Evaluation of Wide-Area Traffic Monitoring Technologies for Travel Time Studies
Bibliographic record
Abstract
Road agencies typically collect travel time information from their network to identify traffic bottlenecks and to quantify the effects of road improvement investments in terms of travel time improvements. Road agencies can benefit from newly emerging automated data collection technologies that acquire travel time information for a large geographical area at lower costs. The objective of the study presented in this paper was to evaluate travel time data obtained from three technologies (i.e., Bluetooth, in-vehicle navigation systems, and mobile phone probes) compared with travel time obtained from probe vehicles equipped with Global Positioning Systems (GPSs). Traffic data were obtained for road types (e.g., freeways, arterials, ramps) in the study area from commercial data providers for a relatively large study area in the Province of Ontario, Canada. A multicriteria methodology was developed to evaluate data from each data provider on the basis of accuracy, coverage, number of observations, and capability to provide data for special facilities such as high occupancy vehicle lanes. The findings of this research suggested that all three technologies could replace traditional, GPS-equipped probe vehicles. This paper offers several recommendations on the use of travel time data from different data providers.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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".