Link travel time and delay estimation using transit AVL data
Bibliographic record
Abstract
Estimating arterial link travel times and traffic delays using vehicular positioning data, such as automatic vehicle location (AVL) data, is still a challenging subject. The difficulties exist in allocating the travel time between two consecutive AVL reports of a vehicle to each traversed link, especially when the data sampling frequency is low, and identifying the proportion of traffic delay in link travel time. In this paper, transit buses with 30-second sampling interval AVL data were applied as probes to estimate link travel time and traffic delay caused by intersections or alighting and boarding at bus stops. The estimation model proposed in this paper decomposed travel time into three components: free flow travel time, congestion time, and stopping time at signalized intersections and bus stops, then allocated them to each road link. Unlike existing deterministic methods, the proposed solution defined a likelihood function that is maximized to solve for the most likely traffic delays for each road segment on the route. Field tests were conducted on a typical arterial corridor in Edmonton, Canada for data collection and algorithm performance evaluation. The results suggested that the proposed model provides effective and accurate estimation of traffic delay, which can be further applied to transit based or probe vehicle based traffic applications, such as travel time estimation and travel speed estimation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".