Travel Time Estimation in Urban Networks Using Neighbor Links Travel Time Data
Bibliographic record
Abstract
This paper presents a framework to estimate link travel times using available data from neighboring links. The spatial covariance between the travel times of nearby (neighbor) links in a network is used for this purpose. This can be a solution to the problem of partial network coverage caused by having a small sample of probe vehicles or a limited number of detectors. Two clues are used for real-time travel time estimation; historical travel time data and online travel time data from neighbor links. In the absence of online travel time data from neighbor links, one may rely on historical records only. However, in case the two types of data are available, a data fusion scheme should be applied to make use of the two clues. Real-life travel time data were collected and used to validate the proposed framework. The prediction accuracy was assessed using error measurements and the results were satisfactory. A microsimulation model for downtown Vancouver was developed using VISSIM to analyze the impact of probe vehicle sample size on the proposed methodology. Six market penetration levels were tested under five different demand scenarios. As expected, the estimation accuracy of the proposed method improved with higher market penetration levels. The methodology provided reasonable estimation even with small probe samples after introducing a threshold to filter outlier predictions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| 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; both teacher heads agree on what is shown here.
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".