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Space-autocorrelation-based traffic accident blackspot identification method and device

A technology of spatial autocorrelation and accident-prone points, applied in the traffic control system of road vehicles, special data processing applications, instruments, etc., can solve the problems of incomplete identification factors and insufficient deep mining of potential accident causes, etc.

Inactive Publication Date: 2017-01-25
TONGJI UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to provide a method for identifying multiple road accidents based on spatial autocorrelation, which overcomes the defects in the prior art that the identification factors are not comprehensive and the depth of potential accident causes is not enough when identifying accidents.

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  • Space-autocorrelation-based traffic accident blackspot identification method and device
  • Space-autocorrelation-based traffic accident blackspot identification method and device
  • Space-autocorrelation-based traffic accident blackspot identification method and device

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Embodiment Construction

[0067] The present invention will be further explained below with reference to the drawings.

[0068] The present invention is a method for identifying frequent points of road accidents based on spatial autocorrelation, such as figure 1 As shown, including the following steps:

[0069] Step S1, using a non-parametric kernel density estimation model to represent the two-dimensional distribution of the accident points to be detected in the area to be tested, select the accident points to be tested in the area to be tested, and adopt the nuclear density as the two-dimensional distribution of the accident points to be tested The model indicates that since the non-parametric kernel density estimation method does not use the prior knowledge of the distribution of the accident points to be tested, no assumption is attached to the distribution of the accident points to be tested, and the distribution of the accident points to be tested is studied from the accident points to be tested. Cha...

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Abstract

The invention belongs to the technical field of road safety evaluation, and discloses a space-autocorrelation-based traffic accident blackspot identification method and a space-autocorrelation-based traffic accident blackspot identification device. A geographical distribution characteristic is described from the angle of geographical accident distribution by combining accident attribute values on road space units on the premise of a proximity relationship between the road space units, and a traffic accident blackspot is identified by adopting a kernel density estimation and space autocorrelation method. The shortcoming of relatively poorer visual expression capability of research data due to adoption of a classical mathematical statistical analysis method for a traffic accident blackspot identification process in the prior art is overcome. By the method, traffic accident blackspots at road sections and intersections can be identified, so that identification results are more accurate. In addition, a special analysis function is fully utilized on the basis of spatial position and geographical position information, and a foundation is laid for finding out accident causes.

Description

Technical field [0001] The invention belongs to the technical field of road safety evaluation, and relates to a method and device for identifying frequent accident points based on spatial autocorrelation. Background technique [0002] The identification of accident-prone points is an important prerequisite for road safety evaluation and targeted improvement of traffic safety measures. Commonly used accident-prone points identification methods include accident number method, accident rate method, equivalent total accident number method, cluster analysis and Empirical Bayesian method, etc. [0003] The accident number method and the accident rate method are designed to determine a critical value as an indicator to judge the occurrence of accidents. They are applied to highways without considering the influence of random fluctuations and are not suitable for large-scale identification; the matrix method overcomes the accident number method to a certain extent And the defects of the a...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G08G1/01G06F19/00
CPCG08G1/0125G16Z99/00
Inventor 张兰芳陈雨人王震宇蒋宏
Owner TONGJI UNIV
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