Bibliographic record
Abstract
Several past studies have attempted to quantify the impact of winter weather conditions on highway mobility in terms of traffic volume, speed, and capacity. While consistent in their general findings, these studies have shown considerably different results in terms of effect size and contributing factors. More importantly, most of these studies have not attempted to model the effects of winter maintenance operations on mobility or isolate these effects from those due to snowstorm characteristics, rendering their results and the proposed methods of limited use for estimating the benefits of maintenance activities. This research attempts to address this gap through a statistical analysis of a data set that is unique in terms of spatial and temporal coverage and data completeness. The data set includes both event based and hourly observations of road weather and surface conditions, maintenance operations, traffic volume and speed, as well as several other measures, from 21 highway sections across the province of Ontario. \nEvent based information is available for six winter seasons (2000 to 2006) at 19 of the sites. For this event based data a matched pair technique was employed to determine the changes in traffic volumes and speeds under matched conditions with and without snow events. A regression analysis was subsequently performed to relate the changes in traffic volume and speed over an event to changes in various contributing factors such as highway type, snow event characteristics and road surface conditions. A case study was conducted to illustrate the application of the developed models for quantifying the mobility impact of road surface condition and the mobility benefit of winter maintenance operations. \nComplete hourly records were available for all 21 sites for three winter seasons. This was used to perform the evaluation on an hourly basis. A matching technique is employed to assign hour-by-hour median speeds observed under typical weather and road surface conditions to each hour of a snowstorm event. A regression analysis is subsequently performed to relate changes from average hourly speed to various contributing factors such as highway type, weather conditions and maintenance operations. Effects of maintenance operations are represented by an intermediate variable called road surface condition index (RSI). A case study is conducted to illustrate the application of the developed models for quantifying the mobility impact of winter snowstorms and the mobility benefit of maintenance operations. \nThe models developed in these analyses confirmed the relationships between weather variables and traffic volume and speed described in the literature. In addition a strong association between road surface condition and traffic volumes and speed was identified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".