An Agent-based Freight Business and Logistics Decisions Simulation - FREBUILDS
Bibliographic record
Abstract
This dissertation focuses on agent-based microsimulation urban freight transportation modelling. Urban freight models are classified based on multiple dimensions of freight transportation. A conceptual framework of an agent-based urban freight model is developed. The framework integrates key business and logistics decisions, from short- to long-term, made by firms. Some of the modelling components of the framework are estimated for the Greater Toronto and Hamilton Area. This dissertation contains four empirical studies that are part of the conceptual framework of the urban freight model. The first study focuses on long-term business decisions and estimates an establishment location choice model that incorporates intra-firm interactions. The next three studies focus on key logistics decisions that are part of the shipment formation decisions. The first of these studies estimates models of freight transportation outsourcing and vehicle type choice decisions. The two decisions are modelled independently and jointly using discrete choice methods. The second study compares machine learning and discrete choice methods for the choice of freight vehicle type. The final study, that is part of shipment formation decisions, estimates empirical models of freight vehicle type and shipment size choice. It studies the nature of the choice process i.e., sequential or joint, and recommends the appropriate modelling structure. Together with the remaining modelling components of the urban freight modelling framework, this dissertation is a first step towards operational urban freight models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".