Analyzing truck accident data on the interurban road Ankara-Aksaray-Eregli in Turkey: Comparing the performances of negative binomial regression and the artificial neural networks models


Kibar F. T. , Celik F., Wegman F.

JOURNAL OF TRANSPORTATION SAFETY & SECURITY, vol.11, no.2, pp.129-149, 2019 (Journal Indexed in SSCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 11 Issue: 2
  • Publication Date: 2019
  • Doi Number: 10.1080/19439962.2017.1363841
  • Title of Journal : JOURNAL OF TRANSPORTATION SAFETY & SECURITY
  • Page Numbers: pp.129-149

Abstract

Statistical methods such as Poisson distribution, negative binomial regression (NB), and zero inflated negative binomial regression (ZINB) have generally been used in road safety studies to establish the complex relationships between variables. Over the last few years, the artificial neural networks (ANN) model has also been used. The ANN model does not have any prior limitations such as the equality condition of mean and variance observed in Poisson regression. However, though the ANN model has been used in the analysis of different accident types, to the best of our knowledge, no study has used the ANN model for establishing this relationship with truck accident data on divided multilane interurban roads. In this study, the road sections D750/07-D750/15 in Ankara-Aksaray-Eregli, Turkey, were considered and truck accident data from 2008 to 2011 were analyzed using NB and ANN. The analysis show that the ANN model has lower errors and higher R-2 values than NB and performs slightly better than NB for predicting the number of trucks involved in accidents. Based on a comparison of performances the study concludes, that ANN could be used as an alternative model for analyzing truck accident data on divided multilane interurban roads in Turkey.