Multiobjective Approach to the Transit Network Design Problem with Variable Demand considering Transit Equity
Bibliographic record
Abstract
Existing research on the transit network design problem has tended to focus on minimizing the various costs for both transit operators and users. However, to implement an appropriate and effective transit network in urban environments, it is important not to overly simplify the intrinsically complex nature of real-life network designs. In particular, the minimization of variance in transit service levels typically employed in existing methods can be significantly improved by incorporating a transit equity component. This paper adopts a multiobjective approach that considers system efficiency, user inconvenience, and transit equity without the use of weights in order to design a more realistic and efficient transit network. In particular, the multiobjective Nondominated Sorting Genetic Algorithm-II and the neighborhood local search method are employed in a logit-based mode-choice model in order to incorporate the variable transit demand arising from the private vehicle traffic volume. A toy test network and a real-life network from the city of Goyang, Republic of Korea, are used to verify the effectiveness of the proposed model. The model finds a set of solutions that improve transit equity with minimal losses of other objectives when compared to existing approaches, which produce a significant variance in the level of service, mainly due to the spatially condensed and overlapping distribution of their transit networks. In addition, the relationship between the three objective functions and their resulting patterns in response to key influential factors are also analyzed to verify the robustness of the proposed method in response to changing future conditions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".