TY - JOUR
T1 - Modeling crash frequency and severity using multinomial-generalized Poisson model with error components
AU - Chiou, Yu Chiun
AU - Fu, Chiang
N1 - Funding Information:
The authors are indebted to three anonymous reviewers for their insightful comments and constructive suggestions, which help clarify several points made in the original manuscript. This study was financially sponsored by the ROC National Science Council ( NSC 97-2628-E-009-035-MY3 ).
PY - 2013/1
Y1 - 2013/1
N2 - Since the factors contributing to crash frequency and severity usually differ, an integrated model under the multinomial generalized Poisson (MGP) architecture is proposed to analyze simultaneously crash frequency and severity - making estimation results increasingly efficient and useful. Considering the substitution pattern among severity levels and the shared error structure, four models are proposed and compared - the MGP model with or without error components (EMGP and MGP models, respectively) and two nested generalized Poisson models (NGP model). A case study based on accident data for Taiwan's No. 1 Freeway is conducted. The results show that the EMGP model has the best goodness-of-fit and prediction accuracy indices. Additionally, estimation results show that factors contributing to crash frequency and severity differ markedly. Safety improvement strategies are proposed accordingly.
AB - Since the factors contributing to crash frequency and severity usually differ, an integrated model under the multinomial generalized Poisson (MGP) architecture is proposed to analyze simultaneously crash frequency and severity - making estimation results increasingly efficient and useful. Considering the substitution pattern among severity levels and the shared error structure, four models are proposed and compared - the MGP model with or without error components (EMGP and MGP models, respectively) and two nested generalized Poisson models (NGP model). A case study based on accident data for Taiwan's No. 1 Freeway is conducted. The results show that the EMGP model has the best goodness-of-fit and prediction accuracy indices. Additionally, estimation results show that factors contributing to crash frequency and severity differ markedly. Safety improvement strategies are proposed accordingly.
UR - http://www.scopus.com/inward/record.url?scp=84870301896&partnerID=8YFLogxK
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U2 - 10.1016/j.aap.2012.03.030
DO - 10.1016/j.aap.2012.03.030
M3 - Article
C2 - 23200442
AN - SCOPUS:84870301896
SN - 0001-4575
VL - 50
SP - 73
EP - 82
JO - Accident Analysis and Prevention
JF - Accident Analysis and Prevention
ER -
