High-risk road section identification method based on k-medoids clustering and Poisson inverse Gaussian

A recognition method and a clustering method technology, applied in the field of high-risk road section recognition based on k-medoids clustering and Poisson inverse Gaussian, can solve the problems of reducing the accuracy of the empirical Bayesian method, and achieve simple and easy calculation steps, Improved precision, flexibility and ease of effect

Active Publication Date: 2020-02-21
TONGJI UNIV
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Problems solved by technology

The defect of this method is that the accident information of the same type of road section should be considered when using the safety performance function, but road traffic accident data often contain potential heterogeneity, which is mainly reflected in road design

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  • High-risk road section identification method based on k-medoids clustering and Poisson inverse Gaussian
  • High-risk road section identification method based on k-medoids clustering and Poisson inverse Gaussian
  • High-risk road section identification method based on k-medoids clustering and Poisson inverse Gaussian

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Embodiment

[0070] The present invention proposes a high-risk section identification method based on k-medoids clustering and Poisson inverse Gaussian, which applies the clustering method to the division of safety performance functions, and introduces Poisson inverse Gaussian distribution into empirical Bayesian In the safety performance function of the road section, a long-term risk identification method is proposed at the same time. Therefore, this method can identify the heterogeneity factors between road sections, improve the accuracy of empirical Bayesian, thereby improving the reliability of high-risk road section identification, and for samples in a long period of time or in multiple time periods, This method can capture the long-term risk characteristics of road segments.

[0071] The present invention includes such as figure 1 The five steps shown are further explained in conjunction with the examples and charts, specifically as follows: The examples select the data of 1,499 dif...

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Abstract

The invention relates to a high-risk road section identification method based on k-medoids clustering and Poisson inverse Gauss. The method comprises the following steps: (1) clustering all research road sections based on a k-medoids clustering method to divide similar road sections and identify heterogeneity characteristic indexes; (2) constructing a road traffic accident distribution model basedon the heterogeneity characteristic indexes; (3) calculating the expected accident number of each road section by utilizing the road traffic accident distribution model; and (4) identifying the high-risk road section according to the expected accident number. Compared with the prior art, the method is high in recognition accuracy, can be flexibly used, and can describe the long-term risk of a high-risk road section according to the requirements of a research time range.

Description

technical field [0001] The invention relates to a high-risk road section identification method, in particular to a high-risk road section identification method based on k-medoids clustering and Poisson inverse Gaussian. Background technique [0002] In recent years, with the rapid development of my country's economy and the continuous acceleration of urbanization, the number of cars in my country has continued to increase, the mileage of expressways has increased rapidly, and the process of road traffic operation has changed from a single, independent, simple process It has gradually evolved into a group, interactive, and complex comprehensive process, and various traffic problems have also emerged, among which road traffic safety problems are not uncommon. Traffic accidents on expressways not only seriously endanger the lives of road users, but also bring considerable economic losses to the country, which greatly restricts the development of traffic and even all walks of lif...

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06Q50/26G08G1/01
CPCG06Q10/04G06Q10/0635G06Q10/067G06Q50/265G08G1/0125
Inventor 程凯邹亚杰张越杨小雪胡笳
Owner TONGJI UNIV
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