利用时空密度聚类的高速公路交通事故黑点路段鉴别

研究高速公路交通事故黑点路段的时空分布规律和关联因素,一直是交通领域的关注重点。本文针对事故统计的交通事故黑点路段鉴别方法存在地理学中的可塑面积单元(MAUP)问题,提出一种基于时空密度聚类的高速公路交通事故黑点路段鉴别方法。该方法改进了传统的DBSCAN空间聚类算法,引入一种顾及时间周期性和事故严重程度的事故时空邻近计算方法,通过密度连接规则自适应鉴别各种时空尺度的交通事故黑点路段。以2012—2016年湖南省的高速公路交通事故为例进行试验,结果表明,本文方法可有效克服不同划分单元的可塑面积单元问题,自适应鉴别不同长度的黑点路段,同时可进一步挖掘黑点路段上交通事故时空聚集模式。

Identification of highway accident black spots based on spatio-temporal density clustering

Due to the characteristics of high driving speed and difficulties of controlling traffic flow, highway traffic accidents are often more serious than that of urban roads. Hence, it is a crucial issue in the traffic engineering field to identify the highway black spots and analyze their spatio-temporal association patterns. Traditional statistical methods may be confronted with the problem of modifiable areal unit problem(MAUP), which means the accuracy of identifying accident black spots directly depends on the basic unit size. Traffic accident is a representative spatio-temporal event, which often contains particular spatio-temporal patterns. The paper proposes a novel method to identify the highway black spots based on spatio-temporal density-based spatial clustering of applications with noise (DBSCAN) clustering. The proposed method considers the time periodicity and accident severity into calculating the spatio-temporal neighboring indicators and then finds various traffic accident black spots of multi-spatio-temporal scales based on the density-connecting rules of DBSCAN. The experimental results of 2012-2016 highway crash datasets in Hunan province illustrate the proposed method can adaptively identify the traffic black spots of different length, efficiently overcome MAUP problem, and meanwhile mine the spatio-temporal aggregative characteristics of traffic accidents to provide auxiliary decision supports for making suitable emergency plans for traffic safety.