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Traffic State Estimation and Classification on Citywide Scale Using Speed Transition Matrices

Author

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  • Leo Tišljarić

    (Faculty of Transport and Traffic Sciences, University of Zagreb, HR-10000 Zagreb, Croatia)

  • Tonči Carić

    (Faculty of Transport and Traffic Sciences, University of Zagreb, HR-10000 Zagreb, Croatia)

  • Borna Abramović

    (Faculty of Transport and Traffic Sciences, University of Zagreb, HR-10000 Zagreb, Croatia)

  • Tomislav Fratrović

    (Faculty of Transport and Traffic Sciences, University of Zagreb, HR-10000 Zagreb, Croatia)

Abstract

The rising need for mobility, especially in large urban centers, consequently results in congestion, which leads to increased travel times and pollution. Advanced traffic management systems are being developed to take the advantage of increased mobility positive effects and minimize the negative ones. The first step dealing with congestion in urban areas is the detection of congested areas and the estimation of the congestion level. This paper presents a a method for a traffic state estimation on a citywide scale using the novel traffic data representation, named Speed Transition Matrix (STM). The proposed method uses traffic data to extract the STMs and to estimate the traffic state based on the Center Of Mass (COM) computation for every STM. The COM-based approach enables the simplification of the clustering process and provides increased interpretability of the resulting clusters. Using the proposed method, traffic data is analyzed, and the traffic state is estimated for the most relevant road segments in the City of Zagreb, which is the capital and the largest city in Croatia. The traffic state classification results are validated using the cross-validation method and the domain knowledge data with the resulting accuracy of 97% and 91%, respectively. The results indicate the possible application of the proposed method for the traffic state estimation on macro- and micro-locations in the city area. In the end, the application of STMs for traffic state estimation, traffic management, and anomaly detection is discussed.

Suggested Citation

  • Leo Tišljarić & Tonči Carić & Borna Abramović & Tomislav Fratrović, 2020. "Traffic State Estimation and Classification on Citywide Scale Using Speed Transition Matrices," Sustainability, MDPI, vol. 12(18), pages 1-16, September.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:18:p:7278-:d:409072
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    References listed on IDEAS

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    3. Wahle, J. & Annen, O. & Schuster, Ch. & Neubert, L. & Schreckenberg, M., 2001. "A dynamic route guidance system based on real traffic data," European Journal of Operational Research, Elsevier, vol. 131(2), pages 302-308, June.
    4. Tanzina Afrin & Nita Yodo, 2020. "A Survey of Road Traffic Congestion Measures towards a Sustainable and Resilient Transportation System," Sustainability, MDPI, vol. 12(11), pages 1-23, June.
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    Cited by:

    1. Bingsheng Huang & Fusheng Zhang, 2022. "Analysis of Traffic Oversaturation Based on Multi-Objective Data," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
    2. Wang, Chun & Zhang, Weihua & Wu, Cong & Hu, Heng & Ding, Heng & Zhu, Wenjia, 2022. "A traffic state recognition model based on feature map and deep learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).

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