@article{ref1,
title="Spatial-temporal analysis of pedestrian injury severity with geographically and temporally weighted regression model in Hong Kong",
journal="Transportation research part F: traffic psychology and behaviour",
year="2020",
author="Xu, Xuecai and Luo, Xiangjian and Ma, Changxi and Xiao, Daiquan",
volume="69",
number="",
pages="286-300",
abstract="This study intended to (1) investigate the pedestrian injury severity involved in traffic crashes; and (2) address the spatial and temporal heterogeneity simultaneously. To achieve the objectives, geographically and temporally weighted regression (GTWR) model was proposed to deal with both spatial and temporal heterogeneity simultaneously. The pedestrian crash data of Hong Kong metropolitan area from 2008 to 2012 were collected, involving 1652 pedestrian-related injury samples. By comparing GTWR model and standard geographically weighted regression (GWR) model and temporally weighted regression (TWR) model, the proposed GTWR model showed potential benefits in modeling both spatial and temporal non-stationarity simultaneously in terms of goodness-of-fit and F statistics. <br><br>RESULTS revealed that number of vehicles, number of pedestrian-related casualties, speed limit, vehicle movement and injury location have significant influence on pedestrian injury severity in different areas. The conclusions are reached that GRWR model can address the relationship between pedestrian injury severities and influencing factors, as well as accommodating spatial and temporal heterogeneity simultaneously. The findings provide useful insights for practitioners and policy makers to improve pedestrian safety.<p /> <p>Language: en</p>",
language="en",
issn="1369-8478",
doi="10.1016/j.trf.2020.02.003",
url="http://dx.doi.org/10.1016/j.trf.2020.02.003"
}