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Highway accident prediction method and system based on gradient boosting tree model

A gradient boosting tree and accident prediction technology, applied in the field of traffic safety, can solve the problem of inability to quantify the risk state, and achieve the effect of improving the detection performance, reducing the false alarm rate, and overcoming the imbalance.

Pending Publication Date: 2021-08-20
招商新智科技有限公司
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AI Technical Summary

Problems solved by technology

[0004] None of the above traffic accident prediction methods can quantify the current risk status of each road section, so that managers can obtain current safety risk information in a timely manner

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  • Highway accident prediction method and system based on gradient boosting tree model

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Embodiment approach

[0080] As an implementation, data preprocessing also includes estimating the full-day cross-section traffic flow, including:

[0081] Through the traffic flow distribution curve of the latest day, the proportion of the hourly section traffic flow to the whole day section traffic flow is obtained;

[0082] Estimated daily cross-section traffic flow;

[0083] A sparse matrix of hourly accident data for road segments is formed.

[0084] The embodiment of this specification also provides a highway accident prediction system based on the gradient boosting tree model, including:

[0085] The data collection module is used to collect historical accident data to form a traffic accident data set;

[0086] The data cleaning module is used for data cleaning of the traffic accident data set;

[0087] The data preprocessing module is used to perform data preprocessing on the traffic accident data set after data cleaning;

[0088] The feature extraction module is used to perform feature...

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Abstract

One or more embodiments of the invention provide a highway accident prediction method and system based on a gradient boosting tree model, and the method comprises the steps of carrying out the data cleaning of a collected traffic accident data set, carrying out the data preprocessing, carrying out the feature extraction of the processed data, obtaining a key variable for predicting the occurrence of an accident, constructing and training a dynamic accident prediction model in combination with a gradient boosting tree model, and inputting the online data into the trained dynamic accident prediction model to obtain an output predicted accident value. According to the embodiment, the accident number of the road section can be predicted through the real accident data details, the problem that an existing model cannot carry out refined prediction on traffic accidents is solved, imbalance of training data can be overcome, and the effects of improving the detection performance and particularly reducing the false alarm rate are achieved; and compared with a traditional method, the model can carry out more accurate prediction on the traffic accidents.

Description

technical field [0001] One or more embodiments of this specification relate to the technical field of traffic safety, in particular to a method and system for expressway accident prediction based on a gradient boosting tree model. Background technique [0002] With the development of expressway operation and management system, the records of expressway accident data are more and more complete and perfect, making it possible to predict accidents using real expressway historical accident data; [0003] The current road traffic accident prediction uses long-term historical data in different regions to study the relationship between traffic accidents and environmental road geometric conditions, weather, traffic volume and other factors or the development trend of traffic accidents, and can only input multiple coarse-grained data. The average data and multiple fitting parameters are given, which cannot predict the occurrence of traffic accidents based on real traffic accident dat...

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Application Information

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IPC IPC(8): G06Q10/04G06F16/215G06F16/22G06K9/62
CPCG06Q10/04G06F16/215G06F16/22G06F18/23213G06F18/24323G06F18/214
Inventor 邹晓芳张威奕杨德元卢佳程刘砚
Owner 招商新智科技有限公司
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