Commodity-Based Truck Origin–Destination Matrix Estimation Using Input–Output Data and Genetic Algorithms
Bibliographic record
Abstract
A commodity-based model to estimate a truck origin–destination (O-D) matrix is presented. The model takes advantage of the genetic algorithm global search method to find the best O-D matrix that when assigned to the network gives the minimum deviation between observed and estimated data. The model is flexible with respect to the type of data used in estimating the O-D matrix; however, the case study presented in this paper takes into consideration only two sets of information: commodity flow on specific links and column and row sums of the O-D matrix. Flows are treated as commodity dollar value; therefore, the estimated O-D matrix entries consist of the value of the commodity shipped by truck from the origin zone to the destination zone. The method is composed of two submodels. The first submodel, the trip generation model, uses input–output data with employment and population data to estimate the zonal level of commodity attraction and production. The second submodel, the genetic algorithm model, searches globally for the optimum O-D matrix. The model and its application to a case study of a region in Ontario, Canada, are presented. Directions for future research are provided.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".