VEHICLE TRAVEL TIME DISTRIBUTION ESTIMATION AND
Bibliographic record
Abstract
We introduce two statistical methods for estimating vehicle travel time distribu-tions on a road network, using Global Positioning System (GPS) data recorded during historical vehicle trips. In the first method, we use a model of the path taken by each vehicle in the data, the travel time on each road segment in the network, and the location and speed errors for each GPS observation. In the sec-ond method, we use a model of the entire travel time of each trip, and include covariates such as the types of roads traveled and time of day. We estimate the parameters of both models by Markov chain Monte Carlo methods. We compare the performance of these methods with two simpler methods, a recently published method, and commercially available travel time estimates, using data from ambulance trips in Toronto and simulated data. Our methods outperform the alternative methods in point and distribution estimation of out-of-sample trip travel times. Our methods also provide more realistic estimates than the recently published method of the probability that an ambulance is able to respond to each intersection in Toronto within a time threshold.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".