Towards Realistic Vehicular Network Modeling Using Planet-scale Public\n Webcams
Bibliographic record
Abstract
Realistic modeling of vehicular mobility has been particularly challenging\ndue to a lack of large libraries of measurements in the research community. In\nthis paper we introduce a novel method for large-scale monitoring, analysis,\nand identification of spatio-temporal models for vehicular mobility using the\nfreely available online webcams in cities across the globe. We collect\nvehicular mobility traces from 2,700 traffic webcams in 10 different cities for\nseveral months and generate a mobility dataset of 7.5 Terabytes consisting of\n125 million of images. To the best of our knowl- edge, this is the largest data\nset ever used in such study. To process and analyze this data, we propose an\nefficient and scalable algorithm to estimate traffic density based on\nbackground image subtraction. Initial results show that at least 82% of\nindividual cameras with less than 5% deviation from four cities follow\nLoglogistic distribution and also 94% cameras from Toronto follow gamma\ndistribution. The aggregate results from each city also demonstrate that Log-\nLogistic and gamma distribution pass the KS-test with 95% confidence.\nFurthermore, many of the camera traces exhibit long range dependence, with\nself-similarity evident in the aggregates of traffic (per city). We believe our\nnovel data collection method and dataset provide a much needed contribution to\nthe research community for realistic modeling of vehicular networks and\nmobility.\n
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".