Implementation strategy of transit-oriented development based on the bus rapid transit system in Indonesia
Bibliographic record
Abstract
The bus rapid transit (BRT) system has become a cheap public transportation option worldwide, including in Indonesia. The problem in the Jababeka area, Indonesia, was the unconnectedness and lack of transportation as a sustainable area with the whole residence, modal shift, and easy access for people. This research aimed to improve access to Bus Rapid Transit (BRT) based public transportation by implementing the Transit Oriented Development (TOD) Model in the Jababeka area, Bekasi Regency. In this research, modeling was made by using PTV Visum with the trip assignment method and continued with the projected movement from 2022 to 2042, resulting from the people movement survey in 2022 and the SWOT strategy. The sample of this research consists of 210 respondents domiciled in nine subdistricts of Bekasi Regency. The result of this research was that the Jababeka area, Bekasi, must be planned as a TOD-based area, facilitating people to fulfill their transportation needs so that derived demand can run efficiently. Therefore, the implemented strategy must improve transportation access by developing TOD areas with a BRT system. Jababeka area was developed using the typology of regional scale city TOD, with a potential sub-city and environmental TOD typology. TOD development using the BRT system must be able to shift the intercity movement to local movement because residential areas were provided as the substitute for intercity movement.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".