Modeling bicycle choice behavior and its potential health impact: Case of first/last mile access to suburban rail
Bibliographic record
Abstract
Cycling offers a sustainable solution to urban mobility challenges, particularly in rapidly growing cities like Mumbai, where it remains an underutilized access mode to suburban rail due to infrastructure gaps and safety concerns. This study explores factors affecting suburban rail commuters’ willingness to cycle for first-mile connectivity, using Ordered Logit and Integrated Choice Latent Variable models on survey data collected from 480 commuters across 20 stations along the central line in Mumbai, India. The survey examines socio-economic traits, travel habits, and attitudes toward cycling, with a focus on four infrastructural aspects: dedicated bike lanes, intersection treatments, bike-sharing services, and secure parking, alongside three latent factors—perceived benefits, physical barriers, and safety/security barriers. Findings reveal that only 8% of suburban rail users currently cycle to stations. Younger, lower-income individuals without motorized vehicles show a greater inclination to adopt cycling. However, broader uptake is hindered by safety issues, poor infrastructure, and insufficient secure parking. Health assessments using WHO’s Health Economic Assessment Tool estimate that the best scenario, with 54% of users cycling 4 km daily, could prevent around 5483 premature deaths annually. The study recommends implementing protected bike lanes, improved intersection designs, secure bike parking, and affordable bike-sharing at select stations to promote cycling as a viable access mode. Addressing these infrastructure needs can create a sustainable, health-promoting urban transport system in cities like Mumbai.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".