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Traffic Accident Severity Prediction CSP-CNN Model and Its Modeling Method

A technology of traffic accidents and modeling methods, applied in the direction of neural learning methods, biological neural network models, character and pattern recognition, etc., can solve the problems that affect the severity of traffic accident casualties, without detailed consideration and mining, etc.

Active Publication Date: 2021-11-19
YUNNAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the above related works have not considered and explored the space, combination and deeper internal relationship among the features that affect the severity of traffic accident casualties in detail.

Method used

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  • Traffic Accident Severity Prediction CSP-CNN Model and Its Modeling Method
  • Traffic Accident Severity Prediction CSP-CNN Model and Its Modeling Method
  • Traffic Accident Severity Prediction CSP-CNN Model and Its Modeling Method

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Embodiment Construction

[0089] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0090] To predict the severity of traffic accident casualties, traffic accident data sets with characteristic information must be considered comprehensively. The known factors that affect the severity of traffic accidents mainly include the following five parent features: road surface characteristics, accident characteristics, vehicle characteristics, driver characteristics, and Environmental factors. Based on the above five parent features that affect the seve...

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Abstract

The invention discloses a traffic accident severity prediction CSP-CNN model and a modeling method thereof. The CSP-CNN model includes a model input layer. The model input layer inputs the traffic accident data grayscale image set converted from traffic accident data, and performs convolution calculation on its input convolution layer to obtain the feature vector extracted by the last convolution layer, and Input the feature vector to the fully connected layer; the fully connected layer performs flatten operation on the input feature vector, converts it into a one-dimensional vector and performs linear processing, the fully connected layer contains 3 hidden units, and outputs 3 linear processing results to the model Output layer: The model output layer sets three traffic accident severity levels, and uses the Softmax activation function to predict the severity of traffic accidents. The invention fully considers the time-space relationship, combination relationship and deeper internal relationship among the characteristics of the traffic accident, and predicts the severity of the traffic accident.

Description

technical field [0001] The invention belongs to the technical field of data mining, and in particular relates to a traffic accident severity prediction model based on deep learning and a modeling method thereof. Background technique [0002] Every year, more than 1.25 million people around the world lose their lives as a result of road traffic accidents, and an additional 20 to 50 million suffer non-fatal injuries, many of them disabled. Road traffic injuries cost individuals, families, and entire countries economically, with road traffic collisions costing up to 3% of GDP in most countries. [0003] Accident severity prediction is one of the important steps in accident management, which provides important information for emergency personnel to assess the severity of accidents, evaluate the potential impact of accidents, and implement effective accident management procedures. Since the work of correctly predicting the severity of traffic accidents will provide extremely imp...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/24G06F18/214
Inventor 李彤郑明朱锐
Owner YUNNAN UNIV
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