智能网联车和人驾车辆混合交通流排队长度估计模型

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中文摘要：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。

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

外文摘要：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.

外文关键词：

mixed traffic flowintelligent connected vehicleBayesian theoremtrajectory dataqueue length estimation

作者：

曹宁博、陈家辉、赵利英

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作者单位：

长安大学运输工程学院,陕西西安 710061

西北工业大学自动化学院,陕西西安 710129

西安理工大学经济与管理学院,陕西西安 710048

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

基金：

陕西省自然科学基础研究计划(青年项目)资助项目陕西省自然科学基础研究计划(面上项目)资助项目陕西省社会科学基金资助项目陕西省社会科学基金资助项目陕西省自然科学基金资助项目

项目编号：

2023-JC-QN-05312024JC-YBMS-3762022R0282021R0252022JM-426

出版年：

2024

DOI：

10.3785/j.issn.1008-973X.2024.09.018

浙江大学学报(工学版)

浙江大学

浙江大学学报(工学版)

CSTPCD北大核心

影响因子：0.625

ISSN：1008-973X

年,卷(期)：2024.58(9)