Uses of Social Media in Public Transportation: Summary of Findings from TCRP Synthesis SB-20
Bibliographic record
Abstract
Social media is a group of web-based applications that encourage users to interact with one another. Examples include Facebook, Twitter, and YouTube. This paper presents findings from TCRP Synthesis SB-20, Uses of Social Media in Public Transportation. The paper focuses on the findings from the survey conducted for the synthesis and the more detailed cases. Thirty-five public transportation agencies in the U.S. and Canada participated in an online survey, and six were selected for more detailed interviews. Most of the surveyed agencies used Twitter, Facebook, and YouTube. Twitter was frequently used for providing time-sensitive information like service alerts, while agencies selected both Twitter and Facebook for disseminating agency news, meeting and event notices, contests and promotions, and general service information. Communicating with current riders was the most important goal for agencies and also the area where they considered social media to be most effective. Survey respondents were especially likely to use these applications to reach everyday riders, young adults and students. At the other end of the spectrum, agencies considered social media to be least effective for reaching seniors and low-income communities. Surveyed agencies stressed the importance of integrating social media with more traditional forms of rider communication and valued social media for providing unfiltered customer feedback. They reminded agencies to obtain the necessary internal approvals before moving forward and to understand the staffing requirements. Finally, social media can be a moving target and the challenge is to stay flexible, expect the unexpected, and adapt accordingly.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".