@article{ref1,
title="Exploring relationships between months and different crash types on mountainous freeways using a combined modeling approach",
journal="Journal of advanced transportation",
year="2022",
author="Zhang, Changjian and He, Jie and Bai, Chunguang and Yan, Xintong and Wang, Chenwei and Guo, Yazhong",
volume="2022",
number="",
pages="e6716275-e6716275",
abstract="Investigating the relationship between the months and traffic crashes is a foremost task for the safety improvement of mountainous freeways. Taking a mountainous freeway located in China as an example, this paper proposed a combined modeling framework to identify the relationships between months and different crash types. K-means and Apriori were initially used to extract the monthly distribution patterns of different types of crashes. A graphical approach and a risk calculation equation were developed to assess the output of K-means and Apriori. Then, using the assessment results as the input, a logistic regression model was constructed to quantify the effects of each month on crashes. The results indicate that the monthly distribution patterns of different crash types are inconsistent, i.e., for a specific month, the high risk of a certain crash type may be covered up if experts only focus on the total number of crashes. Moreover, when identified as high-risk months by K-means and Apriori, the crash-proneness will significantly increase several times than months identified as high-risk by only one of K-means and Apriori, thereby illustrating the superior performance of the mix-method. The conclusions can assist local relevant organizations in formulating strategies for preventing different types of traffic crashes in different months (e.g., the risk of rear-end crashes in August, the risk of fixed-object hitting crashes in February, and the risk of overturning crashes in October) and provide a methodological reference for relevant studies in other regions.<p /> <p>Language: en</p>",
language="en",
issn="0197-6729",
doi="10.1155/2022/6716275",
url="http://dx.doi.org/10.1155/2022/6716275"
}