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

被引:7
作者
Bakibillah, A. S. M. [1 ]
Tan, Yong Hwa [1 ]
Loo, Junn Yong [1 ]
Tan, Chee Pin [1 ]
Kamal, M. A. S. [2 ]
Pu, Ziyuan [1 ]
机构
[1] Monash Univ, Sch Engn & Adv Engn Platform, Bandar Sunway 47500, Selangor, Malaysia
[2] Gunma Univ, Grad Sch Sci & Technol, Kiryu, Gumma 3768515, Japan
关键词
AREKF; Data imputation; Ramp metering; Traffic congestion; Traffic density estimation; STATE ESTIMATION; REAL-TIME;
D O I
10.1016/j.amc.2022.126915
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
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 modelbased 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. (C) 2022 Elsevier Inc. All rights reserved.
引用
收藏
页数:13
相关论文
共 37 条
  • [21] Lunni D, 2018, 2018 IEEE INTERNATIONAL CONFERENCE ON SOFT ROBOTICS (ROBOSOFT), P541, DOI 10.1109/ROBOSOFT.2018.8405382
  • [22] Maybeck P.S, 1982, STOCHASTIC MODELS ES, V2, P68
  • [24] APPROACHES TO ADAPTIVE FILTERING
    MEHRA, RK
    [J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1972, AC17 (05) : 693 - &
  • [25] Adaptive Kalman filtering for INS GPS
    Mohamed, AH
    Schwarz, KP
    [J]. JOURNAL OF GEODESY, 1999, 73 (04) : 193 - 203
  • [26] Muñoz L, 2003, P AMER CONTR CONF, P3750
  • [27] Traffic-responsive linked ramp-metering control
    Papamichail, Ioannis
    Papageorgiou, Markos
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2008, 9 (01) : 111 - 121
  • [28] Estimation of Freeway Traffic Density with Loop Detector and Probe Vehicle Data
    Qiu, Tony Z.
    Lu, Xiao-Yun
    Chow, Andy H. F.
    Shladover, Steven E.
    [J]. TRANSPORTATION RESEARCH RECORD, 2010, (2178) : 21 - 29
  • [29] Stochastic stability of the discrete-time extended Kalman filter
    Reif, K
    Günther, S
    Yaz, E
    Unbehauen, R
    [J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1999, 44 (04) : 714 - 728
  • [30] Simon D, 2006, NONLINEAR APPROACHES, DOI DOI 10.1002/0470045345