Humanizing Transit Data: Connecting Customer Experience Statistics to Individuals’ Unique Transit Stories
Bibliographic record
Abstract
Transit agencies often collect valuable information about their customers, through opinion and behavior surveys that assess travel experience and customer needs. The results of these questionnaires can be used to gain a representative snapshot of the behavior and opinions of a transit agency’s customer base. These assessments are often based on large sample sizes and are therefore useful for understanding broad trends related to users’ overall travel experience. However, these large-scale analyses generally do not capture the important and rich nuances that individuals experience while in a transit station, or on-board a train, conventional bus, streetcar, light rail, subway, or a paratransit vehicle. The purpose of this paper is to demonstrate how transit agencies can gain a better understanding of paratransit customers’ experiences during their interactions with paratransit and conventional transit services. Using data from the Toronto Transit Commissions’ paratransit division and the results of in-person customer interviews, a five-step mixed-method approach for mapping paratransit customers’ travel experiences is developed. Specifically, the aggregate analyses of customers’ experiences and opinions which are derived from agency-wide customer satisfaction surveys are combined with the information obtained through in-person discussions. Four example customer journey maps (CJMs) are presented, and findings demonstrate that by using CJMs, transit agencies can gain a broad understanding of their customer base while also understanding the emotions, needs, desires, and stories of individual transit users.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".