Genetically-optimized origin-destination estimation (GOODE) model: application to regional commodity movements in Ontario
Bibliographic record
Abstract
The problem of origin-destination (O-D) matrix estimation has attracted significant research attention in the past few decades. This paper proposes a novel approach to estimate a regional freight O-D matrix using different data sources. The genetically optimized origin-destination estimation (GOODE) model takes advantage of the genetic algorithm's (GA) global search procedure to find the O-D matrix that is associated with the minimum deviation between estimated and observed data values. The GOODE-commodity model, an extension of the GOODE model, estimates the freight O-D matrix by interfacing GOODE with a trip generation model based on input-output data. The GOODE model and its extension bring together national input-output data, truck survey data, a global searching method, and a GIS platform for data manipulation. This paper presents the GOODE model structure, a prototypical numerical example, a benchmarking exercise with an existing O-D estimation model, and a real-world application of the GOODE-commodity model for a case study of commodity movements in Ontario. Avenues for future research are also addressed.Key words: origin-destination (O-D) matrix estimation, truck transportation modelling, input-output, Ontario, genetic algorithm.
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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 | 0.000 |
| 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".