Using the Canadian ITS architecture for evaluating the safety benefits of intelligent transportation systems
Bibliographic record
Abstract
The benefits of intelligent transportation systems (ITS) are indirectly represented by the annual world market for ITS, which according to ITS Canada (2002) will be $90 billion CAN by 2011. Improved safety is often cited as the top goal of implementing ITS. Despite the magnitude of these investments and their underlying goal to improve transportation safety, there are deficiencies in the quantity and quality of reported ITS safety benefits. Many of the benefits reported to date suffer from poor data, lack of an evaluation framework, and inconsistent terminology used to attribute benefits to ITS application areas. This paper explores these issues, while attempting to address one of them, namely the lack of an evaluation framework for assessing the safety benefits of ITS. Accordingly, a unique framework is developed based on the Canadian ITS architecture. The framework includes the identification of evaluation metrics that are mapped to the market packages in the Canadian ITS architecture and correlated with each other to capture the "cause" and "effect" flow of benefits. This framework will benefit future ITS safety evaluations by providing a structure for undertaking evaluations using terminology consistent with the Canadian ITS architecture.Key words: intelligent transportation systems, ITS architecture, safety benefits, safety evaluation.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".