Tensor Recovery Based Non-Recurrent Traffic Congestion Recognition

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationCICTP 2015 - Efficient, Safe, and Green Multimodal Transportation - Proceedings of the 15th COTA International Conference of Transportation Professionals
EditorsXuedong Yan, Yu Zhang, Yafeng Yin
PublisherAmerican Society of Civil Engineers (ASCE)
Pages591-603
Number of pages13
ISBN (Electronic)9780784479292
DOIs
Publication statusPublished - 2015
Event15th COTA International Conference of Transportation Professionals: Efficient, Safe, and Green Multimodal Transportation, CICTP 2015 - Beijing, China
Duration: 24 Jul 201527 Jul 2015

Publication series

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

Conference

Conference15th COTA International Conference of Transportation Professionals: Efficient, Safe, and Green Multimodal Transportation, CICTP 2015
Country/TerritoryChina
CityBeijing
Period24/07/1527/07/15

Keywords

  • Lowrank tensor
  • No-recurrent traffic congestion recognition
  • Sparse tensor
  • Tensor recovery

Fingerprint

Dive into the research topics of 'Tensor Recovery Based Non-Recurrent Traffic Congestion Recognition'. Together they form a unique fingerprint.

Cite this