Forecasting Trip Generation For High Density Residential Zones of Akure, Nigeria: Comparability of Artificial Neural Network And Regression Models
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.
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.
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.
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.
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.
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.
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.
Oyedepo , O. J., & Makinde , O. O. (2010). Inter-Trip Characteristics Model for Ado-Ekiti Township, Southwest Nigeria., African Research Review. 4(2), 1-14.
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.
Behera, L. (n.d.). Neural Networks: Radial Basis Function Networks. Kanpur: Department of Electrical Engineering Indian Institute of Technology, Kanpur.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Upon acceptance of an article, the corresponding author on behalf of all authors will be asked to complete and upload the Copyright Transfer Form (refer to Copyright Issues for more information on this) alongside the electronic proof file.
Copyright Transfer Statement for Journal
1) In signing this statement, the author(s) grant UNIMAS Publisher an exclusive license to publish their original research papers. The author(s) also grant UNIMAS Publisher permission to reproduce, recreate, translate, extract or summarize, and to distribute and display in any forms, formats, and media. The author(s) can reuse their papers in their future printed work without first requiring permission from UNIMAS Publisher, provided that the author(s) acknowledge and refer the publication in the Journal.
2) For open-access articles, the author(s) agree that their articles published under UNIMAS Publisher are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0) which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original work of the author(s) is properly cited.
3) For subscription articles, the author(s) agree that UNIMAS Publisher holds the copyright or an exclusive license to publish. Readers or users may view, download, print, and copy the content, for academic purposes, subject to the following conditions of use: (a) any reuse of materials is subjected to permission from UNIMAS Publisher; (b) archived materials may only be used for academic research; (c) archived materials may not be used for commercial purposes, which include but not limited to monetary compensation by means of sale, resale, license, transfer of copyright, loan, etc.; and (d) archived materials may not be re-published in any part, either in print or online.
4) The author(s) are responsible to ensure their submitted work is original and does not infringe any existing copyright, trademark, patent, statutory right, or propriety right of others. The corresponding author has obtained permission from all co-authors prior to submission to the journal. Upon submission of the manuscript, the author(s) agree that no similar work has been or will be submitted or published elsewhere in any language. If the submitted manuscript includes materials from others, the authors have obtained permission from the copyright owners.
5) In signing this statement, the author(s) declare that the researches which they have conducted comply with the current laws of the respective country and UNIMAS Journal Publication Ethics Policy. Any experimentation or research involving humans or the use of animal samples must obtain approval from the Human or Animal Ethics Committee in their respective institutions. The author(s) agree and understand that UNIMAS Publisher is not responsible for any compensational claims or failure caused by the author(s) in fulfilling the above-mentioned requirements. The author(s) must accept the responsibility for releasing their materials upon request by Chief Editor or UNIMAS Publisher.
6) The author(s) should have participated sufficiently in the work and ensured the appropriateness of the content of the article. The author(s) should also agree that they have no commercial attachments (e.g. patent or license arrangement, equity interest, consultancies, etc.) that might pose any conflict of interest with the submitted manuscript. The author(s) also agree to make any relevant materials and data available upon request by the editor(s) or UNIMAS Publisher.