Traffic Accident Analysis Using Decision Trees and Neural Networks

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dc.creatorChong, Miao M.
dc.creatorAbraham, Ajith
dc.creatorPaprzycki, Marcin
dc.date2004-05-16
dc.date.accessioned2026-07-07T03:21:16Z
dc.date.available2026-07-07T03:21:16Z
dc.descriptionThe 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.
dc.identifierhttps://arxiv.org/abs/cs/0405050
dc.identifierhttp://arxiv.org/abs/cs/0405050
dc.identifierIADIS International Conference on Applied Computing, Portugal, IADIS Press, Pedro Isaias et al. (Eds.), ISBN: 9729894736, Volume 2, pp. 39-42, 2004
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/32131
dc.subjectArtificial Intelligence
dc.subjectI.2.0
dc.titleTraffic Accident Analysis Using Decision Trees and Neural Networks
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