基于智能网联车的高速公路移动瓶颈实时检测方法  

A Method for Real-time Detecting Freeway Moving Bottlenecks Using Intelligent Connected Vehicles

作　　者：李凯 孙佳 陈非 唐颜东 曹鹏 LI Kai;SUN Jia;CHEN Fei;TANG Yandong;CAO Peng(Sichuan Intelligent Highway Technology Co.,Ltd.,Chengdu 610041,China;School of Transportation and Logistics,Southwest Jiaotong University,Chengdu 611756,China)

机构地区：[1]四川智慧高速科技有限公司,成都610041 [2]西南交通大学交通运输与物流学院,成都611756

出　　处：《交通信息与安全》2024年第5期24-32,共9页Journal of Transport Information and Safety

基　　金：国家自然科学基金项目(61903313、52172395)资助。

摘　　要：针对基于定点的检测方法无法有效监测移动瓶颈的形成及演变态势问题,研究了1种基于智能网联车的高速公路移动瓶颈实时检测方法。结合智能车轨迹数据特性,提出基于小波分析的轨迹降噪方法。基于轨迹形态与交通状态之间的关系,识别智能车轨迹中体现交通状态突变的关键点。由于同1个时刻路段上存在多个交通拥堵,为确定关键点所属交通波,提出基于交通波时空分布特性的关键点分类算法。基于关键点计算交通波波速并估算排队长度,以对识别的移动瓶颈进行评价。基于SUMO仿真平台,以沪嘉高速为对象,对各种智能车占比下移动瓶颈位置和传播速度的检测效果以及排队延误情况开展了试验。结果表明:当高速路上智能车渗透率小于10%,轨迹降噪所带来交通波波速估计精度平均提升20%;当渗透率超过3%,对移动瓶颈传播速度的估计误差在0.42 m/s以下;当渗透率达到7%,移动瓶颈位置估计偏差大多在10 m以内,不超过25 m。本文方法可以实时地检测高速路上随机突发的移动瓶颈并评价其严重程度。Aiming at the problem that the fixed-point detection method cannot effectively monitor the formation and evolution of the mobile bottleneck,a real-time detection method of the mobile bottleneck on the expressway based on intelligent networked vehicles is studied.A wavelet analysis-based method is proposed to reduce the errors of trajectories collected by intelligent connected vehicles(ICVs).And then the key points that represent the change of traffic states are identified based on the relationship between the vehicle trajectories and the traffic states.Considering that multiple traffic congestions may simultaneously occur on a road segment,an algorithm is proposed to classify the key points based on the space-time characteristics of traffic shockwaves.Finally,the traffic shockwave speed is calculated,and moving bottlenecks are identified and evaluated.Based on SUMO simulation platform,experiments are carried out on the detection effect of mobile bottleneck location,propagation speed and queuing delay under the proportion of various intelligent vehicles in Hujia freeway.The results show that when the penetration rate of ICVs is less than 10%,the accuracy of traffic wave speed estimation improves by an average of 20%after trajectory denoising.When the penetration rate exceeds 3%,the estimation error of the moving bottleneck propagation speed is below 0.42 m/s.When the penetration rate reaches 7%,the estimated position of the moving bottleneck has a deviation mostly within 10 m,with a maximum of 25 m.The proposed method can detect the presence of freeway bottlenecks which occur randomly and evaluate their severity in real-time.

关 键 词：智能交通 高速路交通状态 移动瓶颈检测 小波分析 交通波 智能网联车 

分 类 号：U491.2[交通运输工程—交通运输规划与管理]