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A clustering based traffic flow prediction method with dynamic spatiotemporal correlation analysis

Author

Listed:
  • Unsok Ryu

    (Harbin Institute of Technology
    Kim Il Sung University)

  • Jian Wang

    (Harbin Institute of Technology)

  • Unjin Pak

    (Kim Chaek University of Technology)

  • Sonil Kwak

    (Kim Il Sung University)

  • Kwangchol Ri

    (Kim Il Sung University)

  • Junhyok Jang

    (Kim Il Sung University)

  • Kyongjin Sok

    (University of Sciences)

Abstract

There are significant spatiotemporal correlations among the traffic flows of neighboring road sections in the road network. Correctly identifying such correlations makes an essential contribution for improving the accuracy of traffic flow prediction. Many efforts have been made by several researchers to solve this issue, but they assume that the spatiotemporal correlations among traffic flows are stationary in both time and space, i.e., the degrees to which traffic flows affect each other are fixed. In this study, we propose a clustering based traffic flow prediction method that considers the dynamic nature of spatiotemporal correlations. In order to express the short-term dependence between the target road section and neighboring ones, the spatiotemporal correlation matrices are introduced. The historical traffic data are divided into several clusters according to the similarity between spatiotemporal correlation matrices. The spatiotemporal correlation analysis and the predictor selection based on the mutual information are performed in each cluster, and the multiple prediction models are trained separately. A prediction model corresponding to the cluster to which the current traffic pattern belongs is selected to output the prediction result. Experimental results on real traffic data show that the proposed method achieves good prediction accuracy by distinguishing the heterogeneity of spatiotemporal correlations among the traffic flows.

Suggested Citation

  • Unsok Ryu & Jian Wang & Unjin Pak & Sonil Kwak & Kwangchol Ri & Junhyok Jang & Kyongjin Sok, 2022. "A clustering based traffic flow prediction method with dynamic spatiotemporal correlation analysis," Transportation, Springer, vol. 49(3), pages 951-988, June.
    • Handle: RePEc:kap:transp:v:49:y:2022:i:3:d:10.1007_s11116-021-10200-9
    DOI: 10.1007/s11116-021-10200-9
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    References listed on IDEAS

    as
    1. Tang, Jinjun & Chen, Xinqiang & Hu, Zheng & Zong, Fang & Han, Chunyang & Li, Leixiao, 2019. "Traffic flow prediction based on combination of support vector machine and data denoising schemes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    2. Lawrence W. Lan & Feng-Yu Lin & April Y. Kuo, 2010. "Three Novel Methods to Predict Traffic Time Series in Reconstructed State Spaces," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 1(1), pages 16-35, January.
    3. Tao Cheng & James Haworth & Jiaqiu Wang, 2012. "Spatio-temporal autocorrelation of road network data," Journal of Geographical Systems, Springer, vol. 14(4), pages 389-413, October.
    4. Hilmi Berk Celikoglu & Mehmet Ali Silgu, 2016. "Extension of Traffic Flow Pattern Dynamic Classification by a Macroscopic Model Using Multivariate Clustering," Transportation Science, INFORMS, vol. 50(3), pages 966-981, August.
    5. Dong, Chunjiao & Shao, Chunfu & Clarke, David B. & Nambisan, Shashi S., 2018. "An innovative approach for traffic crash estimation and prediction on accommodating unobserved heterogeneities," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 407-428.
    6. Mahmood Akbari & Peter Overloop & Abbas Afshar, 2011. "Clustered K Nearest Neighbor Algorithm for Daily Inflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(5), pages 1341-1357, March.
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