TY  - JOUR
PY  - 2021//
TI  - Pedestrian crash exposure analysis using alternative geographically weighted regression models
JO  - Journal of advanced transportation
A1  - Almasi, Seyed Ahmad
A1  - Behnood, Hamid Reza
A1  - Arvin, Ramin
SP  - e6667688
EP  - e6667688
VL  - 2021
IS  - 
N2  - In order to develop a sustainable, safe, and dynamic transportation system, proper attention must be paid to the safety of pedestrians. The purpose of this study is to analyze the surrogate measures related to pedestrian crash exposure in urban roads, including the use of sociodemographic characteristics, land use, and geometric characteristics of the network. This study develops pedestrian exposure models using geographical spatial models including geographically weighted regression (GWR), geographically weighted Poisson regression (GWPR), and geographically weighted Gaussian regression (GWGR). In general, the results of the GWPR model show that the presence of a bus station, population density, type of residential use, average number of lanes, number of traffic control cameras, and sidewalk width are negatively associated with increasing the number of crashes. In this study, in order to identify traffic analysis zones (TAZ) based on the observed and predicted crash data, spatial distance-based methods using GWPR outputs have been used. This study shows the dispersion and density of pedestrian crashes without possessing the volume of pedestrians. Comparison of the performance of GWPR and Poisson models shows a significant spatial heterogeneity in the analysis.<p />  <p>Language: en</p>
LA  - en
SN  - 0197-6729
UR  - http://dx.doi.org/10.1155/2021/6667688
ID  - ref1
ER  -