TY  - JOUR
PY  - 2019//
TI  - Spatial-temporal analysis of injury severity with geographically weighted panel logistic regression model
JO  - Journal of advanced transportation
A1  - Xiao, Daiquan
A1  - Xu, Xuecai
A1  - Duan, Li
SP  - e8521649
EP  - e8521649
VL  - 2019
IS  - 
N2  - This study is intended to investigate the influencing factors of injury severity by considering the heterogeneity issue of unobserved factors at different arterials and the spatial attributes in geographically weighted regression models. To achieve the objectives, geographically weighted panel logistic regression model was developed, in which the geographically weighted logistic regression model addressed the injury severity from the spatial perspective, while the panel data model accommodated the heterogeneity attributed to unobserved factors from the temporal perspective. The geo-crash data of Las Vegas metropolitan area from 2014 to 2016 was collected, involving 27 arterials with 25,029 injury samples. By comparing the conventional logistic regression model and geographically weighted logistic regression models, the geographically weighted panel logistic regression model showed preference to the other models. <br><br>RESULTS revealed that four main factors, human-beings (drivers/pedestrians/cyclists), vehicles, roadway, and environment, were potentially significant factors of increasing the injury severity. The findings provide useful insights for practitioners and policy makers to improve safety along arterials.<p />  <p>Language: en</p>
LA  - en
SN  - 0197-6729
UR  - http://dx.doi.org/10.1155/2019/8521649
ID  - ref1
ER  -