Deep learning methods in transportation domain: a review

Abstract

Recent years have seen a significant amount of transportation data collected from multiple sources including road sensors, probe, GPS, CCTV and incident reports. Similar to many other industries, transportation has entered the generation of big data. With a rich volume of traffic data, it is challenging to build reliable prediction models based on traditional shallow machine learning methods. Deep learning is a new state-of-the-art machine learning approach which has been of great interest in both academic research and industrial applications. This study reviews recent studies of deep learning for popular topics in processing traffic data including transportation network representation, traffic flow forecasting, traffic signal control, automatic vehicle detection, traffic incident processing, travel demand prediction, autonomous driving and driver behaviours. In general, the use of deep learning systems in transportation is still limited and there are potential limitations for utilising this advanced approach to improve prediction models.