Journal
IET INTELLIGENT TRANSPORT SYSTEMS
Volume 12, Issue 9, Pages 998-1004Publisher
WILEY
DOI: 10.1049/iet-its.2018.0064
Keywords
traffic engineering computing; Big Data; learning (artificial intelligence); road traffic; deep learning methods; transportation domain; transportation data; road sensors; probe; GPS; CCTV; incident reports; big data generation; traffic data; machine learning methods; transportation network representation; traffic flow forecasting; traffic signal control; automatic vehicle detection; traffic incident processing; travel demand prediction; autonomous driving; driver behaviours; deep learning systems
Funding
- Strategic Research Funding at the Data61, CSIRO, Australia
- NSW Premier's Innovation Initiative project
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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.
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