A data-driven congestion diffusion model for characterizing traffic in metrocity scales

Baoxin Zhao, Chengzhong Xu, Siyuan Liu

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

3 Scopus citations

Abstract

Traffic congestion is a spatiooral state of speeds beyond the capacity of road design and congestion may propagate through road networks. Characterizing the diffusion process is of great importance both in congestion relief and traffic condition prediction. Traffic congestion diffusion (TCD) in road networks can be observed, but literature lacks accurate models for characterizing the process. In this paper, we define a concept of Traffic Flow Influence (TFI) as a base for congestion diffusion. A TCD model is designed to characterize not only the traffic flow evolving process in time domain but also the propagation process of TFI through road networks in space domain. The model is for traffic networks in a city, which is divided into grids and each grid is modeled by traffic status of congested or smooth. Different from other diffusion models, the grid status depends on not only its current condition, but also the relative traffic flow from and to its neighbors. We use a gradient descent approach to quantify the traffic flow and TFI intensity of road networks. To the best of our knowledge, this should be the first model for a metro-city scale. The TCD model with TFI is able to predict grid status with an accuracy as high as 89%. Experimental results based on real-world taxi trajectory data in a metro-city show that the TCD approach performs best in comparison with its competitors.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
EditorsJian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1243-1252
Number of pages10
ISBN (Electronic)9781538627143
DOIs
StatePublished - Jul 1 2017
Event5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States
Duration: Dec 11 2017Dec 14 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
Volume2018-January

Other

Other5th IEEE International Conference on Big Data, Big Data 2017
CountryUnited States
CityBoston
Period12/11/1712/14/17

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Information Systems and Management
  • Control and Optimization

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  • Cite this

    Zhao, B., Xu, C., & Liu, S. (2017). A data-driven congestion diffusion model for characterizing traffic in metrocity scales. In J-Y. Nie, Z. Obradovic, T. Suzumura, R. Ghosh, R. Nambiar, C. Wang, H. Zang, R. Baeza-Yates, R. Baeza-Yates, X. Hu, J. Kepner, A. Cuzzocrea, J. Tang, & M. Toyoda (Eds.), Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017 (pp. 1243-1252). (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2017.8258050