Research Area:  Machine Learning

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.

Keywords:  
Transportation Domain
traffic flow forecasting
traffic signal control
automatic vehicle detection
traffic incident processing
travel demand prediction
autonomous driving
Deep Learning
Machine Learning

Author(s) Name:  Hoang Nguyen, Le-Minh Kieu, Tao Wen, Chen Cai

Publisher name:  The Institution of Engineering and Technology

DOI:  10.1049/iet-its.2018.0064

Volume Information:  Volume12, Issue9 November 2018 Pages 998-1004

Paper Link:   https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-its.2018.0064