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Highway Traffic Accident Severity Prediction Method Based on Deep Learning

A traffic accident and deep learning technology, applied in forecasting, data processing applications, instruments, etc., can solve the problems of large model error, incomprehensible analysis, less consideration of influencing factors, etc., and achieve the effect of accurate establishment and small error.

Active Publication Date: 2021-07-27
SOUTHEAST UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] At present, the analysis of accident severity at home and abroad is still mainly at the level of a single data source and traditional statistical analysis methods, with less consideration of influencing factors, often incomplete analysis, and large model errors

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  • Highway Traffic Accident Severity Prediction Method Based on Deep Learning
  • Highway Traffic Accident Severity Prediction Method Based on Deep Learning
  • Highway Traffic Accident Severity Prediction Method Based on Deep Learning

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Embodiment Construction

[0044] In order to make the object, technical solution and advantages of the present invention clearer, the specific implementation cases of the present invention will be described below in conjunction with the accompanying drawings.

[0045] The data set used in this embodiment is a multi-source data set of accidents, weather, driver conditions and road conditions in a certain city from 2011 to 2015. Such as figure 1 As shown, the severity prediction of expressway traffic accidents based on deep learning includes the following steps:

[0046] (1) Collect M variable factors such as road conditions, driver conditions, and vehicle conditions when L traffic accidents occur to form a sample set S=(s 1 ,s 2 ,...,s L ), where s l =(f 1l , f 2l ,..., f Ml ) T , f hl is the hth variable factor of the accident numbered l; record the severity value of each traffic accident, r l is the severity value of the accident numbered l, h=1..M, l=1..L;

[0047] The relevant information...

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Abstract

The invention discloses a method for predicting the severity of expressway traffic accidents based on deep learning, which includes the following steps: 1. Collecting M variable factors such as road conditions, driver conditions, and vehicle conditions when L traffic accidents occur, to form samples set; record the severity value r of each traffic accident l ; 2. Carry out dimension reduction and normalization on the variable factors of the collected L accident samples; 3. Establish a deep learning neural network to construct a traffic accident severity prediction model; 4. Reduce the variable factors of the accident to be predicted after dimensionality reduction The vector x is substituted into the traffic accident severity prediction model established in step 3 to obtain the severity prediction result of the accident to be predicted. This method can accurately predict the severity of highway accidents.

Description

technical field [0001] The invention belongs to the field of traffic accident analysis and prediction, and in particular relates to a method for predicting the severity of expressway traffic accidents based on deep learning. Background technique [0002] At present, the analysis of accident severity at home and abroad is still mainly at the level of a single data source and traditional statistical analysis methods, with less consideration of influencing factors, often incomplete analysis, and large model errors. As technology advances, data collection is becoming easier. A large amount of data can be collected on factors related to traffic accidents, such as road geometry, coil data, weather conditions, road visibility, accident driver status, etc. How to control the severity of accidents within a certain range based on the analysis of massive data through scientific methods is an important issue that needs to be resolved urgently. Contents of the invention [0003] Purp...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q50/26G06N3/04
CPCG06Q10/04G06Q50/26G06N3/045
Inventor 何杰章晨刘子洋邢璐周博见
Owner SOUTHEAST UNIV