Method for predicting city traffic accidents based on time-space distribution characteristics

A technology of traffic accidents and spatio-temporal distribution, which is applied in prediction, instrumentation, data processing applications, etc., can solve problems such as serious subjectivity, artificially obtained, and changes, and achieve the effect of reducing traffic accidents and accurately identifying and expressing

CN106991510AInactive Publication Date: 2017-07-28FUJIAN JIANGXIA UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2017-07-28
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention relates to a method for predicting city traffic accidents based on time-space distribution characteristics. The method comprises: first, in combination of the case information and the space information, creating a case space database and performing pretreatment to the data; then, based on surface area statistics, analyzing the traffic accidents' time-space distribution characteristics; using the global and local self-correlation method to realize the analyzing of the aggregate state; based on the case happening point data, analyzing the traffic accidents' time-space distribution characteristics; through the hierarchical clustering analysis, expressing the distribution rule of the cases hierarchically; through the nuclear density estimation method, expressing the continuous changes and accurate gathering center of the traffic accidents' happening distribution; and finally, utilizing the BP neural network prediction algorithm, using the time-space distribution characteristics of the already happened cases to predict the time-space distribution areas of traffic accidents in the future. According to the invention, in combination with the time-space distribution and through the utilization of big date excavation BP neural network prediction algorithm and the time-space distribution characteristics of the already happened cases to predict the time-space distribution areas of traffic accidents in the future, it is possible to increase the precision, the timeliness and reduce the manual cost.
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Description

technical field

[0001] The invention relates to the field of traffic accident analysis, in particular to a method for predicting urban traffic accidents based on time-space distribution characteristics. Background technique

[0002] At present, foreign scholars have conducted in-depth research on the geography of traffic accident cases, and have achieved fruitful results in the formation mechanism and characteristics of the temporal and spatial distribution of traffic accidents. The current main technology for the temporal and spatial distribution of traffic accident cases is to use the correlation analysis method to study. This method needs to limit several specific factors in advance, by continuously collecting accident data of a specific road section, artificially fitting the correlation between various factors, and then analyzing the obtained data to predict the distribution of each road section in different time and space.

[0003] The traditional method has the follow...

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Embodiment Construction

[0041] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0042]Such as figure 1 As shown, the present embodiment provides a method for predicting urban traffic accidents based on spatiotemporal distribution characteristics, which specifically includes the following steps:

[0043] In the first step, the present invention needs to collect and preprocess the research data. Areas where traffic accidents occur include: urban traffic, rural roads, expressways, railways, etc. Traffic accidents include the following types: property damage accidents, personal injury accidents, fatal accidents, etc. The reason for choosing urban traffic accidents to study the spatio-temporal distribution characteristics is that there are more traffic volumes in cities, the traffic roads are more complex, and there are more factors to be considered. The road conditions in different regions are different, and the traffic volume at diff...