Traffic Accident Analysis Using Decision Trees and Neural Networks

TL;DR

This paper compares decision trees and neural networks for modeling traffic accident injury severity, finding decision trees perform better and identifying key factors influencing fatal injuries.

Contribution

It introduces a comparative analysis of decision trees and neural networks for traffic injury severity prediction using real-world data.

Findings

Decision trees outperform neural networks in accuracy.

Key injury factors include seat belt use, lighting, and alcohol consumption.

Decision trees provide clearer insights into important risk factors.

Abstract

The costs of fatalities and injuries due to traffic accident have a great impact on society. This paper presents our research to model the severity of injury resulting from traffic accidents using artificial neural networks and decision trees. We have applied them to an actual data set obtained from the National Automotive Sampling System (NASS) General Estimates System (GES). Experiment results reveal that in all the cases the decision tree outperforms the neural network. Our research analysis also shows that the three most important factors in fatal injury are: driver's seat belt usage, light condition of the roadway, and driver's alcohol usage.