Examining the spatial correlations between road traffic crash severity and their causative factors: A GIS-based geographically weighted regression approach for Southwest Nigeria

This study explored the spatial relationship between Road Traffic Crash (RTC) severity and their determinants in southwest Nigeria with a view to accounting for how the influence of these determinants varies across the study area for the implementation of target-oriented crash reduction strategies.

Real-time RTC data for the area was collected over five months using a mobile app developed by the author. Federal Road Safety Corps (FRSC) rescue officers across the 28 commands in the study area were trained to use the app, allowing them to capture and send real-time RTC reports to the app’s central server. The datasets were analyzed using a GIS-Based Geographically Weighted Regression (GWR) model.

The results reveal that 13 factors account for 80% of RTC severity in the area with speeding being the main predictor, accounting for as high as 98% of the severity-weighted crashes on single-carrier highways, while fatigue (75–78%) and wrong-way driving (6.5–8.9%) contributed to most of the crashes on dual-carrier highways.

The study calls for immediate action to reduce RTCs and recommends that the FRSC design place-specific interventions targeting prevalent RTC causative factors along each road in the study area.