Discrepancies between initial applicants and actual users of a new microtransit service: The case of FlexRide Milwaukee
Bibliographic record
Abstract
• On-demand, microtransit service helped low-income workers reach suburban jobs. • High use by people who were Black, without cars, under age 35. • Actual use by low-income workers and women was lower than initial applications. • Actual use by unemployed, cash users, and third-shift workers was lower than initial applications. Amid the long-term trend of declining transit ridership in the US, there is growing interest in leveraging on-demand microtransit to complement existing transit service, particularly to improve the accessibility of autoless riders to jobs, health care, or other activities in lower-density areas that are inefficient to serve with fixed-route transit. However, relatively little is known about whether these new microtransit services effectively serve their intended user groups. We studied FlexRide Milwaukee, an on-demand, microtransit service created to connect low-income workers and job seekers from predominantly Black neighborhoods on the northwest side of the City of Milwaukee, Wisconsin with predominately white, employment-rich suburbs. This employment-focused service helped FlexRide users reach jobs located in suburbs with limited transit service. Analyzing trip data from the FlexRide pilot study period (April 18 to September 30, 2022), we profiled 713 applicants who showed initial interest in trying the service. Ultimately, 428 of these applicants signed up as participants to use the service and 128 of them actually used FlexRide Milwaukee (80 frequent and 48 occasional riders). Descriptive analyses and logistic regression models showed that participants used FlexRide more often if they were already employed, Black, or did not have access to a household vehicle. However, compared with their high levels of initial interest, people with low incomes and women underused FlexRide Milwaukee. People who were unemployed, used cash, or worked third shift also underused the service. More research is needed to understand the latent demand for this type of microtransit service among these groups.
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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 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".