The Effect of Weather on Travel Speed from Bluetooth Sensor Data on a Cold-City Urban Arterial
Bibliographic record
Abstract
While traffic flow (volume) is associated with variations in travel times or speeds, studies have shown that weather can play a significant role especially in the winter context of North American cold cities. This study investigated the effect of weather and temporal variables on travel speeds obtained from Bluetooth sensor data collected during winter time. This research is based on data from 2013-2015 collected from permanent sensors installed along one of the main urban arterials in Montreal, Canada. The hourly weather variables included were snow, rain, temperature, visibility, and wind speed. The effects were modeled at an hourly level using three linear regression models considering the whole dataset, only winter period data and only out of winter period data. The results confirmed the major importance of snow as a hindrance to travel speed. The effect of rising temperature was expectedly found to have slightly increased travel speeds as it increased. The impact of visibility and wind speed were found to have positive and negative benefits respectively as they increased although, their effect was not as pronounced as snow. More out of winter data is needed to clarify the effects of rain on travel speed. In regards to the temporal variables, greater speed reductions were unexpectedly observed in the fall than in the winter. It is interesting to note that the hourly count of travel times provided much of the explanatory power to the models suggesting that Bluetooth data may be a good estimator of traffic volume proportions.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".