Analysis of a Flexible Transit Network in a Radial Street Pattern
Bibliographic record
Abstract
Traditional transit systems are usually composed of fixed routes and stops, which are suitable in densely populated areas. This paper presents a reformulation of the flexible transit model developed by Nourbakhsh and Ouyang (2012) to adapt it to many low demand cities in the world, especially those characterized by radial street patterns. Unlike traditional ones, buses of the proposed transit network are allowed to traverse in a predetermined service area and their precise trajectories hinge on the exact locations of passengers. To identify the optimal topology structure of the flexible transit system, continuous approximation approaches are developed to explore the optimal value of design parameters of the whole system, defining the optimal network layout through minimizing its objective function. To exhibit its advantages, numerical experiments are conducted to compare the flexible transit system with its two variants. The results show that the flexible transit system proposed in this paper outperforms the other two variants. The higher the access cost is, the more it would tilt towards the flexible transit system with a significant margin. Besides, the flexible transit system in a radial pattern competes more effectively than that in a grid structure. This is encouraging because the proposed transit system can be applied in a number of real-world cases.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".