Review on Deep Learning in Intelligent Transportation Systems

Abstract

Intelligent transportation system (ITS) contributes to allocate transportation resources, from citywide ones to nationwide, more efficiently with the help of algorithms. Due to the fact that the ITS is a quite comprehensive field, it is necessary for researchers to have a better understanding of dominant methods and which one is proper for the targeted subjects of ITS. More importantly, with a knowledge of the remained challenges in developing ITS and relevant techniques, researchers may have a clearer direction to work on. To provide researchers with dedicated information on specific machine learning (ML) techniques used in object recognition and traffic prediction, two essential study subjects in ITS, this paper mainly focuses on deep learning and neural network (NN), one of widely-used ML algorithms, and aims to conduct a brief review on its recent applications in ITS, as well as to mine its potential usage. As a result, this review introduces some popular NN, convolutional neural network (CNN), long short-term memory (LSTM) network, gated recurrent unit (GRU) network, and their hybrid mechanism, first. Then their applications and performance in ITS are described. Finally, this paper discusses constraints on some of them and suggests some promising research directions.