Enhancing Wayfinding in Chennai Metro: Insights from Passenger Feedback
Bibliographic record
Abstract
Metro systems are now essential for urban mobility due to the rapid urbanization of the world, but many users still find it difficult to navigate these complex environments. Clear and inclusive wayfinding is not only critical for improving passenger ease of commuting. It is also for certifying security, effectiveness, and reasonable access to public transport. In Chennai, like in other cities where the transit usage has increased rapidly in recent years, user-centered navigation design remains unknown. In order to categorise design limitations and recommend user-centered improvements, this study examines passenger feedback to investigate the wayfinding experience in Chennai Metro stations. The research builds on international frameworks such as Metrolinx (Canada), APTA (USA), and MTC (USA), while positioning results within the local socio-cultural context. Using a mixed-methods approach, the study evaluates overall navigation satisfaction through integrating quantitative survey data with qualitative responses from 88 respondents. The investigation apprehended demographic variations across age, gender, and travel frequency. The open-ended responses delivered understandings into signage clarity, digital tools, and accessibility. The results show serious deficiencies in real-time navigation assistance, digital integration, multilingual signage, and accessibility for people with disabilities. Starting from the mobile apps, interactive kiosks, restroom signage, and multilingual guidance, the need varies by age and gender. These results point to demographic-exact needs that should inform design revisions. The study highlights the need for inclusive design principles by comparing Chennai Metro's current practices to international standards like Metrolinx (Canada), APTA (USA), and MTC (USA). The use of universally recognizable symbols, enhanced station mapping, tactile and Braille signage, and participatory design—which involves passengers in the evaluation of signage—are among the recommendations. By providing scalable methods for improving wayfinding in expanding metropolitan networks, the study adds to the broader conversation on transit accessibility and user experience.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".