Organizational Reforms to Enhance the Metro Customer Experience: Case Studies of Agency Practices
Bibliographic record
Abstract
As public transit ridership declines in most North American cities, public transit agencies are seeking ways to remain an attractive commute option in people???s everyday lives. Agencies are shifting to a more customer-focused culture and are viewing themselves as a service industry that provides more than just a standardized service. They are doing this by enhancing the customer experience. Customer experience means the total experience of all touchpoints that make up a transit experience. This project examines possible organizational reforms that my client, Los Angeles County Metropolitan Transportation Authority (Metro), can implement to facilitate the development of programs that enhance a rider???s experience. While the project addresses the content of customer service programs, the focus is on recommendations related to organizational reform, as that is key to moving forward. Through the review of literature and transit agency case studies of Dallas Area Rapid Transit (DART), Toronto Transit Commission (TTC), and New Jersey Transit (NJT), I examine how public transit agencies are facilitating the development of customer experience programs and improving the direct interface with employees as a step towards enhancing the overall customer experience. This project provides Metro with high-level organizational reform recommendations pertaining to management and institutional culture to improve the customer experience, including encouraging interdepartmental coordination, simplifying processes, reimagining and strengthening the Social Media and Customer Care teams, adopting a customer-focused work culture, extending the employee recognition program, and enabling employees to succeed.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".