Real-Time Freeway Travel Time Prediction Using Vehicle Trajectory Data
Bibliographic record
Abstract
This paper describes a new methodology proposed for real-time travel time prediction utilizing vehicle trajectory data and shockwave information. The main idea behind this methodology is that average speed on a section of roadway is constant unless a shockwave is created due to change in flow or traffic density. In the proposed methodology first the route is discretized into a number of smaller road sections and the average speed of each section is calculated based on the available information obtained from vehicles trajectories during the current time interval. The travel times obtained from average speed of each road section are modified if any shockwaves are identified in the traffic stream. The proposed model was evaluated using the vehicle trajectory data from global positioning system (GPS) data loggers on a freeway section in Toronto, Ontario. It is shown that the prediction accuracy of the proposed model is superior to the travel times obtained from traditional loop detectors. Moreover, this paper shows that alternative sources of data which use the existing infrastructure (e.g. cell phone network) can potentially be used to acquire traffic information. This is especially important for rural freeways which do not have full Freeway Traffic Management System (FTMS) infrastructure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".