Development of Sustainable Urban Railway Service Model Using Micmac-Mactor: A Case Study in Jabodetabek Mega-Region Indonesia
Bibliographic record
Abstract
Electric trains (KRL) provide services to residents living in Jabodetabek, one of the world's most significant regions. Although KRL is used daily by about 973,366 residents to carry out their activities, some factors influence its usage. Therefore, this study aims to identify the critical elements that affect train services and the patterns of relationships amongst actors to construct a model for long-term sustainability. This study was carried out using the Micmac and Mactor methods. Micmac is a causal structural matrix that can investigate the relationship between parameters in a system. The Mactor technique, on the other hand, is applied to a variety of tactics involving many actors and a set of related interests and goals. The results showed five critical variables for sustainable urban rail service, namely Safety, Capital, Eco monitoring and evaluation, Eco plan, and COVID control are needed. Meanwhile, The General Administration of Railways, Ministry of Transport, and Indonesian commuter train company are two institutions or actors that are very influential in mobilizing the safety of KRL users amid a pandemic to ensure the continuity of train services. This study also finds that critical variables, key actors, and rail destinations strongly influence the sustainability of social, economic, and environmental aspects of urban rail transportation services. In conclusion, this study provides new insight into developing a sustainable urban rail service model in Jabodetabek KRL.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".