Estimation of expected travel time using the method of moment
Bibliographic record
Abstract
Daily travel time is cast into a framework of nonstationary stochastic process. For a fixed value of departure time in a day, travel time given origin, destination, and route information, is treated as a random variable. For a specific date, travel time is treated as a deterministic function of departure time t. Under this framework, the expected travel time for a given departure time is defined as an ensemble mean travel time (EMTT) over a number of days. The method of moment is proposed to compute EMTT based on a hypothetical piecewise constant speed trajectory for travel time estimation. The advantage of the method of moment for EMTT estimation is that it only requires ensemble mean and ensemble variance of spot speed information at point detectors, which is much easier and cost-effective to get than obtaining collections of massive spot speed data per se. The result is compared against Monte Carlo simulation and direct sampling based simulation. The proposed method of moment approach provides accurate estimation of EMTT (e.g., the expected travel time estimation) under a wide range of traffic conditions (e.g., free flow and congestion).
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".