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.
In order to forecast and make provisions for future demand of motorcycle in Akure, there is a need to understand factors driving motorcycle ownership, therefore, this study examines factors which affect motorcycle ownership in Akure metropolis. Three different zones namely, Low Density (LD), Medium Density (MD) and High Density (HD) were considered,, both close-ended and open-ended questionnaire were administered to 900 households representing 75% of the total population. Using Statistical Package for Social Sciences version 16 data were analyzed and binomial logistic regression analysis was used in developing a model which showed that only academic qualification of household head, number of household members and average monthly income of household significantly influences motorcycle ownership across the zones at the 95% confidence level. Both average monthly income of household and academic qualification of household head had a negative influence on motorcycle ownership whereas number of household members had a positive relationship with motorcycle ownership. The results shows that there is a 1.43 times likelihood of owning a motorcycle with a unit increase in the number of household members, while there is a reduction in the likelihood of owning a motorcycle by 1.66 times and 2.17 times for a unit increase in the average monthly income and academic qualification of household head respectively. The results obtained can be used in developing policy framework to improve public transport and control motorcycle ownership in the city of Akure.
Evidence from literature has shown the absence of the use of Artificial Neural Network techniques in formulating trip generation forecasts in Nigeria, rather the practice has consisted more on use of regression techniques. Therefore, in this study, the accuracy of Radial Basis Function Neural Network (RBFNN) and Multiple Linear Regression model (MLR) in formulating home-based trips generation forecasts was assessed. Datasets for the study were acquired from a household travel survey in the high density zones of Akure, Nigeria and were analysed using SPSS 22 statistical software. Results of data analysis showed that the RBFNN model with higher Coefficient of Determination (R2) value of 0.913 and lower Mean Absolute Percentage Error (MAPE) of 0.421 performed better than the MLR with lower R2 value of 0.552 and higher MAPE of 0.810 in predicting the number of home-based trips generated in the study area. The study demonstrated the higher accuracy of the RBFNN in producing trip generation forecasts in the study area and is consequently recommended for researchers in executing such forecasts.
With the rapid rise in problems associated with use of motorcycles as alternative means to inadequate public transportation, this study seeks to identify household factors influencing motorcycle ownership in Makurdi. The study estimates the influence of the various household factors identified and a model for predicting motorcycle ownership is developed for the study area. Data were collected via a questionnaire survey of 1412 households in the study area. The survey revealed that the number of motorcycles owned per household in the low density zone was 0.67 while that for the medium and high density zones was 0.62 and 0.79 respectively. The multinomial logit model developed predicted that 67% of households owned motorcycles as compared to 71% observed from survey data. Severity applications of the model to test the effects of changing economic situations on motorcycle ownership showed that residents of the study area are more disposed to owning motorcycles in periods of recession than periods of economic boom. The study gives an understanding of motorcycle growth pattern and ownership characteristics in the study area and will therefore serve as a relevant input for planning, regulation and control of motorcycle activities in the study area.
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