Real-time Intersection Queue Length Estimation Based on Kalman Filtering

被引:0
|
作者
Jiang Y.-S. [1 ,2 ,3 ]
Gao K. [1 ,2 ,3 ]
Liu M. [1 ,2 ,3 ]
Wang S.-C. [1 ,2 ,3 ]
Yao Z.-H. [1 ,2 ,3 ]
机构
[1] School of Transportation and Logistics, Southwest Jiaotong University, Chengdu
[2] National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu
[3] National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu
基金
中国国家自然科学基金;
关键词
Connected vehicle; Intelligent transportation; Kalman filtering; Penetration rate; Queue length; Vissim simulation;
D O I
10.16097/j.cnki.1009-6744.2021.02.007
中图分类号
学科分类号
摘要
Considering the existing queue length estimation methods cannot dynamically reflect the queue length at intersections, this paper proposes a Kalman filtering-based queue length estimation model using real-time connected vehicles data. The stepwise state transition equation is developed based on the number of vehicles joining and leaving the queue at the current moment. The observation equation is formulated through the current number of queuing connected vehicles and the penetration rate. Then, a regression model is used to estimate noise covariance matrixes of the state transition equation and the observation equation. The process of the queue estimation is established and the evaluation index is proposed to measure the effectiveness of the proposed model. A simulation evaluation is then performed based on actual data. The results show that when the penetration rate of connected vehicle is 30%, the average values of mean absolute errors (MAE) is 1.6 vehicles, the mean absolute percentage errors (MAPE) is 20.9%, and root mean square errors (RMSE) is 2.5 vehicles. When the penetration rate of connected vehicle is over 20%, the proposed method shows better performances than the benchmark method. Copyright © 2021 by Science Press.
引用
收藏
页码:44 / 50
页数:6
相关论文
共 15 条
  • [1] WEBSTER F V., Traffic signal settings, (1969)
  • [2] GARTNER N H, LITTLE J D C, GABBAY H., Optimization of traffic signal settings by mixed-integer linear programming, Part I: The network coordination problem, Transportation Science, 9, 4, pp. 321-343, (1975)
  • [3] YAO Z H, JIANG Y S, WANG Y., Adaptive signal timing optimization model in vehicle-road collaborative environment, Industrial Engineering Journal, 21, 4, (2018)
  • [4] YAO J, DAI Y X, NI Y L, Et al., Signalized intersection length estimation based on vehicle trajectory, Highway Traffic Science and Technology, 37, 5, pp. 123-130, (2020)
  • [5] DAI L L, JIANG G Y, PEI Y L., Queue length prediction at saturated signalized intersections, Journal of Jilin University (Engineering and Technology Edition), 6, pp. 1287-1290, (2008)
  • [6] ZHUANG L J, HE Z C, YE W J, Et al., Research on queue length detection method based on floating car data, Transportation System Engineering and Information, 13, 3, pp. 78-84, (2013)
  • [7] WANG Y, XU J M, LIN P Q., Real-time queue length estimation at signalized intersections based on GPS data, Transportation Systems Engineering and Information, 16, 6, pp. 67-73, (2016)
  • [8] CHENG Y, QIN X, JIN J, Et al., Cycle-by-cycle queue length estimation for signalized intersections using sampled trajectory data, Transportation Research Record, 2257, 1, pp. 87-94, (2011)
  • [9] ZHAO Y, ZHENG J, WONG W, Et al., Estimation of queue lengths, probe vehicle penetration rates, and traffic volumes at signalized intersections using probe vehicle trajectories, Transportation Research Record, 2673, 11, pp. 660-670, (2019)
  • [10] LI J Q, ZHOU K, SHLADOVER S E, Et al., Estimating queue length under connected vehicle technology: Using probe vehicle, loop detector, and fused data, Transportation Research Record, 2356, 1, pp. 17-22, (2013)