Bikeshare Users on a Budget? Trip Chaining Analysis of Bikeshare User Groups in Chicago
Bibliographic record
Abstract
This analysis focuses on a smartphone app known as “Transit” that is used to unlock shared bicycles in Chicago. Data from the app were utilized in a three-part analysis. First, Transit app bikeshare usage patterns were compared with system-wide bikeshare utilization using publicly available data. The results revealed that hourly usage on weekdays generally follows classical peaked commuting patterns; however, daily usage reached its highest level on weekends. This suggests that there may be large numbers of both commuting and recreational users. The second part aimed to identify distinct user groups via cluster analysis; the results revealed six different clusters: (1) commuters, (2) utility users, (3) leisure users, (4) infrequent commuters, (5) weekday visitors, and (6) weekend visitors. The group unlocking the most shared bikes (45.58% of all Transit app unlocks) was commuters, who represent 10% of Transit app bikeshare users. The third part proposed a trip chaining algorithm to identify “trip chaining bikers.” This term refers to bikeshare users who return a shared bicycle and immediately check out another, presumably to avoid paying extra usage fees for trips over 30 min. The algorithm revealed that 27.3% of Transit app bikeshare users exhibited this type of “bike chaining” behavior. However, this varied substantially between user groups; notably, 66% of Transit app bikeshare users identified as commuters made one or more bike chaining unlocks. The implications are important for bikeshare providers to understand the impact of pricing policies, particularly in encouraging the turn-over of bicycles.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".