Empirical Studies on Traffic Flow in Inclement Weather

Hranac, Rob; Sterzin, Emily D.; Krechmer, Daniel; Rakha, Hesham; Farzaneh, Mohamadreza · 2006 · ROSA P / United States. Federal Highway Administration. Road Weather Management Program

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Summary

This study addresses the critical need to quantify the impacts of inclement weather on macroscopic traffic flow parameters, such as speed, capacity, and density. While severe weather events cause significant delays and safety issues, existing analysis tools lack robust linkages between specific weather conditions and traffic performance due to limitations in historical data quality and coverage. The research aimed to develop a better understanding of how precipitation and visibility affect traffic across different regions and facility types, providing a foundation for improved traffic management strategies like variable speed limits. The methodology combined a comprehensive literature review with empirical analysis of archived data. Researchers selected three metropolitan areas—Minneapolis-St. Paul, Baltimore, and Seattle—to capture a wide range of weather conditions, including significant rain and snow. They utilized macroscopic traffic data from loop detectors and weather data from Automated Surface Observing Systems (ASOS) and Automated Weather Observing Systems (AWOS). The study employed the Van Aerde nonlinear traffic stream model to calibrate speed-flow-density relationships. A customized heuristic tool, SPD_CAL, was developed to minimize the error between field observations and model estimates, allowing for the computation of key parameters: free-flow speed, speed-at-capacity, capacity, and jam density under varying precipitation intensities. The findings revealed that while jam density remained unaffected by weather, rain and snow significantly reduced free-flow speed, speed-at-capacity, and capacity. Capacity reductions ranged from 10% to 11% during rain and 12% to 20% during snow, with snow impacts being more severe than rain. Notably, capacity reductions appeared constant regardless of precipitation intensity, whereas speed reductions varied with intensity. Regional differences were evident; for instance, the Twin Cities experienced greater reductions in free-flow speed and speed-at-capacity during snow (19%) compared to Baltimore (5%), potentially due to heightened driver caution in snow-prone regions. Statistical models confirmed that these parameters are sensitive to both precipitation type and intensity. The significance of this work lies in its provision of empirical, data-driven weather adjustment factors for traffic modeling. By establishing clear relationships between weather variables and macroscopic traffic parameters, the study supports the development of more accurate predictive tools for transportation managers. The authors recommend future research to enhance macroscopic analysis through colocated sensor data, expand the study to include warm-weather cities and arterial facilities, and investigate microscopic driver behavior and human factors to further refine traffic management strategies during adverse weather conditions.

Key finding

Rain and snow significantly reduce traffic free-flow speed, speed-at-capacity, and capacity, while jam density remains unaffected, with reductions varying by precipitation intensity and regional location.

Methodology

dataset

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• weather rain fog snow
• traffic density
• speed choice

Information type

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• Methodological Resource: dataset resource