Road Weather Information Systems at the Ministry of Transportation, Ontario
Bibliographic record
Abstract
This paper describes how the Ontario Ministry of Transportation (MTO) has implemented a comprehensive network of Road Weather Information System (RWIS) stations in order to provide highway maintenance staff with the most up-to-date information so that they can proactively make the most appropriate winter maintenance decisions. The system provides real-time atmospheric and pavement condition data, through NTCIP compliant data transmission telematics, to central servers for interpretation, manipulation and use in forecasting atmospheric and pavement conditions. Data and forecasts are then displayed on a secured web site, in tabular and graphic formats, for easy review and interpretation. Hazardous highway condition warnings are provided to flag the need for prompt attention. Data is automatically archived for future use. Since the system is web-based, it can be linked to other web-based resources like Automated Vehicle Location systems, and the road condition reporting systems currently in place. The capabilities of the system display can be easily packaged to assist maintenance managers in maximizing the effectiveness of winter maintenance operations. The RWIS system is part of a suite of tools that the maintenance manager uses to make the right decisions at the right time on winter road maintenance operations. RWIS is essential in implementing emerging operational innovations such as Direct Liquid Application (liquid anti-icing) and Fixed Automated Spray Technology. For agencies that have contracted maintenance services, it can provide quality assurance data to indicate whether the roads have been serviced. Ontario has been involved in RWIS development since the mid 1990s, and has worked closely with other provinces and Environment Canada to develop standards and implement appropriate forecasting tools appropriate for use in Canada. The MTO network consists of 113 stations comprised of 5 equipment suppliers, all providing data in a legible format for effective provision of forecasting services. The Ontario RWIS model facilitates the sharing of data among road authorities that have stations, to expand the information available for making operational decisions. In addition, MTO has structured a plan to share its station data with municipalities that do not have stations so that all road agencies may be able to improve their winter maintenance operations for the benefit of Ontario drivers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.023 |
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".