作者：
Gil-Marin, Jhan Kevin*;Shirazi, Mohammadali*;Ivan, John N*
通讯作者：
Gil-Marin, Jhan Kevin;Shirazi, Mohammadali;Ivan, John N
作者机构：
Department of Civil and Environmental Engineering, University of Maine, Orono, ME, 04469, USA. Electronic address: jhan.gil@maine.edu
Department of Civil and Environmental Engineering, University of Maine, Orono, ME, 04469, USA. Electronic address: shirazi@maine.edu
Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT, 06269, USA. Electronic address: john.ivan@uconn.edu
通讯机构：
Department of Civil and Environmental Engineering, University of Maine, Orono, ME, 04469, USA. Electronic address: jhan.
Department of Civil and Environmental Engineering, University of Maine, Orono, ME, 04469, USA. Electronic address:
Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT, 06269, USA. Electronic address: john.
语种：
英文
关键词：
Expected Crash Frequency,Full Bayes,Hotspot Identification,Negative Binomial,Negative Binomial-Lindley
期刊：
Accident analysis and prevention
ISSN：
0001-4575
年：
2024
卷：
199
页码：
107478
摘要：
Identifying hazardous crash sites (or hotspots) is a crucial step in highway safety management. The Negative Binomial (NB) model is the most common model used in safety analyses and evaluations - including hotspot identification. The NB model, however, is not without limitations. In fact, this model does not perform well when data are highly dispersed, include excess zero observations, or have a long tail. Recently, the Negative Binomial-Lindley (NB-L) model has been proposed as an alternative to the NB. The NB-L model overcomes several limitations related to the NB, such as addressing the issue of excess zero observations in highly dispersed data. However, it is not clea...
