Expansion of bi-modal express transit networks − a hybrid optimization approach
Bibliographic record
Abstract
Existing research has predominantly concentrated on designing a new transit network without considering the pre-existing network. However, the majority of problems involve redesigning or extending an already existing network. Considering the pre-existing multi-modal transit network in a city, we have integrated analytical methodologies and mathematical programming to formulate a two-stage approach for addressing the bi-modal express transit network design problem (ETNDP) within the context of a surface express transit system. In the first stage, we use analytical approaches and continuum approximations to identify the optimal locations of new stations. In the second stage, mathematical programming is proposed to simultaneously determine the optimal layout of express transit routes, the technology of each transit route, and the service headway associated with all transit routes (i.e., existing and new routes). Then, a metaheuristic algorithm based on a Genetic Algorithm is introduced to solve the proposed mathematical programming for real-size transit networks. The proposed approach has then been applied to the express transit network of Calgary, Canada, a large-sized bi-modal express network. The bi-modal ETNDP has been solved for Calgary under various scenarios, and the results have been discussed. Analyses show that, in the proposed hybrid approach, solving a part of the problem analytically reduces its complexity significantly and enables parametric analysis, while using mathematical programming helps to address the complexity of ETNDP for real transit networks. The proposed approach stands out from existing similar studies due to its departure from simplifying assumptions concerning network topology, the city’s structure, capturing any type of demand patterns, model flexibility to for existing transit network extension, and multi-modality of the express transit network.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".