Microscopic Analysis of Traffic Flow in Inclement Weather: Part 2
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Summary
This report documents the second phase of a Federal Highway Administration study analyzing the microscopic impacts of adverse weather on traffic flow. While previous phases examined macroscopic aggregate effects, this research focuses on individual driver behaviors—specifically car-following, gap acceptance, and lane changing—to support weather-responsive traffic management strategies. The study comprises three distinct efforts: quantifying the impact of icy conditions on car-following behavior, investigating how precipitation and surface conditions affect left-turn gap acceptance, and developing methodologies to incorporate weather adjustment factors into microsimulation software. The first effort utilized field-measured car-following data from experiments conducted at a test track in Hokkaido, Japan, under both dry and icy conditions. Researchers calibrated the Van Aerde car-following model using a custom bi-level heuristic algorithm to estimate driver-specific parameters, including free-flow speed, speed-at-capacity, capacity, jam density, and perception-reaction time. Statistical analysis revealed that individual driver differences significantly influence car-following behavior on dry roads. Under icy conditions, the impact of roadway surface conditions overshadowed individual driver variations, significantly altering capacity and free-flow speed parameters. The second effort analyzed left-turn gap acceptance behavior using data collected over six months at a signalized intersection in Blacksburg, Virginia. The study categorized weather into six conditions based on precipitation (rain or snow) and surface state (dry, wet, snowy, or icy). Logit models were fitted to over 11,000 observations to determine critical gap sizes. Results indicated that drivers adopt more conservative behaviors during snow precipitation compared to rain. Regarding surface conditions, drivers required larger gaps on wet surfaces than on snowy or icy surfaces, with the smallest gaps observed on dry roads. Additionally, required gaps increased as the distance to clear the conflict point grew. The third effort developed methodologies to integrate these weather impacts into microsimulation tools, specifically VISSIM and INTEGRATION. Due to CORSIM’s limited capability to handle weather adjustments, INTEGRATION was selected alongside VISSIM for calibration. The researchers applied weather-related adjustment factors to car-following, lane-changing, and gap-acceptance parameters. Simulation results demonstrated that rain and snow conditions significantly affected traffic flow metrics in INTEGRATION runs. In contrast, weather impacts were not statistically significant in VISSIM simulations, suggesting a need for further validation and refinement of VISSIM’s weather modeling capabilities. These findings provide empirical evidence for calibrating microscopic models to better predict traffic performance under inclement weather.
Key finding
Drivers require larger gaps for left-turn gap acceptance during wet and snowy conditions compared to dry conditions, and weather adjustment factors significantly impacted traffic flow in INTEGRATION simulations but not in VISSIM.
Methodology
mixed_methods
Sample size: 11000
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).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Theoretical Contribution: computational model