INTELLIGENT TRANSPORTATION SYSTEMS STRATEGIES FOR GREENHOUSE GAS REDUCTION IN CANADA
Bibliographic record
Abstract
This paper was presented at the session titled 'Reducing the environmental effects of transportation through technology'. The Canadian Transportation Table is evaluating a wide range of possible measures for reducing greenhouse gas (GHG) emissions from the transport sector. This work is part of the National Climate Change Process to develop a national strategy for meeting Canada's commitment, under the Kyoto Protocol, to reduce GHG emissions by 6% by the years 2008 to 2012, relative to 1990 levels. This paper reports the findings of a study to investigate specific Intelligent Transportation Systems (ITS) measures which might be used to reduce GHG emissions on Canada's road/highway transportation network. A variety of ITS market packages have been analysed with respect to demonstrated environmental benefits, and applicability within the Canadian context. The ITS market packages which provide significant GHG reduction benefits, and should be considered for widespread deployment in Canada include: Incident Management, Traffic Control, Traveller Information, Electronic Payment Systems, Commercial Vehicle Electronic Clearance, and Advanced Vehicle Safety Systems. If all the ITS market packages are deployed the total annual GHG reduction in year 2010 is estimated to be 770 kt. This figure represents 0.5% of the total GHG output attributed to transportation in 1990, and 2% of the total transportation GHG reduction target for 2010. The ITS market packages provide a range of ancillary benefits such as reduced fuel consumption and emissions, reduced collisions, reduced delay and improved operating efficiencies. The cost savings associated with these benefits offset the direct costs, resulting in a lifetime net benefit per tonne of GHG reduction of $100. For the covering abstract of this conference see ITRD number E200883. (A)
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".