Severity Prediction of Traffic Accident Using an Artificial Neural Network

Abstract

In this paper, an artificial neural network (ANN) was used to predict the injury severity of traffic accidents based on 5973 traffic accident records occurred in Abu Dhabi over a 6‐year period (from 2008 to 2013). For each accident record, 48 different attributes had been collected at the time of the accident. After data preprocessing, the data were reduced to 16 attributes and four injury severity classes. In this study, WEKA (Waikato Environment for Knowledge Analysis) data‐mining software was used to build the ANN classifier. The traffic accident data were used to build two classifiers in two different ways. The whole data set were used for training and validating the first classifier (training set), while 90% of the data were used for training the second classifier and the remaining 10% were used for testing it (testing set). The experimental results revealed that the developed ANN classifiers can predict accident severity with reasonable accuracy. The overall model prediction performance for the training and testing data were 81.6% and 74.6%, respectively. To improve the prediction accuracy of the ANN classifier, traffic accident data were split into three clusters using a k‐means algorithm. The results after clustering revealed significant improvement in the prediction accuracy of the ANN classifier, especially for the training dataset. In this work, and in order to validate the performance of the ANN model, an ordered probit model was also used as a comparative benchmark. The dependent variable (i.e. degree of injury) was transformed from ordinal to numerical (1, 2, 3, 4) for (minor, moderate, sever, death). The R tool was used to perform an ordered probit. For each accident, the ordered probit model showed how likely this accident would result in each class (minor, moderate, severe, death). The accuracy of 59.5% obtained from the ordered probit model was clearly less than the ANN accuracy value of 74.6%. Copyright © 2016 John Wiley & Sons, Ltd.

Investigating Key Factors Influencing the Severity of Drivers Injuries in Car Crashes Using Supervised Machine Learning Techniques

“…In these studies, the ANN prediction accuracy ranged between 55% and 65%. On the other hand, the ANN model by [46] resulted in overall prediction accuracy for the testing data of 74.6%. However, although their model could predict moderate and minor injuries with 81.6% and 74.6%, respectively, the model could not predict fatal and severe injuries at all.…”

Traffic Crash Severity Prediction—A Synergy by Hybrid Principal Component Analysis and Machine Learning Models

“…It was found that MLP was superior when compared with ART and OL. Alkheder et al [ 12 ] developed a NN model for the severity prediction of RTC in Abu Dhabi, UAE. A k-means clustering algorithm was used to cluster the crash dataset into three clusters.…”

Model Evaluation for Forecasting Traffic Accident Severity in Rainy Seasons Using Machine Learning Algorithms: Seoul City Study

“…ANNs can be used in various powerful ways: to teach non-linear system dynamics without any mathematical modeling [38][39][40], to analyze and generalize from samples and to make predictions, to memorize the features of given data, and to match or make associations from new data to old data [41]. In transportation research, many studies have applied ANN models to predict traffic accidents and severity [42][43][44], and such models have shown greater accuracy in predicting injury severity compared to other traditional methods, including ordered probit modeling [45]. On the other hand, ANN is disadvantageous for interpreting human factors to predict traffic accident severity [26].…”