TY  - JOUR
PY  - 2021//
TI  - Analyzing accident injury severity via an extreme gradient boosting (XGBoost) model
JO  - Journal of advanced transportation
A1  - Wu, Shubo
A1  - Yuan, Quan
A1  - Yan, Zhongwei
A1  - Xu, Qing
A1  - Truong, Long
SP  - e3771640
EP  - e3771640
VL  - 2021
IS  - 
N2  - Vehicle to vulnerable road user (VRU) crashes occupy a large proportion of traffic crashes in China, and crash injury severity analysis can support traffic managers to understand the implicit rules behind the crashes. Therefore, 554 VRUs-involved crashes are collected from January, 2017, to February, 2021, in a city in northern China, including 322 vehicle-pedestrian crashes and 232 vehicle-bicycle crashes. First, a descriptive statistical analysis is conducted to investigate the characteristics of VRUs-involved crashes. Second, the extreme gradient boosting (XGBoost) model is introduced to identify the importance of risk factors (i.e., time of day, day of week, rushing hour, crash position, weather, and crash involvements) of VRUs-involved crashes. The statistical analysis demonstrates that the risk factors are closely related to VRUs-involved crash injury severity. Moreover, the results of XGBoost reveal that time of day has the greatest impact on VRUs-involved crashes, and crash position shows the minimum importance among these risk factors.<p />  <p>Language: en</p>
LA  - en
SN  - 0197-6729
UR  - http://dx.doi.org/10.1155/2021/3771640
ID  - ref1
ER  -