Freeway Sensor Spacing and Probe Vehicle Penetration
Bibliographic record
Abstract
Accurate travel time prediction–estimation is important for advanced traveler information systems and advanced traffic management systems. Traffic managers and operators are interested in estimating optimal sensor density for new construction and retrofits. In addition, with the development of vehicle-tracking technologies, they may be interested in estimating optimal probe vehicle percentage. Unlike most studies focusing on data-driven models, this paper extends some limited previous work and describes a concept developed from first principles of traffic flow. The goal is to establish analytical relationships between travel time prediction–estimation accuracy and sensor spacing by means of two basic travel time prediction–estimation algorithms, as well as to probe vehicle penetration rate. The methods are based on computing the magnitude of under- and overprediction–estimation of total travel time (TTT) during shock passages in a time–space plane by using the midpoint method for online travel time prediction and the Coifman method for offline travel time estimation. Three shock wave configurations are assessed with each method so as to consider representative traffic dynamics situations. TTT prediction–estimation errors are calculated and expressed as a function of sensor spacing and probe vehicle percentage. Optimal sensor spacing is calculated with consideration of the tradeoff between TTT estimation error and sensor deployment cost. The results from this study can provide simple and effective support for detector placement and probe vehicle deployment, especially along a freeway corridor with existing detectors. Optimal sensor spacing results are analyzed and compared for various methods of travel time estimation during different types of shock waves.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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; 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".