title: Modeling crash frequency and severity using multinomial-generalized Poisson model with error components.
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.
authors.affiliation: Institute of Traffic and Transportation, National Chiao Tung University, 4F, 118, Sec. 1, Chung-Hsiao W. Rd., Taipei 100, Taiwan. ycchiou@mail.nctu.edu.tw
authors.forename: Yu-Chiun
authors.initials: YC
authors.lastname: Chiou
authors.forename: Chiang
authors.initials: C
authors.lastname: Fu
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001334:Automobile Driving / Q000706:statistics & numerical data*; D004779:Environment Design; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D016012:Poisson Distribution; D011237:Predictive Value of Tests; D012449:Safety / Q000706:statistics & numerical data