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Predicting Wet-Road Crashes Using the Finite-Mixture Zero-Truncated Negative Binomial Model

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成果类型:
期刊论文
作者:
Chen, Ying*;Huang, Zhongxiang
通讯作者:
Chen, Ying
作者机构:
[Chen, Ying] Changsha Univ Sci & Technol, Sch Traff & Transportat Engn, Sch Architecture, Changsha 410114, Peoples R China.
[Huang, Zhongxiang] Changsha Univ Sci & Technol, Sch Traff & Transportat Engn, Changsha 410114, Peoples R China.
通讯机构:
[Chen, Ying] C
Changsha Univ Sci & Technol, Sch Traff & Transportat Engn, Sch Architecture, Changsha 410114, Peoples R China.
语种:
英文
关键词:
Developing countries;Forecasting;Markov chains;Mixtures;Monte Carlo methods;Roads and streets;Traffic control;Travel time;Analytic method;Conventional modeling;Finite mixtures;Inclement weathers;Markov chain Monte Carlo method;Model approach;Negative binomial;Negative binomial models;Highway accidents
期刊:
Journal of Advanced Transportation
ISSN:
0197-6729
年:
2020
卷:
2020
页码:
1-9
基金类别:
This research was sponsored jointly by the National Natural Science Foundation of China (project no. 51978082); the Outstanding Youth Foundation of Hunan Education Department (project no. 19B022); and the Young Teacher Development Foundation of Changsha University of Science & Technology (project no. 2019QJCZ056).
机构署名:
本校为第一且通讯机构
院系归属:
交通运输工程学院
建筑学院
摘要:
Inclement weather affects traffic safety in various ways. Crashes on rainy days not only cause fatalities and injuries but also significantly increase travel time. Accurately predicting crash risk under inclement weather conditions is helpful and informative to both roadway agencies and roadway users. Safety researchers have proposed various analytic methods to predict crashes. However, most of them require complete roadway inventory, traffic, and crash data. Data incompleteness is a challenge in many developing countries. It is common that saf...

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