@article{ref1,
title="Exploring injury severity of vulnerable road user involved crashes across seasons: a hybrid method integrating random parameter logit model and Bayesian network",
journal="Safety science",
year="2022",
author="Sun, Zhiyuan and Xing, Yuxuan and Wang, Jianyu and Gu, Xin and Lu, Huapu and Chen, Yanyan",
volume="150",
number="",
pages="e105682-e105682",
abstract="Recently, vulnerable road user to motor vehicle (VRU-MV) crashes have been the focus of much attention. Data from Shenyang, China were used for crashes analysis and significant differences existed in VRU-MV crashes across seasons. Therefore, this study analysed three crash datasets (whole, &quot;spring & summer&quot;, &quot;fall & winter&quot;) by using a hybrid method integrating random parameter logit (RP-logit) model and Bayesian network (BN). First, RP-logit model was established to find out significant factors and heterogeneity considering those three datasets. Second, significant factors identified from RP-logit model were utilized to establish a BN to investigate statistical associations between injury severity and explanatory attributes. <br><br>RESULTS showed seven significant factors were found in &quot;spring & summer&quot; dataset while five significant factors in &quot;fall & winter&quot; dataset, four hidden factors were found by comparative analysis of those three datasets. Besides, five factors were found to be random and normally distributed. <br><br>RESULTS of BN indicated that some factors could significantly increase high possibility of fatality when combined with other factors. For example, functional zone (ZON) in &quot;spring & summer&quot; dataset and motor vehicle type (MVT) in &quot;fall & winter&quot; dataset. The proposed hybrid method demonstrated both the consistence of methods (RP-logit model & BN) and the differences across seasons for VRU-MV crashes analysis. Three personalized factors including physical isolation (PI), crash type (CT) and motor vehicle type (MVT) made great differences across seasons.<p /> <p>Language: en</p>",
language="en",
issn="0925-7535",
doi="10.1016/j.ssci.2022.105682",
url="http://dx.doi.org/10.1016/j.ssci.2022.105682"
}