摘要
为了公路交通管理与出行信息服务的实际需要,提出了车路协同环境下基于多源数据实时感知与估计的微观路段交通状态与行程车速的方法。其基本思想是利用自适应神经网络模糊推理系统(Adaptive Neural-Fuzzy Inference System,ANFIS)算法,对智能网联车辆与路侧端固定检测器采集的数据进行融合处理,实时感知与估计微观路段的交通状态。对北京市典型高速公路进行了仿真建模分析。结果表明:在不同智能网联车辆渗透率条件下基于多源数据的行程车速估计精度均优于单一来源数据检测结果;基于多源数据的微观路段交通状态感知方法能克服固定检测器数据空间分布不均以及低渗透率智能网联车辆数据代表性不足的缺陷,为公路交通管理与服务提供依据。
For the practical needs of road traffic management and travel information services,a method for real-time perception and estimation of traffic status and travel speed of micro road sections in a vehicle-infrastructure cooperation environment based on multi-source data is proposed.The fundamental idea is using an adaptive neural-fuzzy inference system to fuse and process the data collected by the intelligent network connected vehicle and the roadside fixed detector for real-time perception and estimation of the traffic status of micro road sections.The simulation modeling analysis of a typical expressway in Beijing is conducted.The result shows that(1)the estimation accuracies of trip speed based on multi-source data under different penetration rates of the intelligent network connected vehicle are better than that based on single-source data;(2)The micro road section traffic state perception method based on multi-source data can overcome the shortcomings of uneven spatial distribution of fixed detector data and insufficient representation of low penetration of intelligent network connected vehicle data,which provides a basis for highway traffic management and services.
作者
李茜瑶
李宏海
黄烨然
桂勇
赵阳
LI Xi-yao;LI Hong-hai;HUANG Ye-ran;GUI Yong;ZHAO Yang(Research Institute of Highway,Ministry of Transport,Beijing 100088,China;Beihang University,Beijing 100191,China;Beijing Capital Highway Development Group Co.,Ltd.,Beijing 100101,China)
出处
《公路交通科技》
CAS
CSCD
北大核心
2022年第S01期77-83,共7页
Journal of Highway and Transportation Research and Development
基金
公路交通基础设施数字化体系及主被动实现技术研究项目(2019-C513)
关键词
交通工程
数据融合
交通感知
ANFIS
车路协同
traffic engineering
data fusion
traffic perception
Adaptive Neural-Fuzzy Inference System(ANFIS)
vehicle-infrastructure cooperation
