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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".