Mode Choice Change under Environmental Constraints in the Combined Modal Split and Traffic Assignment Model
Bibliographic record
Abstract
With the increasing level of air pollution and fine dust, many countries are trying to prevent further environmental damage, with various government legislations, such as the Kyoto Protocol and the Paris Agreement. In the transportation field, a variety of environmental protection schemes are also being considered (e.g., banning old diesel vehicles, alternate no-driving systems, electric car subsidies, and environmental cost charging by tax). Imposing environmental constraints is a good approach to reflect various environmental protections. The objective of this research was to analyze the mode-choice and route-choice changes based on imposing environmental constraints. For the objective, a combined modal split and traffic assignment (CMA) model with an environmental constraint model was developed. For the environmental constraint, carbon monoxide (CO) was adopted, because most of the CO emissions in the air are emitted by motorized vehicles. After a detailed description of the model, the validity and some properties of the model and algorithm are demonstrated with two numerical examples (e.g., a small and a real network in the city of Winnipeg, Canada). From the numerical results, we can observe that imposing the small restriction (or strict) value has more efficiency in mode change and reducing network emission.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".