@article{ref1,
title="An enhanced empirical Bayesian method for identifying road hotspots and predicting number of crashes",
journal="Journal of transportation safety and security",
year="2019",
author="Lee, Alexander S. and Lin, Wei-Hua and Gill, Gurdiljot Singh and Cheng, Wen",
volume="11",
number="5",
pages="562-578",
abstract="The Empirical Bayesian (EB) method has been widely used for traffic safety analysis. It is well known that the EB method is powerful in handling the regression-to-the-mean bias that would often arise in traffic safety analysis. A prerequisite for applying the EB method for the estimation of the safety of a road segment is to identify a group of similar road segments. In this article, the authors intend to enhance the EB method by incorporating a similarity measure based on the Proportion Discordance Ratio (PDR) into the procedure to identify similar road segments safety wise. Specifically, a methodology to assess and objectively quantify similarity among road segments based on crash patterns is developed, where each crash pattern contains a unique combination of selected crash-related features. Improvement in predicting the number of crashes that would occur in road segments by applying the EB method enhanced by the PDR is demonstrated through a case study.<p /> <p>Language: en</p>",
language="en",
issn="1943-9962",
doi="10.1080/19439962.2018.1450314",
url="http://dx.doi.org/10.1080/19439962.2018.1450314"
}