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Title: Urban traffic prediction from mobility data using deep learning
Authors: Liu, Zhidan
Li, Zhenjiang
Wu, Kaishun
Li, Mo
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: 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
Journal: IEEE Network
Abstract: 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.
ISSN: 0890-8044
DOI: 10.1109/MNET.2018.1700411
Rights: © 2018 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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