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Real-time freeway traffic state estimation based on extended Kalman filter: Adaptive capabilities and real data testing

Author

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  • Wang, Yibing
  • Papageorgiou, Markos
  • Messmer, Albert

Abstract

This paper reports on real data testing of a real-time freeway traffic state estimator, with a particular focus on its adaptive capabilities. The pursued general approach to the real-time adaptive estimation of complete traffic state in freeway stretches or networks is based on stochastic macroscopic traffic flow modeling and extended Kalman filtering. One major innovative feature of the traffic state estimator is the online joint estimation of important model parameters (free speed, critical density, and capacity) and traffic flow variables (flows, mean speeds, and densities), which leads to three significant advantages of the estimator: (1) avoidance of prior model calibration; (2) automatic adaptation to changing external conditions (e.g. weather and lighting conditions, traffic composition, control measures); (3) enabling of incident alarms. These three advantages are demonstrated via suitable real data testing. The achieved testing results are satisfactory and promising for subsequent applications.

Suggested Citation

  • Wang, Yibing & Papageorgiou, Markos & Messmer, Albert, 2008. "Real-time freeway traffic state estimation based on extended Kalman filter: Adaptive capabilities and real data testing," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(10), pages 1340-1358, December.
    • Handle: RePEc:eee:transa:v:42:y:2008:i:10:p:1340-1358
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    References listed on IDEAS

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    1. Yibing Wang & Markos Papageorgiou & Albert Messmer, 2007. "Real-Time Freeway Traffic State Estimation Based on Extended Kalman Filter: A Case Study," Transportation Science, INFORMS, vol. 41(2), pages 167-181, May.
    2. Wang, Yibing & Papageorgiou, Markos, 2005. "Real-time freeway traffic state estimation based on extended Kalman filter: a general approach," Transportation Research Part B: Methodological, Elsevier, vol. 39(2), pages 141-167, February.
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    Cited by:

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    2. Yildirimoglu, Mehmet & Geroliminis, Nikolas, 2013. "Experienced travel time prediction for congested freeways," Transportation Research Part B: Methodological, Elsevier, vol. 53(C), pages 45-63.
    3. Ernesto Cipriani & Lorenzo Giannantoni & Livia Mannini, 2023. "Integrated Variable Speed Limits and User Information Strategy," Sustainability, MDPI, vol. 15(14), pages 1-19, July.
    4. Toan, Trinh Dinh & Lam, Soi Hoi & Wong, Yiik Diew & Meng, Meng, 2022. "Development and validation of a driving simulator for traffic control using field data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    5. Ximan Ling & Zhiren Huang & Chengcheng Wang & Fan Zhang & Pu Wang, 2018. "Predicting subway passenger flows under different traffic conditions," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-23, August.
    6. Wei Huang & Yang Hu & Xuanyu Zhang, 2022. "Enhancing Model-Based Anticipatory Traffic Signal Control with Metamodeling and Adaptive Optimization," Mathematics, MDPI, vol. 10(15), pages 1-18, July.

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