智能网联车和人驾车辆混合交通流排队长度估计模型  

Queue length estimation model for mixed traffic flow of intelligent connected vehicles and human-driven vehicles

作　　者：曹宁博 陈家辉 赵利英 CAO Ningbo;CHEN Jiahui;ZHAO Liying(College of Transportation Engineering,Chang’an University,Xi’an 710061,China;School of Automation,Northwestern Polytechnical University,Xi’an 710129,China;School of Economics and Management,Xi’an University of Technology,Xi’an 710048,China)

机构地区：[1]长安大学运输工程学院,陕西西安710061 [2]西北工业大学自动化学院,陕西西安710129 [3]西安理工大学经济与管理学院,陕西西安710048

出　　处：《浙江大学学报（工学版）》2024年第9期1935-1944,共10页Journal of Zhejiang University：Engineering Science

基　　金：陕西省自然科学基础研究计划(青年项目)资助项目(2023-JC-QN-0531);陕西省自然科学基础研究计划(面上项目)资助项目(2024JC-YBMS-376);陕西省社会科学基金资助项目(2022R028,2021R025);陕西省自然科学基金资助项目(2022JM-426).

摘　　要：为了解决智能网联车(ICVs)和人驾车辆(HDVs)混行交叉口的排队估计问题,提出基于概率统计和贝叶斯定理的排队长度估计模型.综合考虑队列中智能网联车位置、速度和渗透率等因素,分别构建可观测队列排队长度估计模型、不可观测队列排队长度估计模型和渗透率估计模型,通过迭代实现排队长度和渗透率的实时估计.利用随机种子模拟不同渗透率条件下智能网联车在队列中的分布特征,分析不同交通条件下模型的估计精度.与已有模型的对比表明,在智能网联车低渗透率(10%)条件下,在非高峰时段,本研究模型、已有模型的平均绝对百分比误差(MAPE)分别为29.35%、59.68%;在高峰时段,本研究模型、已有模型的MAPE分别为26.50%、34.66%.在智能网联车高渗透率条件下(90%),在非高峰时段,本研究模型、已有模型的MAPE分别为6.90%、17.85%;在高峰时段,本研究模型、已有模型的MAPE分别为1.45%、1.05%,误差接近.本研究所提出的排队估计模型在低渗透率和高渗透率条件下均具有更好的估计精度.A dynamic queue length estimation model based on probability statistics and Bayesian theorem was proposed,to solve the problem of queue length estimation at intersections with mixed traffic of intelligent connected vehicles(ICVs)and human-driven vehicles(HDVs).Firstly,taking into account factors such as the position,speed,and penetration rate of ICVs in the queue,models for estimating the queue lengths of observable and unobservable queues,as well as the penetration rate,were constructed.Real-time estimation of queue lengths and penetration rate was achieved through iteration.Then,the distribution characteristics of ICVs in the queue under different penetration rate conditions were simulated using random seeds.The estimation accuracy of the model under different traffic conditions was analyzed.Comparison analysis with existing models showed that,under low penetration rate conditions of ICVs(10%)during off-peak hours,the average absolute percentage error(MAPE)of the proposed model was 29.35%,while the existing model had an MAPE of 59.68%;during peak hours,the MAPE of this model was 26.50%,compared to 34.66%for the existing model.Under high penetration rate conditions of ICVs(90%)during off-peak hours,the MAPE of this model was 6.90%,while the existing model had an MAPE of 17.85%;during peak hours,the MAPE of this model was 1.45%,compared to 1.05%for the existing model,with similar errors.The proposed queue estimation model for mixed traffic of ICVs and human-driven vehicles has better estimation accuracy under both low and high penetration rate conditions.

关 键 词：混合交通流 智能网联车 贝叶斯定理 轨迹数据 排队长度估计 

分 类 号：U491[交通运输工程—交通运输规划与管理]