计算机与现代化 ›› 2023, Vol. 0 ›› Issue (01): 58-62.

• 人工智能 • 上一篇    下一篇

基于协调影响流量的交叉口群主要流线识别

  

  1. (重庆交通大学交通运输学院,重庆 400074)
  • 出版日期:2023-03-02 发布日期:2023-03-02
  • 作者简介:张建旭(1979—),男,河南长葛人,副教授,博士,研究方向:综合交通系统规划,城市交通设计,交通仿真,E-mail: zhjx79@cqjtu.edu.cn; 通信作者:吴成峰(1996—),男,重庆北碚人,硕士研究生,研究方向:交通运输规划与管理,E-mail:2639985055@qq.com。
  • 基金资助:
    国家自然科学基金青年科学基金资助项目(61703064)

Identification of Main Streamline of Intersection Group Based on Coordinated Influence Flow

  1. (College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China)
  • Online:2023-03-02 Published:2023-03-02

摘要: 为了确定交叉口群的主要流线,以更好地进行交叉口群协调控制,建立基于协调影响流量交叉口群主要流线识别算法。首先分析路径协调影响流量成分,通过对交叉口群范围内的浮动车轨迹路径进行统计分析,确定备选主要流线并计算流线协调影响浮动车数量;然后利用交叉口流量和转向比例估计备选主要流线的协调影响流量;最后根据备选主要流线的统计协调影响浮动车数量和估计协调影响流量来计算流线权重指标,通过权重指标确定交叉口群的主要流线。以西安雁塔区划分的一个交叉口群为例,对交叉口群主要流线进行识别,验证本文算法的效果。实验结果表明,本文算法能利用浮动车数据和流量数据对交叉口群主要流线进行实时识别,为交叉口群信号协调控制提供支撑。

关键词: 交叉口群, 主要流线, 协调影响流量, 浮动车轨迹数据

Abstract: In order to determine the main streamline of intersection group and better coordinate the control of intersection group, an algorithm for identifying the main streamline of intersection group based on coordination influence flow is established. Firstly, the flow components affected by route coordination are analyzed. Through the statistical analysis of the trajectory of floating vehicles within the intersection group, the alternative main streamline is determined and the number of floating vehicles affected by streamline coordination is calculated;Then, the coordinated impact flow of the alternative main streamline is estimated by using the intersection flow and steering ratio; Finally, according to of the number of floating vehicles affected by the statistics of coordination of the alternative main streamline and the estimated coordination impact flow,we calculate the streamline weight index, and determine the main streamline of the intersection group through the weight index. Taking an intersection group divided by Yanta District of Xi’an as an example, the main streamline of the intersection group is identified for verifying the effect of the algorithm. The experimental results show that the algorithm can use the floating vehicle data and flow data to identify the main streamline of the intersection group in real time, and provide support for the signal coordination control of the intersection group.

Key words: intersection group, main streamline, coordinated impact flow, floating vehicle trajectory data