Adaptation and Implementation of a System for Collecting and Analyzing Cyclist Route Data Using Smartphones
Bibliographic record
Abstract
The paper presents the adaptability and implementation process of a cyclist-specific system for collecting route data and information from cyclists in the City of Montreal. Using global positioning system (GPS) functionality on Android and iOS smartphones to log route data, travel time, distance, and route choice are obtained for each trip. An anonymous questionnaire with socio-demographic profiles and other attributes is also obtained for each participant. The system builds on the foundation set out by such peer-reviewed projects as CycleTracks and Cycle Atlanta. However, several features were added to improve performance and provide city-specific information. This includes i) a model to break single trips into a series of segments to manage stopping and GPS connection loss, ii) a new method to compute average speed with a simple Kalman-filter algorithm, iii) addition of a bicycle network showing bicycle facilities, and iv) new models, including a calorie counter and an emissions tool to compute greenhouse gas offset adjusting for congestion (speed) in the network and considering local parameters. Despite the brevity of the application (Mon ResoVelo), the number of logged trips reached more than 2300 with more than 500 cyclists registering in the first three weeks. This demonstrates the great acceptability and performance of the 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".