Forecasting Trip Generation For High Density Residential Zones of Akure, Nigeria: Comparability of Artificial Neural Network And Regression Models
Abstract
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.
References
Owolabi, A. O. (2009). Paratransit Modal Choice in Akure, Nigeria-Applications of Behavioral Models. ITE Journal, 54-58.
Ibeh, G. F., Agbo, G. A., Rabia , S., & Chikwenze, A. R. (2012a). Comparison of empirical and artificial neural network models for the correlation of monthly average global solar radiation with sunshine hours in Minna, Niger State, Nigeria. International Journal of Physical Sciences, 7(8), 1162-1165.
https://doi.org/10.5897/IJPS11.1750
Ibeh, G. F., Agbo, G. A., Oboma, D. N., Ekpe, J. E., & Odoh, S. (2012b). Comparison of Angstrom-Prescott, Multiple Regression and. Global Journal of Computer Science and Technology, 12(11), 6-12.
Ibeh, G. F., Agbo, G. A., Agbo, P. E., & Ona. (2012c). Comparison of Artificial Neural Network (ANN) and Angstrom-Prescott models in correlation between sunshine hours and global solar radiation of Uyo city, Nigeria. Archives of Applied Science Research, 4(3), 1213-1219.
Ojo, A. K., & Adeyemo, A. B. (2013). A Comparison Of The Predictive Capabilities Of Artificial Neural Networks And Regression Models For Knowledge Discovery. Computing, Information Systems, Development Informatics & Allied Research Journal, 4(2), 15-22.
Isaac , A. S., Adetiba , E., Ishioma , A. O., & Felly-Njoku, F. C. (2017). A Comparative Study of Regression Analysis and Artificial Neural Network Methods for Medium-Term Load Forecasting. Indian Journal of Science and Technology, 10(10), 1-7.
https://doi.org/10.17485/ijst/2017/v10i10/86243
Okwu, M. O. (2017). Transshipment model for multi-echelon system. (Doctoral Thesis). Federal University of Technology Owerri, Nigeria.
Okwu, M. O., Oreko, B. U., Okiy, S., Uzor, A. C., & Oguoma, O. (2018). Artificial Neural Network Model For Cost Optimization In A Dual-Source Multi-Destination Outbound System. Cogent Engineering, 1-13.
https://doi.org/10.1080/23311916.2018.1447774
Isenahd, G. M., & Olubusoye, O. E. (2014). Forecasting Nigerian stock market returns using ARIMA and artificial neural network models. CBN Journal of Applied Statistics, 5(2), 25-48.
Deme, C. A. (2016). A Generalized Regression Neural Network Model for Path Loss Prediction at 900 MHz for Jos City, Nigeria. American Journal of Engineering Research (AJER), 5(6), 1-7.
Agbo , G. A., Ibeh , G. F., Onah , D. U., Umahi , A. E., Nnaji, E., & Ugwuonah, F. C. (2013). Application of Neural Network in Atmos-pheric Refractivity Profile at Makurdi. International Conference on Information, Business and Education Technology (ICIBIT 2013), 140-142.
https://doi.org/10.2991/icibet.2013.107
Farinde, D. A. (2013). A Statistical Prediction of Likely Distress in Nigeria Banking Sector Using a Neural Network Approach. International Journal of Mechanical and Industrial Engineering, 7(10), 2721-2725.
Pwasong , A., & Nimyel, C. (2015). Application of Regression and Neural Network Models in Computing Forecasts for Crude Oil Productions. IOSR Journal of Mathematics (IOSR-JM), 11(6), 23-36.
Yahaya , H. U., Nasiru , M. O., & Ebgejiogu , O. N. (2017). Insolvency Prediction Model of Some Selected Nigerian Banks. International Journal of Statistics and Applications, 7(1), 1-11.
Ogwueleka, T. C., & Ogwueleka, F. N. (2010). Modelling Energy Content Of Municipal Solid Waste Using Artificial Neural Network. Iran. J. Environ. Health. Sci. Eng., 7(3), 259-266.
Jaiyeola , M. O., Oyamakin, S. O., Akinyemi, J. O., Adebowale, S. A., Chukwu, A. U., & Yusuf, O. B. (2016). Assessing Infant Mortality in Nigeria Using Artificial Neural Network and Logistic Regression Models. British Journal of Mathematics & Computer Science, 19(5), 1-14.
https://doi.org/10.9734/BJMCS/2016/28870
Ogunbodede, E. F., & Ale, A. S. (2015). The Regression Model In The Forecast Of Travel Demand In Akure, Nigeria. Analele Universităţii din Oradea, Seria Geografie (2), 186-194.
Okoko, E., & Fasakin, J. O. (2007). Trip Generation Modelling in Varying Residential Density Zones an Empirical Analysis for Akure, Nigeria. The Social Sciences, 2(1), 13-19.
Aworemi, J. R., & Ajayi, J. O. (2016). Factors Influencing Inter-City Trip Generation In Ifo Local Government Area Of Ogun State, Nigeria. International Journal of Latest Research in Science and Technology, 5(3), 22-26.
Osula , D. O. (1991). Development of Trip Generation Models for Land Uses in Nigeria. ITE Journal, 28-31.
Solanke, M. O. (2015). Socio-economic Characteristics of Urban Residents and Intra-urban Trip Generation: An Illustration from Abeokuta, Ogun State, Nigeria. Ethiopian Journal of Environmental studies and Management, 8(5), 593-605.
https://doi.org/10.4314/ejesm.v8i5.12
Oyedepo , O. J., & Makinde , O. O. (2010). Inter-Trip Characteristics Model for Ado-Ekiti Township, Southwest Nigeria., African Research Review. 4(2), 1-14.
https://doi.org/10.4314/afrrev.v4i2.58284
Ogwueleka, F. N., Misra, S., Ogwueleka, T. C., & Fernandez-Sanz, L. (2014). An Artificial Neural Network Model for Road Accident Prediction: A Case Study of a Developing Country. Acta Polytechnica Hungarica, 11(5), 177-197.
https://doi.org/10.12700/APH.11.05.2014.05.11
Behera, L. (n.d.). Neural Networks: Radial Basis Function Networks. Kanpur: Department of Electrical Engineering Indian Institute of Technology, Kanpur.
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