Direct and Lagged Effects of Adverse Winter Weather Conditions on Operating Speed in Urban and Rural Highways: A Time-Series Analysis
Bibliographic record
Abstract
This paper investigates the direct and lagged effects of adverse winter weather conditions on the operating speed of a number of highway segments in Ontario using a time-series approach. The effect of adverse weather was studied using data from multiple sites including both urban and rural highways, considering weekdays versus weekends separately. For this purpose, a large dataset containing hourly traffic data, weather variables (temperature, snow, wind speed, etc.), and surface conditions were used. Some previous studies have examined the effect of snowstorms on traffic parameters; however, little has been done regarding their spillover effects (lagged effects). Extreme events or weather conditions might have a strong effect on traffic conditions not only during these events, but also before and after such events. In this study, time-series regression techniques―in particular, Autoregressive Integrated Moving Average (ARIMA) models―were used to model highway operating speed. These methods are able to consider the serial correlation among error terms. From the results, it can be inferred that snow storms have a statistically significant effect on speed. The lagged effects are however offset by the time and intensity of winter maintenance operations during and after the event. The effect of weather also varies depending on the type of site (urban or rural) and day of the week. The results of this study can be applied in quantifying the mobility effect of winter weather and benefits of winter road maintenance.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".