Investigating Combined Impact of Adverse Road-Weather Conditions and Heavy Vehicles on Saturation Headway
Bibliographic record
Abstract
Adverse road-weather (RW) conditions make driving behavior more conservative and the headway during saturated conditions longer, leading to a significant reduction in the capacity of signalized intersections. Past studies indicate that the degree of the influence of adverse RW conditions on intersection performance changes by heavy vehicle (HV) ratio in traffic flow. However, little is known about the combined impacts of adverse RW conditions and HV ratio on saturation headway and how they can be considered in the planning of signalized intersections in areas with long winter. To fill this research gap, in this study the saturation headway data for over 2,000 signal cycles were extracted from video recordings at two signalized intersections in Winnipeg, Canada. The combined impacts of adverse RW conditions and HV ratios are statistically investigated in the paper with due focus given to saturation headway distributions and models. To account for differences in vehicle type, passenger car equivalent and headway distributions are evaluated under different RW conditions. The analysis findings suggest that the saturation headways increased by up to 38.7% as a result of adverse RW conditions. The multiple regression analyses incorporating HV ratios quantify the relationship between saturation headway and various sets of explanatory variables covering adverse RW conditions and roadway geometric factors. The model estimation results reveal that HVs are less sensitive to RW conditions than passenger vehicles. Overall, the study findings will help in designing signalized intersections under adverse RW conditions with various HV ratios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".