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Robust estimation of traffic density with missing data using an adaptive-R extended Kalman filter

Author

Listed:
  • Bakibillah, A.S.M.
  • Tan, Yong Hwa
  • Loo, Junn Yong
  • Tan, Chee Pin
  • Kamal, M.A.S.
  • Pu, Ziyuan

Abstract

Traffic density is a crucial indicator of traffic congestion, but measuring it directly is often infeasible and hence, it is usually estimated based on other measurements. However, a challenge in measuring traffic parameters is the high probability of sensor failure, which results in missing measurement or missing data. To overcome this difficulty, in this paper, we propose a novel adaptive-R extended Kalman filter (AREKF) combined with a model-based data imputation technique to estimate traffic density. We show analytically that the AREKF is able to accurately estimate the density even when the noise covariance matrices are not accurately known. Microscopic traffic simulations demonstrated the efficacy of the AREKF, where the estimated density is fed into a real-time ramp metering control algorithm to control vehicle flow on a merging road, which is highly susceptible to traffic congestion. The results show that the proposed AREKF with data imputation is able to accurately estimate the traffic density even when data is missing, and the ramp-metering controller significantly improves the traffic flow and thus, alleviates congestion.

Suggested Citation

  • Bakibillah, A.S.M. & Tan, Yong Hwa & Loo, Junn Yong & Tan, Chee Pin & Kamal, M.A.S. & Pu, Ziyuan, 2022. "Robust estimation of traffic density with missing data using an adaptive-R extended Kalman filter," Applied Mathematics and Computation, Elsevier, vol. 421(C).
    • Handle: RePEc:eee:apmaco:v:421:y:2022:i:c:s0096300322000017
    DOI: 10.1016/j.amc.2022.126915
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    References listed on IDEAS

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    1. Coifman, Benjamin, 2003. "Estimating density and lane inflow on a freeway segment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(8), pages 689-701, October.
    2. Sun, Fengchun & Hu, Xiaosong & Zou, Yuan & Li, Siguang, 2011. "Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles," Energy, Elsevier, vol. 36(5), pages 3531-3540.
    3. 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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