Driver Performance and Behavior in Adverse Weather Conditions: An Investigation Using the SHRP2 Naturalistic Driving Study Data—Phase 2

Ahmed, Mohamed; Ghasemzadeh, Ali; Hammit, Britton E.; Khan, Md Nasim; Das, Anik; Ali, Elhashemi; Young, Rhonda; Eldeeb, Hesham · 2018 · ROSA P / Wyoming. Department of Transportation

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Summary

This study investigates how adverse weather conditions—specifically rain, snow, and fog—affect driver performance and behavior on freeways. While previous research often relied on aggregate traffic data, this work addresses a gap in understanding individual, trajectory-level driver adjustments to environmental changes. The research aims to provide insights that can inform the development of Weather Responsive Traffic Management (WRTM) strategies, particularly Variable Speed Limit (VSL) systems, by quantifying how drivers modify speed selection, car-following, and lane-keeping behaviors under varying weather conditions. The researchers utilized data from the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) and Roadway Information Database. To identify weather-related trips, they employed complementary methodologies, including monitoring vehicle wiper status, correlating trip locations with National Climatic Data Center weather stations, and analyzing weather-related crash reports. A semi-automated data reduction procedure was developed to process raw trip data into a format suitable for modeling. The study applied both parametric models, such as binary and ordinal logistic regression, and non-parametric data mining techniques, including Classification and Regression Trees (CART) and Multivariate Adaptive Regression Splines (MARS). These models analyzed the relationships between driver behaviors and factors such as roadway characteristics, traffic conditions, and driver demographics. The findings revealed significant variations in driver behavior across different weather conditions. Speed selection models indicated that drivers were most likely to reduce speed in snowy conditions, with odds 9.29 times higher than in clear conditions. Rain and fog also prompted speed reductions, with odds 1.55 and 1.29 times higher, respectively. Variable importance analysis identified weather conditions, traffic conditions, and posted speed limits as the three most critical factors influencing speed selection. Regarding lane-keeping, drivers exhibited worse performance in heavy rain compared to clear weather. The study also noted that non-parametric models like CART and MARS offered high prediction accuracy and interpretability, avoiding the "black box" issues common in other machine learning techniques. The significance of this research lies in its potential to enhance the logic behind VSL systems and other operational countermeasures. By providing detailed, trajectory-level data on driver responses to weather, the study supports the development of more realistic and effective safety strategies. The findings suggest that SHRP2 NDS data can effectively identify adverse weather trips and assess their impact on driver performance. This work lays the foundation for future phases aimed at validating speed selection models using Wyoming interstate data and developing weather-related microsimulation guidance. Ultimately, the study contributes to improving transportation safety and reliability by integrating human factors into weather-responsive traffic management systems.

Key finding

Drivers were 9.29 times more likely to reduce speed in snowy weather compared to clear conditions, and heavy rain significantly worsened lane-keeping performance.

Methodology

naturalistic

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

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

• weather rain fog snow
• speed choice
• naturalistic crash near crash
• traffic density
• speed distance perception

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: crash risk outcomes, observational prevalence
• Methodological Resource: dataset resource