Modelling Road Traffic Crashes Using Spatial Autoregressive Model With Additional Endogenous Variable

Olubusoye Olusanya Elisa (), Korter Grace Oluwatoyin () and Afees Salisu
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Olubusoye Olusanya Elisa: Department of Statistics, University of Ibadan, Oyo State, Nigeria
Korter Grace Oluwatoyin: Department of Statistics, University of Ibadan, Oyo State, Nigeria

Statistics in Transition New Series, 2016, vol. 17, issue 4, 659-670

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 (search for similar items in EconPapers)
Date: 2016
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Journal Article: MODELLING ROAD TRAFFIC CRASHES USING SPATIAL AUTOREGRESSIVE MODEL WITH ADDITIONAL ENDOGENOUS VARIABLE (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:stintr:v:17:y:2016:i:4:p:659-670:n:9

DOI: 10.21307/stattrans-2016-045

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