Application of Decision Tree Algorithms for Predicting Trip Purposes in Makurdi, Nigeria

Abstract

Decision tree models are versatile and interpretable machine learning algorithms widely used for both classification and regression tasks in transportation planning. This research focuses on analysing the suitability of decision tree algorithms in predicting trip purposes in Makurdi, Nigeria. The methodology involves formalizing household demographic and trip information datasets obtained through an extensive survey process. Modelling and prediction were conducted using Python programming language, and evaluation metrics such as R-squared and Mean Absolute Error (MAE) were used to assess the model’s performance. The results indicate that the model performed well, achieving accuracies of 84% and 68% and low MAE values of 0.188 and 0.314 on training and validation data, respectively. These findings suggest the model's reliability for future predictions. The study concludes that the decision tree-based model provides actionable insights for urban planners, transportation engineers, and policymakers to make informed decisions for improving transportation planning and management in Makurdi, Nigeria.

Keywords

• Decision Tree Algorithm
• Trip Purpose
• Intelligent Transport
• Sustainable Transport

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