Unravelling experiences of transit captivity with time-geography: The case of commuters in Jakarta Metropolitan area
Bibliographic record
Abstract
Captive transit users are likely to be more vulnerable to public transport service disruptions than choice users. However, what precisely is a captive user? Sociodemographic characteristics have up till now been mainly used to make assumptions on who are captive and who are choice users. However, transit users with similar sociodemographic characteristics may have distinct life situations and spatiotemporal constraints. This study contributes to understanding public transport captivity by examining perceived captivity concerning experienced and measured spatiotemporal constraints, following time-geography theory. Based on a 2022 survey in the Greater Jakarta Metropolitan Area, two-way clustering based on perceived constraints was used to capture transit user segments. Ordinal regression analysis was then performed to examine the role of constraint perception segment membership in comparison to measurable sociodemographic factors and spatial instrumental capability constraints in explaining perceived transit captivity. Results revealed that perceived capability and coupling constraints are significant factors in defining transit user segments. Three clusters were identified: flexible commuters, commuters with responsibilities, and non-driving constrained commuters. The findings show that non-driving constrained commuters and commuters with responsibilities (to a lesser extent) are more captive than flexible commuters. Segmenting users based on an individual time-geography approach helps to accurately identify captive and choice users and the in-between groups. Results provide insights for policymakers about the most vulnerable groups. It helps to develop tailor-made strategies targeting different transit user segments with differing constraints to promote transit usage and reduce transport poverty.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".