Determining the Level of Service Scale of Public Transport System considering the Distribution of Service Quality
Bibliographic record
Abstract
In China, many cities are building themselves the transit metropolis, and the reasonable evaluation of level of service (LOS) of public transport system (PTS) is one important aspect. However, to determine the overall LOS is hard because the distribution of service in PTS is not homogeneous with regard to time and space. To address this problem, this study proposes a general framework to determine the LOS scale of PTS based on the distribution of service quality. Under the framework, two classification methods are discussed. Method 1 uses two parameters, the mean and coefficient of variation to model the distribution, and Method 2 is an existing approach that only considers mean. Then the specific use of the framework is expounded for the service attribute of crowding, and Beijing subway line LOS is evaluated. The line LOS is divided into I–IV, whose threshold is expressed as a function of mean and coefficient of variation. The results show that 57.8% of the sample points are in the most crowding level IV in morning peak hours by Method 1, but 60.9% of sample points are in a comfortable level II by Method 2, and the former is more consistent with reality. In addition, it reveals which lines and time periods need to improve the service level. The research proves the feasibility of considering the service distribution to determine the overall LOS of PTS, and it is useful for capturing more detailed information of the system performance in time and space. This research can provide an approach for evaluating and helping to improve the overall service level of PTS for public transport authorities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".