Decomposing Urban Traffic Flows: A Multistage Approach to Model Heavy Vehicle Movements in Greater Sydney

Abstract

This study presents an innovative multistage methodology for decomposing urban traffic flows into light vehicle (LV) and heavy vehicle (HV) categories, addressing a critical gap in transportation network analysis. Utilizing data from Greater Sydney’s road network, we develop a comprehensive approach comprising three main stages: origin–destination (OD) matrix estimation using RapidEx, quadratic programming optimization for HV/LV proportion estimation, and XGBoost regression for generalization. Our analysis examines associations between HV proportions and urban characteristics, including points of interest (POI), nightlight intensity, and zonal attributes. The XGBoost model achieves a test R 2 of 0.637, demonstrating strong predictive power for real-world applications. Through SHAP (SHapley Additive exPlanations) analysis, we uncover complex nonlinear relationships between nightlight intensity and HV proportions, with significant interaction effects between urban features. The model performs particularly well in predicting common urban HV proportion ranges (0.2–0.6), reflecting typical urban traffic compositions. These findings provide valuable insights for urban planning and policy development, especially in contexts where detailed vehicle classification data are limited.

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