An evaluation of the productivity change in public transport sector using DEA-based model
Bibliographic record
Abstract
This study aims to build a framework for measuring the productivity in the public transport sector through a data envelopment analysis (DEA) technique. This paper extends the Malmquist productivity index (MP1) and Luenberger productivity indicator (I.P1) evaluation with the concept of an input-oriented new slack model (NSM). NSM model measures the efficiency with the effect of slacks and satisfies unit invariance, radial and translation invariance properties. In particular, the purpose of the proposed extension is to obtain the overall productivity change in terms of technical change (Frontier Shift) and technical efficiency change (Catch-up Effect) for Rajasthan State Road Transport Corporation (RSRTC) bus depots from 2008 to 2019. For this purpose, the number of buses, number of employers, fuel consumption and route distance arc are considered input variables, while passenger-kilometres occupied and vehicle utilisation are output variables. Finally, the result demonstrates that the average total factor productivity (TFP) growth of 46 depots using MPI and LPI over the study period is 1.956% and 1.409%, respectively. This study enables policy-maker and managers to evaluate the input to reach consistent output up to an optimum level and understand the process of improving the productivity level for the bus depots.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".