CityDrive: A map-generating and speed-optimizing driving system
Bibliographic record
Abstract
There have been many traffic light control systems around the globe, but the high cost of infrastructure and maintenance hinders their wide deployment. However, speed-advisory systems enabled by on-vehicle devices are much cheaper and easier to deploy. The first challenge of such systems is to get the traffic signal schedule in complex intersections. The second challenge is to get map information and calculate the distance. Facing these challenges we devise and implement a speed-advisory driving system called CityDrive, which harnesses the sensor and GPS data from a wide participation of smartphones to suggest proper speed for drivers so that they arrive at intersections in green phase. CityDrive first generates a road map and then infers traffic signal schedules, using only smartphones and a server. CityDrive does not eliminate stops at intersections, but it tries to maximize the probability that vehicles cruise through intersections in green phase. Both simulation and real test show that this continuous speed advisory service effectively smoothes traffic flow and significantly reduces energy consumption.
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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 | 0.001 |
| 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 | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".