Bibliographic record
Abstract
This document summarizes the work undertaken for the development of an Intelligent Sign Management System (ISMS). The purpose of the project was to research and define an intelligent sign management methodology that allows the generation of location-specific messages based on multiple traffic condition criteria and knowledge of current operation strategies. The goal is to provide the foundations for significant advancement in current freeway traffic management practices. The project began with extensive research of existing Changeable Message Sign (CMS) message generation systems worldwide and potential intelligent designs in the area of sign management and response plan operations. As a result of the research, a rule-based approach was adopted due to its flexibility in response to system changes. Some requirements, such as automatic event response and an easy-to-use rule-editing tool were suggested to enhance the system functions. A prototype ISMS was designed to include four processes: event assessment, sign selection, message composition and multiple-message handling. All associated rules for each process were further defined. The ISMS prototype was developed with three components: an XML rule-based engine (including all rules), a digital road/sign navigation map and a user interface. A system evaluation using COMPASS output from the Ministry of Transportation of Ontario was conducted to validate the performance of the prototype system. The results of the evaluation showed that the ISMS prototype produced valid responses and reduced traffic response database maintenance significantly (e.g. reduced to a few minutes). This concept was proven to be an effective and efficient tool for intelligent sign management systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".