Evolutionary Game Model of Civil Aviation and High-Speed Rail Interaction Strategies Based on the Passenger Ticket and Carbon Trading Prices
Bibliographic record
Abstract
This study elaborated an evolutionary game model to optimize the decision-making process on the interaction between civil aviation and high-speed rail under alternative passenger ticket prices (PTPs) and carbon trading prices (CTPs). First, a logit model was used to calculate the passenger flow distribution rate in civil aviation and HSR, and their revenue loss function was determined according to varying PTPs and CTPs. Second, an evolutionary game model with incomplete information was developed to assess the respective revenues. Third, the stable strategy solution of the game model was derived from replicator dynamics, and an investigation of stable conditions under variable cases was performed. Finally, the simulation of a case study on the Beijing–Shanghai corridor was conducted to validate the proposed model’s feasibility. The additional revenue is shown to be the key influencing factor, mainly controlling the strategic decisions of airport and high-speed rail companies. Besides, the final strategy was strongly influenced by the alteration of PTPs and CTPs: higher PTPs promoted civil aviation and high-speed rail collaboration, while increased CTPs forced their competing behavior. The results obtained are instrumental in outlining the optimal strategy range for passenger ticket and carbon trading prices, encouraging high-speed transportation system growth.
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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".