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

Emmanuel Okechukwu Nwafor; Folake Olubunmi Akintayo

Article Details

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

Authors:

Emmanuel Okechukwu Nwafor | Folake Olubunmi Akintayo

Publisher:

Gazi University

Main Topics:

Transportation Engineering

Journal:

Gazi University Journal of Science Part A: Engineering and Innovation Vol.12 Issue 1, pp.332-346

Document Type:

Article

Publication Date:

3/2025

Document Language:

English

Document ID (Org No.):

2001342766

pISSN:

2147-9542

Keywords:

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 models 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 models 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.

 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 models 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 models 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.

 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 models 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 models 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.

 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 models 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 models 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.

Article Details

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

Authors:

Emmanuel Okechukwu Nwafor | Folake Olubunmi Akintayo

Publisher:

Gazi University

Main Topics:

Transportation Engineering

Journal:

Gazi University Journal of Science Part A: Engineering and Innovation Vol.12 Issue 1, pp.332-346

Document Type:

Article

Publication Date:

3/2025

Document Language:

English

Document ID (Org No.):

2001342766

pISSN:

2147-9542

Decision Tree Algorithm

Trip Purpose

Intelligent Transport

Sustainable Transport

Issues

Article Details

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

Authors: Emmanuel Okechukwu Nwafor | Folake Olubunmi Akintayo

Publisher: Gazi University

Main Topics: Transportation Engineering

Journal: Gazi University Journal of Science Part A: Engineering and Innovation Vol.12 Issue 1, pp.332-346

Document Type: Article

Publication Date: 3/2025

Document Language: English

Document ID (Org No.): 2001342766

pISSN: 2147-9542

Decision Tree Algorithm

Trip Purpose

Intelligent Transport

Sustainable Transport

Issues

Authors:

Emmanuel Okechukwu Nwafor | Folake Olubunmi Akintayo

Publisher:

Gazi University

Main Topics:

Transportation Engineering

Journal:

Gazi University Journal of Science Part A: Engineering and Innovation Vol.12 Issue 1, pp.332-346

Document Type:

Article

Publication Date:

3/2025

Document Language:

English

Document ID (Org No.):

2001342766

pISSN:

2147-9542