Investigating the impacts of adverse road-weather conditions on saturation headway
Bibliographic record
Abstract
Adverse road-weather (RW) conditions may deteriorate traffic operations and safety at intersections due to reduced capacity, frequent collisions, and increased traffic emissions. Weather responsive traffic management (WRTM) can potentially lessen the negative impact of adverse RW conditions. Yet, saturation flow rate (SFR) is a major input for any WRTM that is affected by RW conditions and traffic composition. Few studies investigated the combined effect of adverse RW conditions and heavy vehicle (HV) ratios on the operation of signalized intersections. Additionally, the classic method for measuring SFR estimates the mean of observed flow rates starting from a critical vehicle (CV), i.e., 5th vehicle in the queue per cycle which has several shortcomings including: (1) not considering the probabilistic nature of SFR, (2) ignoring the unsaturated vehicles in the queue i.e., vehicles preceding the CV, and (3) using a particular CV threshold regardless of RW conditions. This thesis investigates saturation headway variations considering RW conditions and HV ratios using the data collected at two busy signalized intersections in Winnipeg, Canada. Regression analysis was used to analyze saturation headways and passenger car equivalent (PCE) factors considering RW conditions. Further, a novel methodology is proposed to model SFR variations using survival analysis that was implemented to explore the stochastic characteristics of SFR considering different CV settings and RW conditions. The findings confirmed that adverse RW conditions can increase the saturation headway significantly e.g., by up to 38.7% for snowy RW conditions. Estimated PCE values under each RW classification and the regression models implied that HVs are less susceptible to adverse RW conditions in terms of their impact on saturation headway. The results from the proposed survival analysis method indicated that adverse RW conditions tend to move the CV position forward to the head of the queue. The findings of this thesis provide a practical method for estimation of SFR at signalized intersections under different adverse RW conditions and contribute to establishment of WRTM strategies to improve the safety and operation of signalized intersections in winter.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".