An XGBoost-SHAP Framework for Predicting Traffic Speed Based on Road Geometry
DOI:
https://doi.org/10.63746/njtd.v22i3.3514Keywords:
Highway Geometric, Traffic Operating Speed, Speed Prediction, XGBoost Model, SHAPAbstract
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.
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