Sh Hasanzadeh, H Sadeghi Bazargani , M Hashemiparast, M Asghari Jafarabadi ,
Volume 15, Issue 4 (Vol.15, No.4 2020)
Abstract
Background and Objectives: The aim of this study was to determine the predictability of the PLS-SEM model for injuries resulting in hospitalization in motorcyclists using a mediator variable in a case-control study.
Methods: In this case-control study, 300 cases and 156 controls were randomly selected from 150 clusters using random cluster sampling. The data were collected using the motorcycle riding behavior (MRB) questionnaire, adult attention deficit hyperactivity disorder (ADHD) questionnaire (subscales) and a checklist containing motorcycle related variables.
Results: The motorcycle riding behavior, adult ADHD, motorcycling related variables and some demographic variables were found to be the predictors of injury. There were significant positive relationships between injury and motorcycling related variables (B=0.20, P=0.001) and ADHD (B=0.33, P=0.001), between MRB and motorcycling related variables (B=0.51, P=0.001) and ADHD (B=0.52, P=0.001), and between ADHD and motorcycling related variables (B=0.39, P=0.001).
Conclusion: Considering the more accurate results of PLS-SEM, the intervention programs should especially address those who have hyperactive children, those who use the cellphone while riding, and those who ride in dark hours of the night.
Iman Dianat, Mohammad Sadegh Masoumi, Homayoun Sadeghi Bazargani, Gholam Hossein Safari, Sepideh Harzand-Jadidi,
Volume 20, Issue 4 (Vol.20, No.4, Winter 2025)
Abstract
Background and Objectives: One of the most important steps in reducing traffic accidents is the accurate recording of the spatial information of these incidents using Geographic Information Systems (GIS).The present study was conducted with the aim of geographically analyzing high-risk areas for traffic accidents in Tabriz and determining the spatial distribution pattern of traffic incidents based on accident outcomes.
Methods: In this descriptive-analytical study, data on property damage, injury, and fatal traffic accidents in Tabriz during 2017 were collected from various sources, and accident locations were identified using geographic addresses and coordinates. To analyze the geographical distribution of high-risk accident zones, spatial analysis methods including Moran’s Index, Kernel Density Estimation, Geographically Weighted Regression (GWR), and correlation analysis were employed.
Results: In this study, the cumulative pattern of accidents in Tabriz was confirmed, such that district 8, northeast of District 3, central and southern regions of District 1, and the entrance to District 5 of Tabriz city had dense accident distribution patterns. The religious, commercial, and service land-use layers had the highest correlation with accident density. High-traffic axes had fewer accidents; the highest was related to areas with low traffic volume. Autumn and winter have the most damage accidents, and the highest number of accidents leading to death was assigned to the spring season.
Conclusion: Given the concentration of traffic accidents in high-risk areas of Tabriz—particularly in District 8 and the eastern entrances—it is recommended that traffic infrastructure be improved, traffic regulations be strengthened, and monitoring be enhanced through intelligent systems to reduce accidents. Additionally, conducting public awareness campaigns can also be effective in decreasing traffic incidents.
