Article Sidebar
Published:
Jun 22, 2026Keywords:
Article Details
In accordance with the Copyright Act of 1976, which became effective January 1, 1978, the following statement signed by each author must accompany the manuscript submitted: "I, the undersigned author, transfer all copyright ownership of the manuscript referenced above to the Nigerian Journal of Technological Development, in the event the work is published. I warrant that the article is original, does not infringe upon any copyright or other proprietary right of any third party, is not under consideration by another journal, and has not been published previously. I have reviewed and approve the submitted version of the manuscript and agree to its publication in the Nigerian Journal of Technological Development." A copyright transfer form may be downloaded from the NJTD Website (http://njtd.com.ng/index.php/njtd). Author(s) will be consulted, whenever possible, regarding republication of material. All authors must have access to the data presented and the authors and sponsor (if applicable) must agree to share original data with the editor if requested.
Main Article Content
An XGBoost-shap framework for predicting traffic speed based on road geometry
Abstract
In Intelligent Transportation System (ITS), Road Traffic Speed Prediction (RTSP) is essential for effective road operation and safety. This study investigates the impact of highway geometric features on traffic operating speed of Idiroko Road, a critical transportation link in Nigeria with the border. Geometric features including radius of curvature, road gradient, pavement width, shoulder width, median width and the presence of intersections were determined on site, as well as the spot speed of different vehicle types using a stopwatch approach. The Extreme Gradient Boosting (XGBoost) model was used to predict traffic operating speed, and Shapley additive explanations (SHAP) were employed to interpret the correlation between variables. The findings reveal that the radius of curvature, road gradient, pavement width, shoulder widths and the presence of intersections along the road have a significant effect on traffic operating speeds. The XGBoost model demonstrated high accuracy and efficiency with 98.5% and 97.9% coefficient of determination (R2) in both testing and training datasets. This study emphasizes the effectiveness of XGBoost in predicting traffic operating speed. The findings can be implemented in urban road design, infrastructural planning and traffic management ultimately contributing to safer and more efficient road networks.
