Link speed estimation using GPS data: an empirical investigation of some issues
Bibliographic record
Abstract
Probe vehicles equipped with tracking devices such as global positioning system receivers (GPS) can be utilized for real-time link speed estimation. In this empirical research, the impact of the data collection resolution, also known as the polling interval, on the network coverage and link speed estimation accuracy was explored. Furthermore, a comparison was made between different methods that currently exist for average link speed estimation using GPS data. The study made use of a 1 s resolution GPS dataset that covered 100 trips in Vancouver, BC. The dataset was sub-sampled 36 times to simulate cases of 5–180 s sampling intervals. An existing map-matching algorithm was used to match the GPS points to the correct travel links for the 36 datasets. Consequently, average link speed was calculated for each link in the dataset using the time stamp difference method and the average instantaneous speed method. A slight variation of the average instantaneous speed method was also tested where instantaneous speeds were computed from position information only. An improvement was further applied to the latter method by using a path inference technique to compensate for the lack of GPS points on some links. The speed estimation methods were compared at different polling intervals and the results were discussed. In general, it was shown that the average instantaneous speed method provides the highest estimation accuracy while the path inference method provides the highest coverage compared to all other methods.
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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".