world datasets demonstrate that DeepSTD outperforms the state-of-the-art methods.