A System Dynamics Model of Urban Railway Demand Prediction for Safety and Security Improvement: Lessons Learned from Indonesian Railway Network
Bibliographic record
Abstract
The number of passengers on the Indonesian urban rail network (KRL Jabodetabek) continues to increase from year to year, causing problems like passenger overcapacity at stations and on trains.The safety and security of KRL needs to be improved by managing the demand for KRL passengers.This paper aims to predict the number of passengers, trains, and employees, as well as the total income and amount of subsidies required to support rational policymaking for future railway management.The prediction was carried out based on system dynamics modeling, a framework demonstration technique integrating system science with computer simulation.Our system dynamics model was built, and validated on time series data from 2014 to 2020.The results show that, the number of passengers, trains, and employees increased annually from 2014 to 2019, but decreased in 2020, for the human movement is restricted by COVID-19.According to the simulation results, the daily number of electric rail train passengers was projected to reach 1,387,295 in 2035, and 1,365 extra trains would be required to cater to the passengers.The safety and security of trains, which are strongly correlated with the management of urban electric rail passenger demand, would be jeopardized, if the number of passenger requests is out of control.The research provides unique empirical and theoretical materials for academics, and sheds new light on the importance of safety and security enhancement to urban rail network.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".