@article{ref1,
title="Modeling the effects of lake-effect snow related weather conditions on daily traffic crashes: a time series count data approach",
journal="Accident analysis and prevention",
year="2020",
author="Ayon, Bandhan Dutta and Ofori-Amoah, Benjamin and Oh, Jun-Seok and Baker, Kathleen",
volume="144",
number="",
pages="e105510-e105510",
abstract="Highlights  • The study develops a crash count model establishing the relationship between lake-effect snow (LES) and traffic crashes.   • The methodological approach uses Integer-valued Generalized Autoregressive Conditional Heteroscedastic (INGARCH) model.   • Negative Binomial INGARCH model outperformed Poisson INGARCH model by managing the overdispersion and autocorrelation issues.   • The model also captured the temporal correlation and allowed nonnegative covariate effects.   • Overall, the proposed method enables safety personnel to better understand the impact of LES on increased crashes.<p /> <p>Language: en</p>",
language="en",
issn="0001-4575",
doi="10.1016/j.aap.2020.105510",
url="http://dx.doi.org/10.1016/j.aap.2020.105510"
}