Assessing the Impact of Vehicle Heterogeneity on Traffic Flow Efficiency on a Bridge Using Weigh-In-Motion Data

This study investigates how traffic heterogeneity affects both traffic-flow efficiency and bridge structural demand using one year of road-based Weigh-in-Motion (WIM) data collected on a 60 m two-span simply supported prestressed-concrete girder bridge in Nanjing, China. The objective was to develop an integrated framework for quantifying the joint influence of vehicle composition on traffic performance and bridge load effects, and to evaluate whether operational mitigation measures can improve both mobility and structural performance. The methodology combined data preprocessing, vehicle classification, macroscopic traffic-flow modelling, statistical analysis, influence-line-based structural load-effect estimation, machine-learning prediction, and simulation-based intervention evaluation. Traffic-flow relationships were analysed using the Greenshields’s, Greenberg’s, and Underwood’s models, while XGBoost and SHAP were applied to predict and interpret traffic and structural indicators derived from the processed WIM dataset. The results showed that increasing heavy-vehicle proportion reduced traffic efficiency by lowering flow and speed and increasing density, while simultaneously increasing predicted bending moment, shear force, and fatigue-related demand. Among the calibrated traffic-flow models, the Underwood’s model achieved the lowest RMSE, while the Greenshields’s model remained highly competitive and was retained because of its simpler and more interpretable formulation. The scenario analysis further indicated that both lane management and speed harmonization reduced structural demand relative to the baseline observed traffic condition, with lane management providing the greater overall benefit. The proposed framework is intended as an analytical and scenario-based decision-support methodology for evaluating bridge traffic-management strategies under heterogeneous traffic conditions.