Volume List  / Volume 7 (2)



DOI: 10.7708/ijtte.2017.7(2).05

7 / 2 / 203-215 Pages


Oyedepo Olugbenga Joseph - Department of Civil and Environmental Engineering, Federal University of Technology, Akure Ondo State, Nigeria -

Etu Japheth Eromietse - Department of Civil and Environmental Engineering, Federal University of Technology, Akure Ondo State, Nigeria -

De Souza Emmanuel - Department of Civil and Environmental Engineering, Federal University of Technology, Akure Ondo State, Nigeria -

Afolabi Olufunke - Department of Civil and Environmental Engineering, Federal University of Technology, Akure Ondo State, Nigeria -


Understanding and predicting factors affecting car ownership and use is important given its impacts on transportation planning. This study investigates contributing factors to car ownership in Akure. The study’s data were gotten through the administration of a well-structured questionnaire survey from May-July 2016 to households in the city. The study area was divided High, Medium and Low population density zones with the survey yielding a return of 1002 questionnaire out of the 1181 distributed. SPSS 22 was used for both descriptive statistical analysis and formulation of the Multinomial Logit Model of car ownership. Model estimates shows that the probability of owning a car increases with increase in income (I2) and reduces with increase in household members (HN). Sensitivity analysis was carried out with the model estimates to test different scenarios. The test results show that a 50% increase in income and Number of household members increases the number of cars owned in Akure metropolis by 51% and 0.8% respectively while a decrease in I2 and HN by 50% reduces the number of cars owned by 37% and 0.8% respectively. R-square value of 0.705 and P-value of 0.000 implies that the model gives a good prediction. The study has shown that car ownership will increase with increase in earning capacity of households in Akure such as in the periods of economic boom while the reverse is the case in periods of recession. The results will assist transportation planners and agencies in solving urban traffic problems in the city.

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