A Novel Trip Coverage Index for Transit Accessibility Assessment Using Mobile Phone Data
Bibliographic record
Abstract
Transit accessibility is an important measure on the service performance of transit systems. To assess whether the public transit service is well accessible for trips of specific origins, destinations, and origin-destination (OD) pairs, a novel measure, the Trip Coverage Index (TCI), is proposed in this paper. TCI considers both the transit trip coverage and spatial distribution of individual travel demands. Massive trips between cellular base stations are estimated by using over four-million mobile phone users. An easy-to-implement method is also developed to extract the transit information and driving routes for millions of requests. Then the trip coverage of each OD pair is calculated. For demonstrative purposes, TCI is applied to the transit network of Hangzhou, China. The results show that TCI represents the better transit trip coverage and provides a more powerful assessment tool of transit quality of service. Since the calculation is based on trips of all modes, but not only the transit trips, TCI offers an overall accessibility for the transit system performance. It enables decision makers to assess transit accessibility in a finer-grained manner on the individual trip level and can be well transformed to measure transit services of other cities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".