Evaluating the impact of vertical accessibility performance to Bangkok mass transit stations on the travel behavior of elderly passengers
Bibliographic record
Abstract
The Bangkok Mass Transit System (BTS) sky-train is one of Bangkok's primary public transit modes to alleviate traffic congestion among many daily users. Regarding urban mobility, inclusivity is crucial. According to a 2016 World Bank report, by 2040, more than a quarter of the Thai population will be 65 or older. If the elderly could move around the city with greater ease, they would be able to participate more in society and have access to health care regardless of their age. However, news and reports demonstrated the BTS sky train's limited accessibility for persons with limited mobility. In this study, lifts and escalators are regarded as the primary alternatives to stairs for vertical circulation in stations. In terms of vertical egress and access to each station level, the performance of BTS stations in elderly passengers' accessibility was measured. The impact of station facilities' performance on the travel behaviour of the elderly was analysed using a quantitative approach. Three types of stations are categorised according to the dependability and functionality of their equipped facilities. The assessment revealed that nearly half of the stations are conditionally or limitedly accessible. They often use BTS for visits, shopping, recreation, and healthcare-related activities and mostly ride the BTS at non-rush hours. In addition, their choice of station is influenced by its closeness to their residence or its more accessible. Many older passengers who ride BTS alone stated that the seniors independently use the transit. Escalators are the most common means of accessing the station on all levels, while stairs are used to exit the stations. Although the chi-square test reveals that the ease of access to station platforms via escalators and elevators has no significant effect on the travel behaviour and satisfaction of elderly transit users, older people who do not own a private vehicle are more likely to use BTS if all stations have completed facilities. Despite this, findings indicate that age-friendly transit services and policies should consider seniors' well-being, travel convenience, and safety using a holistic design approach.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".