重庆理工大学学报(自然科学) ›› 2023, Vol. 37 ›› Issue (4): 294-303.

• 能源动力环境 • 上一篇    下一篇

尖点突变理论在近邻交织区交通状态判别中的应用

马庆禄,吴跃川,袁新新   

  1. (1.重庆交通大学 交通运输学院,重庆 400074; 2.宁夏交投高速公路管理有限公司,银川 750000)
  • 出版日期:2023-05-06 发布日期:2023-05-06
  • 作者简介:马庆禄,男,博士,教授,主要从事智能交通与安全、大数据与智慧城市、智慧公路感知与安全研究,Email: mql360@qq.com。

The cusp-mutation theory in determining traffic state in the adjacent weaving segments

  • Online:2023-05-06 Published:2023-05-06

摘要: 为了提高近邻交织区的通行效率,探究近邻交织区交通流的运行状态及其变化规 律,提出了一种结合尖点突变理论与高斯混合模型的自适应交通状态判别方法。该方法以快速 路近邻交织区为研究对象,通过研究不同间距条件下平均速度和时间占有率的高斯混合分布, 利用尖点突变理论剖析近邻交织区交通状态变化趋势,为交织区交通管控提供必要的理论与技 术支撑。实验中选择重庆市海峡路与四公里互通处近邻交织区为例,以 VISSIM交通仿真软件 对实际调查数据进行仿真效果验证。实验结果表明:近邻交织区交通流在不同状态间转变时, 存在一种临界状态,随近邻交织区不同间距变化,当近邻交织区间距小于 150m时临界拥挤流 速度变化显著,大于 150m时趋于稳定;当近邻交织区间距小于 200m时临界拥挤流占有率变 化显著,大于 200m时趋于稳定。研究成果对于近邻交织区的交通管控以及交通流研究都奠定 了很强的理论基础。

关键词: 交通状态, 尖点突变理论, 高斯混合分布, 近邻交织区, 临界状态

Abstract: Traffic variation tendency has a significant impact on the efficiency and safety of urban traffic operations. To explore and improve its traffic efficiency in adjacent weaving segments (AWS) near a ramp of an urban rapid road, this paper proposes an adaptive traffic state discrimination method combining the cusp-mutation theory and Gaussian-mixture model. With an aim of reducing traffic congestion, the optimal combination matching of the cusp-mutation theory and Gaussian-mixture model is discussed to establish a more practical determining model for the actual traffic operation status of the AWS in this paper. This method focuses on the AWS of urban rapid roads, takes the AWS as the research object, and studies the Gaussian-mixture distribution of the average speed and time occupancy under different spacing conditions. At the same time, it takes the advantage of the cusp-mutation theory to analyze the trend of traffic variation in the AWS and uses the Gaussian-mixture model to identify and classify the congestion distribution pattern of the traffic operation status in the AWS. Determining traffic is the theoretical basis, which provides necessary theoretical and technical support for traffic control in the AWS by establishing the traffic flow model and the state discrimination model of AWS. Besides the integration of the cusp-mutation theory and the Gaussian-mixture model, a method for estimation of traffic state parameters is proposed to solve the problem that the congestion state discrimination criteria of the AWS are affected by the weaving interval distance. Through investigating the actual road structure and traffic volume, the real-time traffic operation status and its changes under the condition of an increasing traffic flow are simulated and analyzed using the VISSIM traffic simulation software for the selected AWS at the interchange of Haixia Road and Sigongli Road in Chongqing. It is verified that the cusp-mutation theory is applicable to discriminating operating status of the AWS, and the critical state of traffic is a key to evaluating level of traffic congestion, which changes along with different distances between the two weaving segments. The experimental results show that the critical congestion velocity varies significantly when the spacing between two AWS is less than 150 m, and stabilizes when it is greater than 150 m. The critical congestion rate varies significantly when the spacing between two AWS is less than 200 m, and stabilizes when the spacing is greater than 200 m. This study highlights the importance of an adaptive traffic state discrimination method that combines the cusp-mutation theory and the Gaussian-mixture model to study the traffic flow in the AWS. The effectiveness of the proposed method in analyzing traffic state change is also demonstrated experimentally, which helps to study traffic control and traffic flow in the AWS, provides a strong theoretical basis for analyzing the trend of traffic state change in the AWS, and offers valuable insights into the effect of different spacing conditions on the critical congestion speed and rate. The study can help traffic managers to formulate better traffic control strategies and improve the traffic efficiency of the AWS.

中图分类号: 

  • U491.1