A Congestion Diffusion Model with Influence Maximization for Traffic Bottlenecks Identification in Metrocity Scales

Baoxin Zhao, Chengzhong Xu, Siyuan Liu, Juanjuan Zhao, Li Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Traffic bottlenecks identification plays an important role in traffic planning and provides decision-making for prevention of traffic congestion. Although traffic bottlenecks widely exist, they are difficult to predict because of the changing traffic condition and traffic demand. In this paper, we introduce a traffic congestion diffusion (TCD) model with traffic flow influence (TFI) to capture the traffic dynamics and give a panoramic view for the city by cross domain data fusion. We proposed novel definition of bottleneck from the perspective of influence spread under TCD. The bottlenecks identification problem is modeled as an influence maximization problem, i.e., selecting the top K influential nodes in road networks under certain traffic conditions. We establish the submodularity of influence spread and solve the NP-hard optimal seed selection problem by using an efficient heuristic algorithm (TCD-IM) with provable near-optimal performance guarantees. To the best of our knowledge, this should be the first model for a metro-city scale from the influence perspective. The TCD-IM model is able to identify the dynamic traffic bottlenecks.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
EditorsChaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1717-1722
Number of pages6
ISBN (Electronic)9781728108582
DOIs
StatePublished - Dec 2019
Event2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States
Duration: Dec 9 2019Dec 12 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

Conference

Conference2019 IEEE International Conference on Big Data, Big Data 2019
CountryUnited States
CityLos Angeles
Period12/9/1912/12/19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

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  • Cite this

    Zhao, B., Xu, C., Liu, S., Zhao, J., & Li, L. (2019). A Congestion Diffusion Model with Influence Maximization for Traffic Bottlenecks Identification in Metrocity Scales. In C. Baru, J. Huan, L. Khan, X. T. Hu, R. Ak, Y. Tian, R. Barga, C. Zaniolo, K. Lee, & Y. F. Ye (Eds.), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 1717-1722). [9006472] (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData47090.2019.9006472