Tensor Recovery Based Non-Recurrent Traffic Congestion Recognition

Huachun Tan, Qin Li, Yuankai Wu, Bin Ran, Baodi Liu

科研成果: 书/报告/会议事项章节会议稿件同行评审

3 引用 (Scopus)

摘要

It is critical to detect and recognize non-recurrent traffic congestion (NRC), which brings unexpected delays to commuters, companies and traffic operators. In this paper, we propose a tensor recovery based non-recurrent traffic congestion recognition (TR-NRC) model to detect and recognize non-recurrent traffic congestion by decomposing the observed travel time tensor into a low-rank tensor and a sparse tensor. A tensor model can fully utilize the intrinsic multiple correlations of travel time data. The sparse tensor represents unexpected congestion. Values of sparse tensors reveal the distribution of unexpected delays compared to expected travel time. The recovered low-rank tensor structure expresses the distribution of general expected travel time as an auxiliary product, which was unattainable in the traditional detection methods. Experimental results show that compared to previous matrix recovery based methods, our proposed method can not only detect unexpected congestion, but can also recognize the congestion patterns more effectively.

源语言英语
主期刊名CICTP 2015 - Efficient, Safe, and Green Multimodal Transportation - Proceedings of the 15th COTA International Conference of Transportation Professionals
编辑Xuedong Yan, Yu Zhang, Yafeng Yin
出版商American Society of Civil Engineers (ASCE)
591-603
页数13
ISBN(电子版)9780784479292
DOI
出版状态已出版 - 2015
活动15th COTA International Conference of Transportation Professionals: Efficient, Safe, and Green Multimodal Transportation, CICTP 2015 - Beijing, 中国
期限: 24 7月 201527 7月 2015

出版系列

姓名CICTP 2015 - Efficient, Safe, and Green Multimodal Transportation - Proceedings of the 15th COTA International Conference of Transportation Professionals

会议

会议15th COTA International Conference of Transportation Professionals: Efficient, Safe, and Green Multimodal Transportation, CICTP 2015
国家/地区中国
Beijing
时期24/07/1527/07/15

指纹

探究 'Tensor Recovery Based Non-Recurrent Traffic Congestion Recognition' 的科研主题。它们共同构成独一无二的指纹。

引用此