An Interactive Route Planner Incorporating Air Pollution and Cycling Determinants to Facilitate and Promote Cycling in Metro Vancouver, Canada
Bibliographic record
Abstract
S-30A1-6 Background/Aims: With increasing fuel costs, greater awareness of greenhouse gas emissions and increasing obesity levels, cycling is promoted as a health promoting and sustainable transport mode. To facilitate cycling amongst the general public and to optimize new cycling route design by transportation planners, we developed a cycling route planner (http://cyclevancouver.ubc.ca) for Metro Vancouver, Canada. Methods: The geographical information system-based planner is unique in its incorporation of multiple user-specified factors that influence the choice to bicycle (eg distance, elevation gain, safety, route features, and links to transit) and traffic-related air pollutant exposures in selecting the preferred routing. Using a familiar and user-friendly Google Maps interface, the planner allows individuals to seek optimized cycling routes throughout the region based on their own preferences. In addition to the incorporation of multiple user preferences in route selection, the planner is unique in its use of topology to minimize data storage redundancy, reliance on node/vertex index tables to increase the efficiency of optimal route selection process and the use of web services and asynchronous technology to create a rich media application with quick data delivery. Results: In addition to route output, the planner also provides route-specific information on distance, net elevation gain, duration, calories burned, air pollution exposure, and carbon dioxide emissions reduction (relative to travel by automobile). The code and input data requirements are readily transferable to other locations, making possible the development of similar planning tools elsewhere. Conclusion: Use of this tool can help promote bicycle travel as a form of active transportation and help lower CO2 and air pollutant emissions by reducing car trips.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 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".