MODELLING ROAD TRAFFIC CRASHES USING SPATIAL AUTOREGRESSIVE MODEL WITH ADDITIONAL ENDOGENOUS VARIABLE
MODELLING ROAD TRAFFIC CRASHES USING SPATIAL AUTOREGRESSIVE MODEL WITH ADDITIONAL ENDOGENOUS VARIABLE

Summary/Abstract: Road traffic crashes have become a global issue of concern because of thenumber of deaths and injuries. The model of interest is a linear cross sectional Spatial Autoregressive (SAR) model with additional endogenous variables,exogenous variables and SAR disturbances. The focus is on RTC in Oyo state,Nigeria. The number of RTC in each LGA of the state is the dependent variable.A 33×33 weights matrix; travel density; land area and major road length of eachLGA were used as exogenous variables and population was the IV. The objectiveis to determine the hotspots and examine whether the number of RTC cases in agiven LGA is affected by the number of RTC cases of neighbouring LGAs and aninstrumental variable. The hotspots include Oluyole, Ido, Akinyele, Egbeda,Atiba, Oyo East, and Ogbomosho South LGAs. The study concludes that thenumber of RTC in a given LGA is affected by the number of RTC in contiguousLGAs. The policy implication is that road safety and security measures must beadministered simultaneously to LGAs with high concentration of RTC and theirneighbours to achieve significant remedial effect.

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