Study on Accessibility of Feeder Lines with Different Geometric Shapes
Bibliographic record
Abstract
The performance of the urban rail transit system depends largely on the operation efficiency of its feeder system. An improved way is proposed in this paper to calculate the accessibility index which is used for evaluation of feeder lines, and both passengers’ waiting time and the competition between different access modes are considered. Different from previous research, this paper focuses on whether and how the geometric shape differences affect the performance of fixed-route feeder lines. The impacts of line shape on accessibility are analysed by derived formulas for calculating accessibility under ideal conditions. The finding emerging from this study is that the accessibility of feeder lines differs obviously due to their shapes, given the same conditions of total line length, stop spacing, and vehicle fleet size. The service area of feeder lines with branches or a loop is closer to the rail station compared with that of linear lines, thus leading to fewer passengers served but shorter average travel time under specific routing plans. For further exploring the universality of the above finding, route alignment optimization models to maximize accessibility are built for different shaped lines. The optimal solutions of different shapes are obtained and compared in the cases of random generation to analyse the impacts of shapes on accessibility under different line length 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".