A System for Collecting and Mapping Traffic Congestion in a Network Using GPS Smartphones from Regular Drivers
Bibliographic record
Abstract
This paper presents an update on the current development of a smartphone-based system for collecting and analyzing route global positioning system (GPS) data coming from regular driver routes. Using GPS functionality on Android and iOS smartphones to log route data, a large database containing thousands of trips was collected in a short period of time after a mass-media campaign in Quebec City. As part of the system, a platform was built for mapping traffic congestion using the average speed and speed differential at the link level. The system includes a number of components: i) a smartphone application, ii) a server-platform to receive and display raw data, and iii) an analytics platform to compute and display different link-level speed (or travel time) measures with interactive maps. As a result of the smartphone GPS data collection method, a large dataset of information was generated from more than 30,000 trips collected in 3 weeks and more than 4,000 drivers emitting trips on a voluntary basis. The results demonstrate the feasibility and promising potential of the data collection system that can be implemented in any city and sets the ground for real-time applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".