Predicting Injury Severity of Road Traffic Accidents Using a Hybrid Extreme Gradient Boosting and Deep Neural Network Approach

Author(s):  
Biswajeet Pradhan ◽  
Maher Ibrahim Sameen
Author(s):  
Jaratsri Rungrattanaubol ◽  
Anamai Na-udom ◽  
Antony Harfield

This paper introduces a computer-based model for predicting the severity of injuries in road traffic accidents. Using accident data from surveys at hospitals in Thailand, standard data mining techniques were applied to train and test a multilayer perceptron neural network. The resulting neural network specification was loaded into an interactive environment called EDEN that enables further exploration of the computer-based model. Although the model can be used for the classification of accident data in terms of injury severity (in a similar way to other data mining tools), the EDEN tool enables deeper exploration of the underlying factors that might affect injury severity in road traffic accidents. The aim of this paper is to describe the development of the computer-based model and to demonstrate the potential of EDEN as an interactive tool for knowledge discovery.


IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ahmed Abdulkareem Ahmed ◽  
Biswajeet Pradhan ◽  
Subrata Chakraborty ◽  
Abdullah Alamri ◽  
Chang-Wook Lee

2021 ◽  
Vol 170 ◽  
pp. 120903
Author(s):  
Prajwal Eachempati ◽  
Praveen Ranjan Srivastava ◽  
Ajay Kumar ◽  
Kim Hua Tan ◽  
Shivam Gupta

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