Abstract

This study is intended to focus on the major factors affecting traffic crash rates and severity levels, in addition to identifying crash-prone locations (i.e., black spots) based on the two indicators. The available crash data for different road segments used for the analysis were obtained from the Washington state database provided by the Highway Safety Information System (HSIS) for the years 2006 to 2011. A Random Forest (RF) classifier was used to predict the outcome level of crash severity, while crash rates were predicted by applying RF regressor. Certain features were selected for each model besides the abstraction of new features to check if there are unobserved correlations affecting the independent variables, such as accounting for the number and weight of crashes within 1 km2 area by implementing the Getis-Ord Gi∗ index. Moreover, to calculate the collective risk (CR) score, crash rates were adjusted to incorporate crash severity weights (cost per severity type) and regression-to-the-mean (RTM) bias via Empirical Bayes (EB) method. Finally, segments were ranked according to their CR score.

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The different versions of the original document can be found in:

http://downloads.hindawi.com/journals/jat/2020/8837762.xml,
http://dx.doi.org/10.1155/2020/8837762
https://doaj.org/toc/2042-3195 under the license https://creativecommons.org/licenses/by/4.0/
http://downloads.hindawi.com/journals/jat/2020/8837762.pdf,
https://academic.microsoft.com/#/detail/3095048088
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Published on 01/01/2020

Volume 2020, 2020
DOI: 10.1155/2020/8837762
Licence: Other

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