Freeway Travel Time Prediction and Route Recommendation via Cell Phone
Bibliographic record
Abstract
This paper presents an innovative approach to improving the congestion problems on highways. The result provided below can benefit highway users and, therefore, is commercially appealing. Within the scope of this project, three different methods were developed to interpret Highway 401 (Toronto) data into expected travel time. MATLAB was used to retrieve real-time speed data from the ITS Center and Testbed (ICAT) platform at the University of Toronto and a linear interpolation over distance was used to calculate an estimated travel time. Given the algorithm and user inputs for the on-ramp and off-ramp locations, the program approximated the expected travel time from the origin to destination and also suggests whether to switch from “collector” to “express” lanes and where to do so to minimize the travel time. Finally, the project incorporated a significant inter-protocol development component, where a user interface was created with the use of wireless internet technology. With this service, having already departed, users can receive valuable information about their optimal travel route, by just specifying the on- and off-ramp locations on a wireless internet site accessible from their cell phones.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".