Urban traffic prediction from mobility data using deep learning
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...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/140307 |
| _version_ | 1811677764513693696 |
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| author | Liu, Zhidan Li, Zhenjiang Wu, Kaishun Li, Mo |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Liu, Zhidan Li, Zhenjiang Wu, Kaishun Li, Mo |
| author_sort | Liu, Zhidan |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T02:42:33Z |
| format | Journal Article |
| id | ntu-10356/140307 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:42:33Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1403072020-05-28T02:07:49Z Urban traffic prediction from mobility data using deep learning Liu, Zhidan Li, Zhenjiang Wu, Kaishun Li, Mo School of Computer Science and Engineering Engineering::Computer science and engineering Data Models Predictive Models 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. MOE (Min. of Education, S’pore) 2020-05-28T02:07:48Z 2020-05-28T02:07:48Z 2018 Journal Article Liu, Z., Li, Z., Wu, K., & Li, M. (2018). Urban traffic prediction from mobility data using deep learning. IEEE Network, 32(4), 40-46. doi:10.1109/MNET.2018.1700411 0890-8044 https://hdl.handle.net/10356/140307 10.1109/MNET.2018.1700411 2-s2.0-85054863526 4 32 40 46 en IEEE Network © 2018 IEEE. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Data Models Predictive Models Liu, Zhidan Li, Zhenjiang Wu, Kaishun Li, Mo Urban traffic prediction from mobility data using deep learning |
| title | Urban traffic prediction from mobility data using deep learning |
| title_full | Urban traffic prediction from mobility data using deep learning |
| title_fullStr | Urban traffic prediction from mobility data using deep learning |
| title_full_unstemmed | Urban traffic prediction from mobility data using deep learning |
| title_short | Urban traffic prediction from mobility data using deep learning |
| title_sort | urban traffic prediction from mobility data using deep learning |
| topic | Engineering::Computer science and engineering Data Models Predictive Models |
| url | https://hdl.handle.net/10356/140307 |
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