基于卡尔曼滤波的交叉口排队长度实时估计模型

交通运输系统工程与信息 ›› 2021, Vol. 21 ›› Issue (2): 44-50.

• 智能交通系统与信息技术 • 上一篇下一篇

基于卡尔曼滤波的交叉口排队长度实时估计模型

蒋阳升^{a, b, c}，高宽^{a, b, c}，刘梦^{a, b, c}，王思琛^{a, b, c}，姚志洪^{*a, b, c}

西南交通大学，a. 交通运输与物流学院；b. 综合交通大数据应用技术国家工程实验室； c. 综合交通运输智能化国家地方联合工程实验室，成都 610031

收稿日期:2020-10-24 修回日期:2021-01-03 出版日期:2021-04-25 发布日期:2021-04-25
作者简介:蒋阳升(1976- )，男，湖南衡阳人，教授，博士。
基金资助:
国家自然科学基金/National Natural Science Foundation of China(52002339)；四川省科技计划项目/ Sichuan Science and Technology Program(2021YJ0535)；综合交通大数据国工室交大数科创新中心项目/ Innovation Center Project of Chengdu Jiao Da Big Data Technology Co. Ltd.(JDSKCXZX202003)。

Real-time Intersection Queue Length Estimation Based on Kalman Filtering

JIANG Yang-sheng^{a, b, c}, GAO Kuan^{a, b, c}, LIU Meng^{a, b, c}, WANG Si-chen^{a, b, c}, YAO Zhi-hong^{*a, b, c}

a. School of Transportation and Logistics; b. National Engineering Laboratory of Integrated Transportation Big Data Application Technology; c. National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu 610031, China

Received:2020-10-24 Revised:2021-01-03 Online:2021-04-25 Published:2021-04-25

摘要/Abstract

摘要：

为解决现有排队长度估计方法不能对排队长度进行实时秒级估计的问题，本文采用车联网实时数据，构建基于卡尔曼滤波的实时排队长度估计模型。首先，以当前时刻加入和离开排队队列的车辆数为输入变量构建状态转移方程，以当前排队网联车的数量和渗透率构建观测方程；其次，采用回归模型估计状态转移方程和观测方程的噪声协方差矩阵；然后，提出基于卡尔曼滤波方法估计排队长度的流程算法和模型性能评价指标；最后，基于实际数据构建仿真环境验证模型的有效性。结果表明：当网联车渗透率为30％时，平均绝对误差(MAE)，平均绝对百分比误差 (MAPE)和均方根误差(RMSE)的平均值分别为1.6辆，20.9％和2.5辆；当渗透率大于20%时，与基准方法相比，本文模型估计效果更优。

关键词: 智能交通, 队列长度, 卡尔曼滤波, 网联车, 渗透率, Vissim仿真

Abstract:

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.

Key words: intelligent transportation, queue length, Kalman filtering, connected vehicle, penetration rate, Vissim simulation

中图分类号:

U491

蒋阳升，高宽，刘梦，王思琛，姚志洪. 基于卡尔曼滤波的交叉口排队长度实时估计模型[J]. 交通运输系统工程与信息, 2021, 21(2): 44-50.

JIANG Yang-sheng, GAO Kuan, LIU Meng, WANG Si-chen, YAO Zhi-hong. Real-time Intersection Queue Length Estimation Based on Kalman Filtering[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(2): 44-50.

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链接本文: http://www.tseit.org.cn/CN/abstract/abstract30208.shtml

http://www.tseit.org.cn/CN/Y2021/V21/I2/44

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基于卡尔曼滤波的交叉口排队长度实时估计模型

Real-time Intersection Queue Length Estimation Based on Kalman Filtering

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