Freight Demand Model Development and Application at the National and Urban Scale
Bibliographic record
Abstract
Freight demand models are tools that are used for the analysis of the freight transportation system, exploring the impacts of potential changes to the system, and forecasting the future demand. This thesis identifies some of the limitations in freight modelling through a review of current models and addresses these gaps by developing a component of a national freight model in the US, a carrier selection model, and an urban commercial vehicle (CV) model for the Greater Toronto and Hamilton Area (GTHA). The models are then applied to examine some important issues and trends in freight transportation, namely, truck driver shortages, truck carrier market consolidation, off-peak urban truck delivery and automated trucking.The carrier selection model simulates the transportation procurement market in the US, where shippers award annual contracts to for-hire carriers to transport goods to their customers. It is based on a heuristic algorithm that iterates through a list of shippers and shipment flows and assigns carriers based on price and carrier capacity. The model generates reasonable outcomes in terms of empty truck mileage and carrier revenue, among others. Model results suggest that some carriers may gain an advantage in terms of revenue compared to others. Scenario analyses identify how empty mileage, market share and contract locations (within-state, out-of-state, etc.) may change for owner-operators, small and large carriers. The GTHA CV model is a trip-based model with three components – truck trip generation, truck trip distribution and traffic assignment. Truck trip generation is based on a set of regression models, the outcomes of which suggest that rate-based trip generation – a common approach in the operational freight models – is not appropriate for most industry categories and truck types. Two applications of the CV model are presented in this thesis – off-peak truck deliveries and automated trucking. Model results of off-peak scenarios suggest that a daily travel time savings of over 5500 vehicle-hours can be obtained in the GTHA. Automated trucking is predicted to cause more congestion during partial adoption, but substantial travel time savings will be achieved at full automation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".