@article{ref1,
title="Modeling crash frequency and severity using multinomial-generalized Poisson model with error components",
journal="Accident analysis and prevention",
year="2013",
author="Chiou, Yu-Chiun and Fu, Chiang",
volume="50",
number="",
pages="73-82",
abstract="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.<p /> <p>Language: en</p>",
language="en",
issn="0001-4575",
doi="10.1016/j.aap.2012.03.030",
url="http://dx.doi.org/10.1016/j.aap.2012.03.030"
}