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Route Identification Method for On-Ramp Traffic at Adjacent Intersections of Expressway Entrance

机译:高速公路入口相邻交叉口斜坡交通的路径识别方法

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摘要

To determine the control strategy at intersections adjacent to the expressway on-ramp, a route identification method based on empirical mode decomposition (EMD) and dynamic time warping (DTW) is established. First, the de-noise function of EMD method is applied to eliminate disturbances and extract features and trends of traffic data. Then, DTW is used to measure the similarity of traffic volume time series between intersection approaches and expressway on-ramp. Next, a three-dimensional feature vector is built for every intersection approach traffic flow, including DTW distance, space distance between on-ramp and intersection approach, and intersection traffic volume. Fuzzy C-means clustering method is employed to cluster intersection approaches into classifications and identify critical routes carrying the most traffic to the on-ramp. The traffic data are collected by inductive loops at Xujiahui on-ramp of North and South Viaduct Expressway and surrounding intersections in Shanghai, China. The result shows that the proposed method can achieve route classification among intersections for different time periods in one day, and the clustering result is significantly influenced by three dimensions of traffic flow feature vector. As an illustrative example, micro-simulation models are built with different control strategies. The simulation shows that the coordinated control of critical routes identified by the proposed method has a better performance than coordinated control of arterial roads. Conclusions demonstrated that the proposed route identification method could provide a theoretical basis for the coordinated control of traffic signals among intersections and on-ramp.
机译:为了确定与高速公路上斜坡相邻的交叉点的控制策略,建立了一种基于经验模式分解(EMD)和动态时间翘曲(DTW)的路由识别方法。首先,应用EMD方法的遮光功能来消除干扰和提取特征和交通数据的趋势。然后,DTW用于测量交叉点接近和斜坡高速公路之间的业务体积时间序列的相似性。接下来,为每个交叉路口流量流构建三维特征向量,包括DTW距离,上斜坡和交叉点的空间距离以及交叉路口流量。模糊C-means聚类方法用于将交叉点接近分类,并确定携带最多流量的关键路线。交通数据是通过北部和南高桥高速公路和中国上海上海周边交叉口的徐家汇的电感环收集的流量数据。结果表明,该方法可以在一天内实现不同时间段的交叉点之间的路由分类,并且聚类结果受到交通流量特征向量的三维维度的显着影响。作为说明性示例,使用不同的控制策略构建了微型仿真模型。该模拟表明,由所提出的方法所识别的关键路线的协调控制具有比动脉道路的协调控制更好的性能。结论表明,所提出的路线识别方法可以为交叉口和斜坡之间的交通信号进行协调控制提供理论依据。

著录项

  • 来源
    《Journal of Advanced Transportation》 |2019年第5期|6960193.1-6960193.15|共15页
  • 作者单位

    Tongji Univ Minist Educ Key Lab Rd & Traff Engn Shanghai 201804 Peoples R China;

    China Highway Engn Consultants Corp Beijing 100089 Peoples R China;

    Tongji Univ Minist Educ Key Lab Rd & Traff Engn Shanghai 201804 Peoples R China;

    Tongji Univ Minist Educ Key Lab Rd & Traff Engn Shanghai 201804 Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
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