Better estimation of origin–destination matrix using automated intersection movement count data
Bibliographic record
Abstract
Intersection movements carry more disaggregate information about origin–destination (O–D) flows than link counts in a traffic network. In this paper, a mathematical formulation is presented for O–D matrix estimation using intersection counts, which is based on an existing linear programming model employing link counts. The proposed model estimates static O–D flows for uncongested networks assuming no a priori information on the O–D matrix. Both models were tested in two hypothetical networks previously used in O–D matrix studies to monitor their performances assuming various numbers of count location and measurement errors. Two new measures were proposed to evaluate the model characteristics of O–D flow estimation using traffic counts. While both link count based and intersection count based models performed with the same success under complete data collection assumption, intersection count based formulation estimated the O–D flows more successfully under decreasing number of observation locations. Also, the results of the 30 measurement error scenarios revealed that it performs more robustly than the link count based one; thus, it better estimates the O–D flows.
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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 | 0.001 | 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".