Effects of adverse winter weather conditions on highway traffic and driver behavior
Bibliographic record
Abstract
This research looks into the impact of adverse winter weather conditions on highway driver behaviors using microscopic data from loop detectors and video cameras (e.g., hourly average speed, trajectories, lane changes, time-to-collisions measures). This thesis is composed of two main sections in addition to the introductory section: i) direct and lagged effects of adverse weather on hourly speeds and volumes; and ii) direct effect of adverse weather on driver behaviors (microscopic) measured at the vehicle level using video data. The first part of the thesis presents a review of literature related to past research on the topic. The second part investigates the direct and lagged effects of adverse winter weather conditions on the operating speed in a number of highway segments in Ontario using a time-series approach. This is complemented by the analysis of hourly traffic volumes in the region of Montreal, Canada, using data from magnetic loop detectors as well. In speed modeling, 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 (e.g., temperature, snow, wind speed), and surface conditions was used. A few previous studies have examined the effect of snowstorms on traffic parameters; however, little research has been done regarding the spillover effects (lagged effects) that adverse weather conditions may have on travel demand and traffic patterns. Extreme events or weather conditions might have a strong effect on traffic conditions not only during the events, but also before and after the events. In this study, time-series regression techniques―in particular, Autoregressive Integrated Moving Average (ARIMA) models―were used to model the highway operating speed. These methods are able to consider the serial correlation among error terms. The results indicate that snowstorms have a statistically significant effect on the 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. Similarly, the effects of different weather variables including their lagged effects were analyzed using hourly traffic volume data. Despite the fact that information of the road surface condition was not available, this analysis is in accordance with previous finding, showing the utility of ARIMA approaches in modeling the highway volume as well. The results of this study can be applied in quantifying the mobility effect of winter weather and benefits of winter road maintenance. In recent years, driver behavior analysis using microscopic (vehicle level) data is a topic that is attracting more attention in road safety analysis. This popularity has brought about research in many different innovative techniques and microscopic measures used to quantify and analyze driver behavior. In the second part of this thesis, it demonstrates a method of analyzing driver behavior using video data approach. This thesis elucidates both a manual and an automated, computer-based method to analyze driver behavior. It also uses the computer-based method to evaluate the effect of adverse winter weather conditions on the driver behavior of highway users. Both the manual and the automated approaches have been used with 15 video recordings obtained from three different locations on the Don Valley Parkway (DVP) in Toronto, Ontario. The results demonstrate the effectiveness of the automated method in analyzing driver behavior, as well as in evaluating the impact of adverse winter weather conditions on driver behavior.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".