吉林大学学报(工学版) ›› 2023, Vol. 53 ›› Issue (1): 124-131.doi: 10.13229/j.cnki.jdxbgxb20210513

• 交通运输工程·土木工程 • 上一篇    

基于密度峰值聚类的交通控制子区划分方法

魏路1(),高磊1,2,李晋宏2(),杨建1,2,田玉林1   

  1. 1.北方工业大学 城市道路交通智能控制技术北京市重点实验室，北京 100144
    2.北方工业大学 信息学院，北京 100144
  • 收稿日期:2021-06-07 出版日期:2023-01-01 发布日期:2023-07-23
  • 通讯作者: 李晋宏 E-mail:wltop001@sina.com;ljh@ncut.edu.cn
  • 作者简介:魏路（1985-），男，博士研究生. 研究方向：交通信号控制，数据挖掘. E-mail： wltop001@sina.com
  • 基金资助:
    国家重点研发计划项目(2018YFB1601003)

Traffic sub⁃area division method based on density peak clustering

Lu WEI1(),Lei GAO1,2,Jin-hong LI2(),Jian YANG1,2,Yu-lin TIAN1   

  1. 1.Beijing Key Laboratory of Urban Road Traffic Intelligent Control Technology，North China University of Technology，Beijing 100144，China
    2.School of Information Science and Technology，North China University of Technology，Beijing 100144，China
  • Received:2021-06-07 Online:2023-01-01 Published:2023-07-23
  • Contact: Jin-hong LI E-mail:wltop001@sina.com;ljh@ncut.edu.cn

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摘要：

为提升区域交通信号系统的控制效率，提出了一种基于车辆轨迹数据和密度峰值聚类的城市路网交通控制子区划分方法。首先，结合轨迹数据特性并综合考虑交叉口间距、车辆延误、车队离散度等因素的影响，定义并计算了交叉口的关联度指标。其次，根据关联度指标得到交叉口的距离矩阵，作为密度峰值聚类算法的输入；针对密度峰值聚类的超参数设置问题，引入数据场理论中势能熵的概念确定最优值；同时，借鉴肘部法则的思想确定聚类中心数量。最后，将改进的密度峰值聚类算法应用于交叉口子区划分中。以北京市中关村西区真实车辆轨迹数据的实验分析表明：本文方法可以仅基于车辆轨迹数据实现城市路网交通控制子区的高效、合理划分。

关键词: 交通信息工程及控制, 子区划分, 车辆轨迹, 交叉口关联度, 密度峰值聚类

Abstract:

To improve the efficiency of urban traffic signal control system， this paper proposes a sub-area division method based on vehicle trajectory data and density peak clustering. Firstly， the correlation index between adjacent intersections is calculated by combining the influence of distance between intersections， vehicle delays and platoon dispersion based on vehicle trajectory data. Secondly， the distance matrix is obtained according to the correlation indexes， which is used as the input of the density peak clustering algorithm. For the hyperparameter determination in density peak clustering， the concept of potential entropy in the data field theory is introduced to optimize. Simultaneously， the elbow rule is used to determine the number of clusters. Finally， the division of sub-areas is completed by using the improved clustering algorithm. The experiment on real-world vehicle trajectory data in Zhongguancun West District of Beijing shows that the proposed method could divide the road network into sub-area effectively and reasonably based on vehicle trajectory data only.

Key words: transportation information engineering and control, sub-area division, vehicle trajectory, intersection correlation degree, density peak clustering

中图分类号: 

  • U491.1

图1

采样时间差分布"

图2

轨迹段可视化"

图3

γ决策图示例"

图4

实验区域路网结构及交叉口位置"

图5

决策图"

图6

γ决策图"

图7

子区划分结果"

图8

Synchro子区划分结果"

图9

路网控制指标对比"

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