On the practical utility of a continuum traffic flow model on curvy highways in adverse weather conditions

Imran, Waheed; Khan, Zawar H.; Khan, Daud; Ghani, Usman; Bashir, Tahseen · 2024 · Crossref

DOI: 10.1016/j.trip.2024.101108

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Summary

This study investigates the practical utility of the Non-Homogeneous Stimulus-Response Model (NHSRM), a macroscopic continuum traffic flow model, for forecasting traffic dynamics on curvy highways under adverse weather conditions. While macroscopic models are valuable for traffic management, their application in complex scenarios involving road geometry and weather variability remains underexplored. The authors aim to assess how weather impacts travel time, velocity, and density evolution, specifically focusing on upstream congestion and velocity breakdowns. The research integrates the NHSRM with design parameters from the American Association of State Highway and Transportation Officials (AASHTO) guidelines to create a robust methodology for weather-adaptive traffic management. The methodology employs the NHSRM, which extends the Payne-Whitham model by incorporating driver response and road stimulus to characterize traffic more realistically. The study simulates traffic on a 10,000-meter highway featuring sharp curves with radii of 120 meters and 500 meters. Weather conditions—dry, light, moderate, and heavy rainfall—are modeled by adjusting the coefficient of friction ($\mu$) and free-flow velocity ($v_f$) based on AASHTO standards. The safe velocity on curves is calculated using the formula $v_s = \sqrt{\mu R g}$, which serves as the maximum achievable speed in the model. The NHSRM is discretized using the First ORder CEntered (FORCE) technique with a spatial step of 200 meters and a time step of 1 second, ensuring stability via Courant-Friedrichs-Lewy conditions. Simulations run for 3600 seconds to observe spatiotemporal traffic evolution. Results indicate that adverse weather significantly degrades traffic performance, particularly on curved sections. Heavy rainfall causes substantial velocity breakdowns that propagate upstream, leading to significant congestion formation. The study quantifies capacity reductions based on curve radius and weather severity. For a 120-meter radius curve, traffic capacity drops from 707 veh/h in dry conditions to 258 veh/h in heavy rainfall, representing a 63.5% reduction. For a 500-meter radius curve, capacity decreases from 963 veh/h to 571 veh/h under heavy rainfall, a 40.7% reduction. Travel times for highway segments elevate sharply during heavy rainfall. The findings highlight that the choice of maximum velocity is critical for accurate predictions, as it must account for both road geometry and weather-induced friction changes. The significance of this work lies in demonstrating the NHSRM’s effectiveness in capturing complex traffic dynamics influenced by weather and geometry. The integrated methodology provides a practical tool for predicting jam formation and travel time increases, offering insights for real-world traffic management. By coupling continuum models with established engineering guidelines, the study offers a framework for weather-adaptive control strategies. This approach allows for more precise traffic forecasting and infrastructure planning, addressing limitations of previous studies that often ignored the combined effects of curvature and weather intensity on traffic flow.

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• traffic density