期刊
IEEE NETWORK
卷 32, 期 4, 页码 40-46出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MNET.2018.1700411
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资金
- NSF of SZU [2018061]
- China NSFC [61601308, 61472259]
- Guangdong NSF [2017A030312008]
- Guangdong Provincial Science and Technology Development Special Foundation [2017A010101033]
- Shenzhen Science and Technology Foundation [JCYJ20170302140946299, JCYJ20170412110753954]
- Fok Ying-Tong Education Foundation for Young Teachers in the Higher Education Institutions of China [161064]
- Guangdong Talent Project [2014TQ01X238, 2015TX01X111]
- GDUPS
- ECS grant from the Research Grants Council of Hong Kong [CityU 21203516]
- GRF grant from the Research Grants Council of Hong Kong [CityU 11217817]
- Singapore MOE Tier 2 grant [MOE2016-T2-2-023]
- NTU CoE [M4081879]
Traffic information is of great importance for urban cities, and accurate prediction of urban traffics has been pursued for many years. Urban traffic prediction aims to exploit sophisticated models to capture hidden traffic characteristics from substantial historical mobility data and then makes use of trained models to predict traffic conditions in the future. Due to the powerful capabilities of representation learning and feature extraction, emerging deep learning becomes a potent alternative for such traffic modeling. In this article, we envision the potential and broard usage of deep learning in predictions of various traffic indicators, for example, traffic speed, traffic flow, and accident risk. In addition, we summarize and analyze some early attempts that have achieved notable performance. By discussing these existing advances, we propose two future research directions to improve the accuracy and efficiency of urban traffic prediction on a large scale.
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