Modelling road traffic crashes  using spatial autoregressive model with additional endogenous variable

Olusanya Elisa  Olubusoye; Grace  Oluwatoyin Korter; Afees Adebare  Salisu

doi:https://doi.org/10.59170/stattrans-2016-036

Modelling road traffic crashes using spatial autoregressive model with additional endogenous variable

Olusanya Elisa Olubusoye Department of Statistics, University of Ibadan, Oyo State, Nigeria. , Grace Oluwatoyin Korter Department of Statistics, University of Ibadan, Oyo State, Nigeria. , Afees Adebare Salisu Department of Economics, University of Ibadan, Oyo State, Nigeria. Statistics in Transition new series, vol. 17, 2016, 4, pages: 659-670 Published online: 1 December 2016 https://doi.org/10.59170/stattrans-2016-036

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ABSTRACT

Road traffic crashes have become a global issue of concern because of the number 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 each LGA were used as exogenous variables and population was the IV. The objective is to determine the hotspots and examine whether the number of RTC cases in a given LGA is affected by the number of RTC cases of neighbouring LGAs and an instrumental variable. The hotspots include Oluyole, Ido, Akinyele, Egbeda, Atiba, Oyo East, and Ogbomosho South LGAs. The study concludes that the number of RTC in a given LGA is affected by the number of RTC in contiguous LGAs. The policy implication is that road safety and security measures must be administered simultaneously to LGAs with high concentration of RTC and their neighbours to achieve significant remedial effect.

KEYWORDS

road traffic crashes, generalized spatial two-stage least squares estimator, instrumental-variable estimation, spillover effects.

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