TRIP GENERATION MODELING OF ILORIN CITY, NIGERIA, USING GIS AND ARTIFICIAL NEURAL NETWORK

Abstract

Ilorin's traffic situation, while not as severe as larger cities like Lagos, Ibadan, and Port-Harcourt, is showing signs of bottlenecks and congestion. Travel demand modeling is important for effective transportation planning. This study develops Artificial Neural Network (ANN) trip generation (production and attraction) models using household and trip characteristics, population data, and maps of the base year (2022). The models had high accuracy values of 0.999873850524 and 0.999999999903 with low error values of 0.058 and 0.0000000419 for trip production and attraction respectively. The models were then used to foresee trip production and attraction for the horizon year (2032).