Designing a multimodal feeder network by covering stops with different modes
Bibliographic record
Abstract
Mass rapid transit requires a high demand to function economically. It is necessary to provide complementary services, such as buses and vans, to increase the required demand. This results in the possibility of providing high performance transit to a wider area. One of the suggested methods is to establish an integrated system of a feeder network. Using such systems provide not only sufficient demand for the main networks but also the possibility of an integrated public transportation system that extends to the entire city. In this paper, we have proposed a method for designing feeder networks that gives multimodal services at each stop simultaneously by assigning the demand to different modes. The demand assignment is based on optimum use of fleet rather than desirability functions. The objective is to minimize the costs of the users, operators, and society. Furthermore, as multimodal network designing is a complex problem, a metaheuristic approach, ant colony optimization, is used. For illustrating and comparing the results, a real example in Mashhad, Iran, is run through 16 different scenarios. The scenarios were designed with different unit costs, and the results have been compared with the latest study. In all of the scenarios, the network costs are lower than those in other methods.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".