Microscopic Analysis of Traffic Flow in Inclement Weather

Rakha, Hesham; Krechmer, Daniel; Cordahi, Gustave; Zohdy, Ismail; Sadek, Shereef; Arafeh, Mazen · 2009 · ROSA P / United States. Dept. of Transportation. Research and Innovative Technology Administration

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Summary

This study addresses the gap in understanding how inclement weather affects microscopic driver behavior, specifically longitudinal vehicle motion, lane-changing, and gap acceptance. While macroscopic impacts of weather on traffic flow are well-documented, existing analysis tools lack robust linkages between specific weather conditions and individual driver actions due to limited empirical data. The research aims to quantify these impacts to improve traffic microsimulation models and support weather-responsive traffic management strategies. The methodology involved a comprehensive literature review and the evaluation of potential datasets. Two primary datasets were identified: the 100-Car Study and the Cooperative Intersection Collision Avoidance System (CICAS-V) project. Due to data quality issues, the 100-Car Study was unsuitable for analysis. Consequently, the CICAS-V dataset, which provided infrastructure-based kinematic data from five stop-controlled intersections, was used to analyze deceleration, acceleration, steady-state car-following, and gap acceptance behaviors. The researchers also reviewed commercial microsimulation software packages, including CORSIM, VISSIM, Paramics, AIMSUN, and INTEGRATION, to assess the feasibility of incorporating weather-related adjustment factors into their submodels. Key findings indicate that weather significantly alters driver behavior. For longitudinal motion, the study demonstrated that the Van Aerde and Gipps car-following models offer the highest flexibility for capturing driver behavior under varying conditions. Deceleration behavior was modeled by adjusting maximum deceleration levels to account for reduced roadway adhesion, while acceleration was best modeled using vehicle dynamics that incorporate roadway surface parameters. Gap acceptance analysis revealed that adverse weather, specifically rain intensity, significantly increases the critical gap values drivers require to enter traffic streams. However, no suitable data were available to analyze lane-changing behavior, preventing the development of weather-specific adjustments for this category. The study confirmed that weather adjustment factors can be incorporated into most commercial microsimulation packages for longitudinal motion and gap acceptance, though techniques vary by software. The significance of this work lies in providing a framework for integrating weather impacts into microscopic traffic simulation tools. By identifying specific parameters—such as free-flow speed, saturation flow rate, and critical gap sizes—that can be adjusted for weather conditions, the research enables more accurate modeling of traffic flow during inclement weather. The authors conclude that while current models can accommodate these adjustments, further empirical research is needed, particularly for lane-changing behavior and snowy conditions, to validate and refine these factors. This research supports the development of advanced traffic management strategies aimed at reducing weather-related congestion and improving safety.

Key finding

Adverse weather significantly impacts gap acceptance decisions, with critical gap values increasing as rain intensity and waiting time rise.

Methodology

dataset

Provenance

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

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• traffic density
• weather rain fog snow
• speed choice
• gap acceptance
• mental model of traffic

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: behavioral performance data
• Methodological Resource: dataset resource
• Theoretical Contribution: computational model