Dual‐Layer Dynamic Optimization Model for Carbon‐Conscious Transport Mode Allocation
Bibliographic record
Abstract
Within the framework of China’s “Dual Carbon” strategy, numerous cities have articulated visions and objectives for optimizing the travel structure of transport to further urban sustainable development goals. A critical challenge lies in minimizing CO 2 emissions from road networks while meeting diverse transport demands. However, at present the mode shares are set arbitrarily and may not be realistically achievable. When government officials establish travel structure targets, they may not adequately consider the intricate balance between residents’ travel demands and low‐carbon development objectives. To address this issue, this paper presents a dual‐layer optimal allocation model for transport modes, which simultaneously addresses travel demand management and carbon emission control. The upper‐layer model evaluates carbon emissions with the help of speed‐dependent emission factors for various transport modes, and the lower‐layer model leverages the logit Stochastic User Equilibrium (logitSUE) model to yield the velocities of road segments under a diverse array of travel structures. A sophisticated fusion algorithm, integrating the Dial_MSA algorithm with a genetic algorithm (GA), is developed to solve the model. The proposed model and algorithm are tested on a large‐scale real network and show its robustness and scalability. The optimal travel structure derived from this study can provide a theoretical foundation and empirical support for policymakers and urban planners in setting transport infrastructure goals and strategies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".