Enhancing Urban Resilience through Strategic Parking Pricing in Jakarta’s Bus Rapid Transit Corridors
Bibliographic record
Abstract
Although Jakarta has invested in various mass transport systems, these efforts have not successfully reduced private vehicle use. Due to this, this study aims to analyze the impact of implementing TransJakarta bus rapid transit corridor-based high parking tariffs on travel mode choice, including road- and rail-based public transport, ride-hailing, taxi, car, and motorcycle. Involving 478 private vehicle users and implementing a nested logit model, some variables, including respondents’ income, travel time, egress time, parking costs, parking distance, travel cost, and parking surcharge, are considered to understand to what extent these variables influence the use of proposed travel mode in the future. The nested logit model shows that not all variables significantly influence travel mode use, specifically related to rail-based public transport choice among motorcyclists. Meanwhile,parking distance insignificantly influences the choice of all travel modes except cars among car users. The results also indicate that increasing parking tariffs insignificantly influences the likelihood of both motorcyclists and car users shifting to public transport. Motorcyclists and car users tend to continue using motorcycles but change parking locations with higher tariffs. Additionally, some shifts towards ride-hailing services and TransJakarta Bus Rapid Transit are found, meaning that there is potential for these alternatives to play a significant role in reducing private vehicle use. Based on the model results, additional push-based policies, such as the odd-even license plate rule, are necessary to effectively support the transition from private vehicle use to public transport. Implementing these policies is expected to significantly contribute to reducing traffic congestion and promoting a sustainable and resilient urban environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".