A Review on Machine Learning in Intelligent Transportation Systems Applications

Abstract

With the expansion of transportation networks and the advancement of embedded and communication technologies, research on intelligent transportation systems has gained immense interest from the research community, especially utilizing the potential of machine learning techniques for increasing efficiency, safety, and travel experience. This paper presents a detailed review of intelligent transportation systems applications using machine learning techniques. Initially, 11 popular applications were selected for further study by focusing on four key areas: traffic management, safety management, infotainment and comfort, and autonomous driving. To explore the current trends of each application, 48 recent proposals using machine learning techniques that have gained high attention have been selected by following some selection criteria. The selected proposals have been discussed in detail, focusing on the proposed methods and contributions. After a detailed review, 10 potential issues have been identified and addressed, which could lead to the development of more efficient and optimized intelligent transportation systems solutions. Overall, the review serves as a valuable guide for researchers in identifying the current research trends in popular intelligent transportation systems applications using machine learning, pinpointing the gaps and developing more attractive solutions.