Traffic Accident Analysis Using Decision Trees and Neural Networks
  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.
