智能网联车和人驾车辆混合交通流排队长度估计模型

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

浙江大学学报(工学版)

2024, Vol. 58

Issue (9): 1935-1944 DOI: 10.3785/j.issn.1008-973X.2024.09.018

交通工程

智能网联车和人驾车辆混合交通流排队长度估计模型

曹宁博1(

),陈家辉2,赵利英3,*(

)

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

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

Ningbo CAO1(

),Jiahui CHEN2,Liying ZHAO3,*(

)

1. College of Transportation Engineering, Chang’an University, Xi’an 710061, China
2. School of Automation, Northwestern Polytechnical University, Xi’an 710129, China
3. School of Economics and Management, Xi’an University of Technology, Xi’an 710048, China

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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.

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

Abstract:

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.

Key words: mixed traffic flow intelligent connected vehicle Bayesian theorem trajectory data queue length estimation

收稿日期: 2023-07-29 出版日期: 2024-08-30

CLC:

U 491

基金资助: 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).

通讯作者: 赵利英 E-mail: caonb@chd.edu.cn;lyzhao@xaut.edu.cn

作者简介: 曹宁博（1987—），男，讲师，从事自动驾驶汽车和行人安全研究. orcid.org/0000-0002-6630-0466. E-mail：caonb@chd.edu.cn

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引用本文:

曹宁博,陈家辉,赵利英. 智能网联车和人驾车辆混合交通流排队长度估计模型[J]. 浙江大学学报(工学版), 2024, 58(9): 1935-1944.

Ningbo CAO,Jiahui CHEN,Liying ZHAO. Queue length estimation model for mixed traffic flow of intelligent connected vehicles and human-driven vehicles. Journal of ZheJiang University (Engineering Science), 2024, 58(9): 1935-1944.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2024.09.018 或 https://www.zjujournals.com/eng/CN/Y2024/V58/I9/1935

图 1 车辆排队过程观察示意图

图 2 排队长度估计模型构建和验证流程图

图 3 排队状态简化示意图

表 1 队列误差概率

图 4 交叉口数据选取示意图

图 5 排队长度估计流程

表 2 排队长度调查数据

表 3 渗透率估计结果

图 6 基于本研究模型的非高峰和高峰时段MAPE

表 4 基于本研究模型的估计排队长度

图 7 基于本研究模型的非高峰和高峰时段MAPE($p = 20\text{%} $)

表 5 不同渗透率下基于本研究模型的非高峰时段估计排队长度

表 6 不同渗透率下基于本研究模型的高峰时段估计排队长度

图 8 基于贝叶斯定理的非高峰和高峰时段MAPE($p = 20\text{%} $)

表 7 不同渗透率下基于贝叶斯定理的非高峰时段估计排队长度

表 8 不同渗透率下基于贝叶斯定理的高峰时段估计排队长度

图 9 不同变量下本研究模型和贝叶斯模型的MAPE

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