Speed Selection During Winter Road Conditions

Thapa, Sandeep; Young, Rhonda · 2019 · ROSA P / Mountain-Plains Consortium

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Summary

This study investigates the impact of adverse winter weather conditions on driver speed selection and microscopic traffic behaviors, specifically headways and gaps, on rural interstate corridors. Motivated by the significant safety risks and economic costs associated with winter storms—including increased crash rates, road closures, and traffic delays—the research aims to address a knowledge gap regarding the relationship between specific weather parameters (precipitation, visibility, surface temperature, humidity, and wind) and driver behavior. The findings are intended to support Weather Responsive Traffic Management Strategies (WRTMS) and provide guidelines for calibrating microscopic traffic simulation models to reflect non-ideal weather conditions. The researchers focused on Interstate 80 corridors in Wyoming, selected for their severe winter conditions and uniform geometry, which minimizes the confounding effects of roadway alignment. Data were collected from three specific corridors: Elk Mountain, Laramie–Cheyenne, and Green River–Rock Springs. The study utilized paired datasets combining traffic data from Wavetronix speed sensors and weather data from Road Weather Information Systems (RWIS) during twelve distinct storm events. The methodology involved descriptive statistical analysis of vehicle speeds and headways, followed by statistical modeling using an Ordered Probit Model for speed selection behavior and a Log-Logistic Distribution Model for vehicle headways. Additionally, the study employed the VISSIM microsimulation tool to develop base and adjusted models, calibrating simulation parameters against observed data to assess sensitivity to weather-induced behavioral changes. The analysis revealed that adverse weather conditions significantly alter traffic operations. Descriptive statistics showed notable reductions in average speeds and increases in speed variability during storm events compared to ideal conditions. The Ordered Probit Model identified specific weather variables influencing speed selection, while the Log-Logistic Model demonstrated shifts in headway distributions, indicating that drivers increase spacing during adverse conditions. The microsimulation results confirmed that standard VISSIM parameters, such as those in the Wiedemann 99 car-following model, require adjustment to accurately replicate observed traffic flow during storms. Sensitivity analysis highlighted which simulation parameters are most critical for capturing weather-responsive behaviors, providing a framework for calibrating models to reflect reduced visibility and pavement friction. The significance of this research lies in its contribution to the development of more accurate, weather-responsive traffic simulation tools. By establishing quantitative relationships between weather parameters and microscopic driver behaviors, the study provides transportation agencies with evidence-based guidelines for calibrating simulation models. This enables better prediction of traffic impacts during winter storms and supports the implementation of proactive mitigation strategies, such as Variable Speed Limit (VSL) systems. Ultimately, the findings aim to improve traffic safety and mobility by allowing agencies to make informed decisions based on realistic simulations of adverse weather conditions, rather than relying on ideal-condition assumptions.

Key finding

Adverse winter weather conditions significantly reduce average vehicle speeds and increase speed variability, with heavy snowfall causing speed reductions of up to 50 km/h compared to ideal conditions.

Methodology

field_study

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

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

Topics

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• speed choice
• weather rain fog snow
• speed distance perception