Conceptualizing How Agencies Could Leverage Weather-Related Connected Vehicle Application to Enhance Winter Road Services
Bibliographic record
Abstract
Winter inclement weather negatively influences the safety, mobility, economy, and user experience of roadway transportation systems. Ice and snowfall conditions result in more accidents and casualties and reduce the travel speed and roadway capacity because of decreased friction and visibility. Precise and timely road weather information is necessary for road maintenance decisions and high level-of-service trips of road users. In this context, connected vehicle (CV) technologies hold great promise in addressing the various influences of winter weather on the safety and mobility of road users. This work started from a nationwide survey of US and Canadian road maintenance departments to evaluate whether and how CV technologies are perceived by the practitioners for their potential in improving winter roadway safety and mobility. All respondents to the survey thought positively of the potential of CV application in improving winter road services, even though some expressed concerns over whether the system would perform well in poor weather, how to address risks associated with vehicle and system security, and the probability of increased driver distraction. This work presents a concept of operations, including the potential application and operational scenarios of CV technologies for agencies to improve winter road services. For instance, agencies may leverage the CV/mobile collection capabilities to provide customized and route-specific (disaggregated) road weather data to support more proactive and resource-efficient maintenance strategies and tactics and provide road users with more reliable, timely, and more localized travel alerts and advisories.
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 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".