Accuracy of distance‐based travel time decomposition in probe vehicle systems
Bibliographic record
Abstract
SUMMARY Probe vehicle data (PVD) are commonly used for area‐wide measurements of travel time in road networks. In this context, travel times usually refer to fixed edges of an underlying (digital) map. That means measured travel times have to be transformed into so‐called link travel times first. This paper analyzes a common method being applied for solving this task (distance‐based travel time decomposition). It is shown that, in general, its inherent imprecision must not be neglected. Instead, it might cause a serious misinterpretation of data if potential errors in the context of travel time decomposition are ignored. For this purpose, systematic as well as maximum deviations between “decomposed” and “true” link travel times are mathematically analyzed. By that, divergent statements in the literature about the accuracy of PVD are harmonized. Moreover, conditions for the applicability of the so‐called distance‐proportion method are derived depending on the permitted error level. Three examples ranging from pure theory to real world confirm the analytical findings and underline the problems resulting from distance‐based travel time decomposition at local level, for example, at individual intersections. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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".