Title: Assessment of the Impact of Transportation on the Hospitality Industry in Akure city using Machine Learning Algorithms
Authors: Okougha Christopher, Aderinlewo Olufikayo.O
Volume: 8
Issue: 10
Pages: 142-153
Publication Date: 2024/10/28
Abstract:
This study aims at assessing the impact of transportation on Akure City's hospitality sector using machine learning algorithms. A total of 270 structured questionnaires were applied across nine hospitality and tourism hubs in Akure. Different performance indices were used to assess the impact of transportation on the hospitality industry in Akure which uncovered distinct gender and age distribution patterns across the locations. The different modes of transportation, the respondent's perception of convenience, accessibility of respondents, safety and security, affordability, transportation hub proximity, transportation reservation and transportation availability further underscore the urgency for infrastructure enhancements, in line with the Random forest model's prediction of hospitality ratings in Akure. Random Forest's higher accuracy (89%) compared to Kernel Nearest Neighbour (77%) signifies its superior predictive prowess, as indicated by the evaluation metrics of Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2). Overall, the study significantly enriches the understanding of transportation's intricate role in shaping Akure's hospitality landscape, providing actionable insights for industry enhancement and sustainability.