Deep Learning Techniques for Traffic Flow Prediction in Intelligent Transportation System: A Survey

Abstract

Intelligent Transportation System (ITS) intends to provide progressive services linking different modes of transportation and related service system. Road transportation is one of the major and complex entity of the traffic management system. Traffic flow prediction (TFP) contributessignificantrole inpredicting various parameters for road transportation thatgenerates stochastic and nonlineardata through sensors. Traffic flow is demarcated as the average number of vehicles present in a specific region given the historical flow data.Accurate forecasting of macroscopic parameters such as volume, density, speed and flowof traffic can improve the efficiency of traffic management system.In order to predict traffic flow, spatial and temporal traffic features act as a raw data input for the predicting models. Most of the shallow models are incapable to reveal bothspatiotemporal information in big data. Deep neural networks (DNNs) have recently highlighted the potentiality of capturing andextracting important features for various application frommassive dataset.Our work depictsthe current state-of-the-art deep learning techniques (DL) and itsinspirations in TFP incorporating various contextual factorssuch as construction zones, weather conditions, special events, traffic incidents, weekdays and holidays apart from spatiotemporalfeatures for predicting the flow.Finally, provides open challengesyet to be exploredfurther for enhancing deep learning techniques and approachesin forecasting accurate flow.