高速公路多源数据融合下的层次拥堵区间探测方法

Detecting Hierarchical Congestion Intervals Based on the Fusion of Multi-source Highway Data

  • 摘要: 随着我国经济的发展及城市化建设的推进,交通拥挤问题日益突出。作为城际之间生命线的高速公路在节假日期间的拥堵情况尤为严重,为了更有效地管理高速公路、及时疏导交通拥堵,需要对高速公路上的拥堵事件进行探测,实现对高速公路路段交通状态的监管。首先利用高速公路路径识别点的识别数据、收费系统的收费流水数据和“两客一危”重点车辆的全球定位系统(global positioning system,GPS)轨迹数据,构建了多源数据融合下的层次拥堵区间探测方法;然后采用模糊综合评价算法,对多层次路段的交通状态进行识别;最后,利用湖南省长株潭城市群区域2018年2月中两周的数据进行了实际探测和结果分析。

     

    Abstract: With the development of economy and urbanization in China, the issue of traffic congestion is increasingly prominent. Highways as the lifelines between cities are particularly vulnerable during the holidays. In order to manage highways more efficiently and regulate traffic more timely, it is necessary to detect congestion events on highways to realize the supervision of highway traffic. Using recognition data of highway path recognition system, transactions data of toll station, and GPS trajectory data of coach buses, touring buses and dangerous goods transport vehicles, this paper proposes a hierarchical congestion interval detection method which adopts fuzzy comprehensive evaluation algorithm to identify the traffic status of road sections. Taking the Changsha-Zhuzhou-Xiangtan urban agglomeration region of Hunan province as a case study, this paper uses the proposed method to detect and analyze traffic status of the experimental area in two weeks in February, 2018.

     

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