Abstract
Traffic prediction problems concern the mobility of human crowds in urban city landscapes. The study of these problems is crucial to improve the efficiency of traffic flows in urban networks and people’s quality of life. Deep learning, in recent years, has achieved impeccable results and outperformed traditional statistical methods for solving traffic-related problems in the literature. However, deep learning suffers from poor interpretability. The aim of this dissertation is to investigate deep learning models applied in the traffic domain, and interpret the behaviors of the models by analyzing salient global feature importance and specific input-to-output relationship. First, a lit erature review of traffic prediction problems and deep learning models is included, followed by a review of analyzer methods for interpreting deep learning models. Sec ond, we explore the usage of Twitter’s tweets as a new source of additional information in the new digital age to improve the performance of a crowd flow prediction model ST-ResNet on the city-wide crowd flow prediction task, as well as to provide addi tional context to the prediction in the form of human natural language. Third, we take inspirations from deep learning attribution methods such as Integrated Gradients and SmoothGrad, and proposed a novel improved method, SmoothTaylor, which is derived from the Taylor’s theorem. Finally, we discuss future work as we share some prelim inary applied results of the above attribution methods on the traffic status prediction for graph-based traffic status prediction mode