Train Service Plan Design under the Condition of Multimodal Rail Transit Systems Integration and Interconnection
Bibliographic record
Abstract
Multimodal rail transit systems integration and interconnection can solve frequent transfer problems and better adapt to disequilibrium passenger flow and space. It is an inevitable choice in the development of various rail transit systems. Firstly, this paper proposes a novel train service plan design model in the scenario of multimodal rail transit systems integration and interconnection. Our model takes into account the costs of both passengers and enterprises, and passengers travel time is converted into cost using passengers’ nonworking time value coefficient. The model contains some conventional constraints such as passenger flow, station capacity, and line carrying capacity. It also considers whether the transportation capacity of different lines is matched, that is, the constraint of capacity matching degree. Secondly, an improved harmonic search algorithm (IHSA) is designed to solve the problem, and a numerical experiment is used to prove the performance of the proposed method. Our research result shows that the model and algorithm proposed in this paper is effective, which can help overcome the drawbacks of the existing independent operation mode of different rail transit systems. This study is also applicable to the scenario of other kinds of rail transit systems integration and interconnection.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".