Is It Possible to Automatically Build a Large Scale Metropolitan Traffic Model? Evidence from a Study of Connected Transportation Applications in Montreal
Bibliographic record
Abstract
Intelligent Transport Systems (ITS) can improve transport systems while reducing their negative impacts. The deployments of ITS applications are highly dependent on telecommunications technologies, which must be tested either in field experiments or in simulation. The traffic simulation is the least expensive and most flexible solution. Yet, models require large amounts of data and expertise to develop, and the impacts of their parameters on their outputs for ITS applications have not been much investigated. Using the Simulation of Urban Mobility (SUMO) tool as an example, this article attempts to build automatically a simple large-scale traffic model for the Island of Montreal and evaluates how this model performs. This paper considers microscopic and a mesoscopic models, along with three traffic assignment methods. The outputs are compared to travel times from global navigation satellite system (GNSS) data and from Bluetooth sensors, and a sensitivity analysis is also performed for several output indicators at different scales. The results highlight the trade-offs between more detailed and accurate microscopic models, and faster, less accurate, mesoscopic models.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".