BBAuthors: Zheng, Chuanpan; Advisor: -; Participants: Fan, Xiaoliang; Wen, Chenglu; Chen, Longbiao; Wang, Cheng; Li, Jonathan (2020)
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 Pre... 
