Geographic information system (GIS) based decision support for neighbourhood traffic calming
Bibliographic record
Abstract
In suburban areas, traffic issues are generally related to elevated speeds and volumes and a perceived reduction in personal safety. In response, traffic engineers have designed and implemented a variety of traffic calming measures for local and collector streets, with significant speed reductions and other benefits. Less common are measures to address traffic issues on arterials which (if implemented) might reduce speeds, thereby encouraging more sustainable transportation modes and lessening automobile dependence. A geographic information system (GIS) based tool has been developed to provide decision support for the development of neighbourhood traffic calming plans for all street types. This tool is potentially useful because of the increased use of traffic calming measures and the growing public desire for safer streets. Decision support (provided by the tool) is dependent upon measured or perceived problems, roadway type, and user objectives, as well as the potential impacts and current installation costs of traffic calming measures. An application to suburban Hamilton demonstrates the functionality of this tool.Key words: traffic calming, suburban retrofitting, urban sustainability, decision support system.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".