TY - JOUR
T1 - Analyzing truck accident data on the interurban road Ankara–Aksaray–Eregli in Turkey
T2 - Comparing the performances of negative binomial regression and the artificial neural networks models
AU - Ture Kibar, Funda
AU - Celik, Fazil
AU - Wegman, Fred
PY - 2019
Y1 - 2019
N2 - 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 R2 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.
AB - 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 R2 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.
KW - artificial neural network
KW - divided multilane interurban road
KW - negative binomial regression
KW - road geometric characteristics
KW - traffic characteristics
KW - truck accidents
UR - http://www.scopus.com/inward/record.url?scp=85066396385&partnerID=8YFLogxK
U2 - 10.1080/19439962.2017.1363841
DO - 10.1080/19439962.2017.1363841
M3 - Article
AN - SCOPUS:85066396385
SN - 1943-9962
VL - 11
SP - 129
EP - 149
JO - Journal of Transportation Safety and Security
JF - Journal of Transportation Safety and Security
IS - 2
ER -