Volume List  / Volume 9 (2)



DOI: 10.7708/ijtte.2019.9(2).03

9 / 2 / 163 - 176 Pages


ETU Japheth Eromietse - Department of Civil Engineering, Federal University of Technology, Akure, Ondo State, Nigeria -

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


Efficacy of using Radial Basis Function Neural Network (RBFNN) and Regression Models (MLR) to estimate trip generation rates in Akure, Nigeria was compared. This sterns from a desire to test more novel modelling techniques besides regression which has hitherto been used in the study area. Data for the study were collected through household questionnaire interview survey in the study area between October 2017 and January 2018. SPSS 22 was used in carrying out data analysis. Correlation analysis showed that Number of household members, (NHM), Number of employed household members, (NEHM), Number of students in household (NSH), Number of Household members with age greater than 12years, (NHM12) and Number of Driver’s license holders in the household, (NDLH) were the household variables having significant influence on home based trips generation rates. The models were compared and validated using their R2 values and Relative Error (RE). Modelling results showed that RBFNN displayed higher accuracy with R2 value of 0.947 and RE of 0.391 as compared to MLR with R2 value of 0.589 and RE of 0.875. The study was able to uphold the capability of artificial neural networks to produce better results in travel demand forecasting areas than regression techniques.

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