Short-term lagged interactions between freight and passenger volumes in urban traffic: inter- and intra-modal effects with explainable machine learning

E. Amirnazmiafshar, D.P. Song, B. Kenny, J.M. Wu, B. Kulcsár, Y.Z. Liu and C. Olaverri-Monreal

Transportation Research Part A: Policy and Practice, 2026, vol. 206, issue C

Abstract: Urban transport systems face increasing complexity as freight and passenger flows compete for limited road capacity. While multimodal forecasting methods have progressed, short-term interactions between vehicle classes remain underexplored, particularly in real-world operational settings. This study addresses that gap by examining whether recent freight or passenger volumes are significantly associated with current traffic conditions across modes. Using 6,003 hourly records from Liverpool, UK, we develop an interpretable machine learning framework combining K-means clustering, XGBoost classification, and the DALEX explainability toolkit. Results show that one-hour lagged freight volume significantly improves the classification of current passenger traffic states, while the reverse effect is limited. Global feature importance and local interpretability analyses consistently identify freight volume as the most influential predictor. Partial dependence plots (PDPs) reveal a nonlinear inflexion point, where freight volumes exceeding roughly 500 vehicles per hour in this Liverpool case study are associated with reduced passenger flow. McNemar’s test confirms a statistically significant improvement, and robustness checks, including alternative lag structures, interaction terms, and reciprocal models, reinforce the stability of this finding. These insights offer practical value for short-term forecasting, corridor-level coordination, and longer-term multimodal planning. The observed directional asymmetry, wherein freight volumes more reliably predict passenger conditions than the reverse, highlights the potential benefits of incorporating freight data into real-time traffic management systems. More broadly, the study demonstrates how interpretable machine learning can uncover cross-modal dependencies and support the development of more integrated, responsive, and equitable urban mobility systems.

Keywords: Freight-passenger interaction; Urban traffic management; Multimodal transport planning; Short-term demand forecasting; Lagged traffic volumes; Explainable machine learning (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1016/j.tra.2026.104927

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