Poisson and Negative Binomial Regression Models Application to Model the Factors of Car Ownership in Akure, South West, Nigeria

Authors

  • Joseph O. Oyedepo Department of Civil and Environmental Engineering, Federal University of Technology, Akure, Nigeria
  • Japheth E. Etu Department of Civil and Environmental Engineering, Federal University of Technology, Akure, Nigeria

DOI:

https://doi.org/10.33736/jaspe.309.2016

Keywords:

Poisson, Binomial, Regression, Model, Household, Factors

Abstract

Increase in number of cars without commensurate increase in the number of transport facilities and infrastructures has led to diverse traffic problems in many Nigerian cities like Akure. Factors which contribute to increase in the numbers of cars owned in Akure metropolis were investigated in this study. The study area was divided into three density zones namely High, Medium and Low while, data was collected using well-structured household questionnaire survey distributed amongst residents; with the survey yielding a return of 1002 questionnaire out of the 1181 distributed. Results from field findings gave the average number of cars owned per household in the study area as 0.62. Results of the Poisson Regression Model show that a change in the number of employed household members will decrease the number of cars owned in the study area by 9% while, a unit increase in the number of driver’s license holders in the household, academic qualification and average monthly income of the household will increase the number of cars owned by 60%, 26% and 30% respectively. The negative binomial model indicates that a change in the number of employed household members will decrease the number of cars owned by 10% whereas a change in the number of driver’s license holders in the household and monthly income will lead to an increase in the number of cars owned by 101% and 24% respectively. The test of model effects affirm that all the predictor variables are statistically significant indicating a good fit for the model predicted. Out of the two models, Poisson regression model is found to be a superior model due to a higher log likelihood ratio Chi Square and improved statistically significant variables. The findings in this research will assist government agencies to plan future transportation infrastructure development.

References

Fenger, J. (1999): Urban Air Quality. Atmospheric Environment, Vol. 33, pp. 48-9

https://doi.org/10.1016/S1352-2310(99)00290-3

Rodrigue, J-P et al. (2013).The Geography of Transport Systems, Hofstra University, Department of Global Studies & Geography. Retrieved from http://www.people.hofstra.edu/geotrans.on 03/10/2013 at 12:00noon

Cirillo, C. and Liu, Y. (2013) Vehicle Ownership Modeling Framework for the State of Maryland: Analysis and Trends from 2001 and 2009 NHTS Data Transportation Model (MSTM). American Society of Civil Engineers.

https://doi.org/10.1061/(ASCE)UP.1943-5444.0000128

Awoyemi, O. K., Ita, A. E., Oke, M. O., Abdulkarim, I. A. & Awotayo, G. P.(2012). An Analysis of Trip Generation and Vehicular Traffic Pattern in Akure Metropolis Ondo State, Nigeria. Journals of Social Science and Public Policy, Volume 4.

Scott, D.M. & Axhausen, K.W. (2005). Household Mobility Tool Ownership: Modelling Interactions between Cars and Season Tickets Transportation (forthcoming).

https://doi.org/10.1007/s11116-005-0328-7

De Jong, G., Fox, J., Pieters, M.,Daly, A. and Smith, R., (2004) A comparison of car ownership models Transport Reviews 24(4), pp.379.

https://doi.org/10.1080/0144164032000138733

De Jong, G. C., Kitamura, R. and Klooster, J (1994): A disaggregate model of vehicle holding duration, type choice and use.

Dargay, J. and Gately, D. (1999). Income's effect on car and vehicle ownership worldwide: 1960-2015. Transport atcion Research Part A: Policy and Practice, 33(2):101-138.

https://doi.org/10.1016/S0965-8564(98)00026-3

Mokonyama, M. and Venter, C. (2007). Journal of the South African institution of civil engineering Vol 49 no 3, pages 2-10, paper 633.

Kalenoja, H. (2001). Car ownership effects on the availability of local service. The Nordic Research Network on modelling transport, land use and environment, Finland

Dargay J. and Vythoulkas P., (1999) Estimation of a Dynamic car Ownership Model; a Pseudo-panel Approach. Journal of transport economics and policy, September 199, vol 33, part 3, pp 287-302.

Oyedepo J. O. and Etu J. E. Statistical Model Analysis of Dependence on Motor Cycle Transport at Ifedore LGA Ondo Nigeria Futojnls, Volume2, Issue-1, Pp- 118 - 126

Agbelie, B.R.D.K. A comparative empirical analysis of statistical models for evaluating highway segment crash frequency. Journal of Traffic and Transportation Engineering (English Edition) (2016), http://dx.doi.org/ 10.1016/j.jtte.2016.07.001

https://doi.org/10.1016/j.jtte.2016.07.001

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Published

2016-09-30

How to Cite

Oyedepo, J. O., & Etu, J. E. (2016). Poisson and Negative Binomial Regression Models Application to Model the Factors of Car Ownership in Akure, South West, Nigeria. Journal of Applied Science &Amp; Process Engineering, 3(2), 72–82. https://doi.org/10.33736/jaspe.309.2016