Enabling Car-Free Living: Shared Micromobility and Public Transit Interactions in Chicago
Bibliographic record
Abstract
Shared micromobility/bikeshare services and public transit both offer travel alternatives to the automobile in urban areas. While these services might be viewed as competitors in the urban mobility space, this thesis argues that each benefits from the other as part of a “package of options” available to the car-free or car-lite urban resident that together provide a comprehensive replacement for auto-mobility. This work centers on the Chicago mobility context. It compares shared micromobility systems in Chicago, Los Angeles, Austin, Pittsburgh, and Washington, D.C., each of which have varying levels of transit integration, ridership, ownership models, and fares. It finds that transit agency ownership of shared micromobility systems appears not to be a panacea and that truly integrated fares are not present even in agency-owned systems. It also finds that lower fares are present in systems with greater levels of public subsidy, regardless of the ownership model. The second part of the thesis characterizes the specific interactions between Divvy, Chicago’s main scooter- and bikeshare system, and the Chicago Transit Authority (CTA). It tests the suitability of novel data sources, including CCTV footage and CTA farecard transactions, for inferring transfers between the two systems and finds that existing spatiotemporal inference methods do not capture the wide heterogeneity in transfer rates among rail stations. Although Divvy has stations near most CTA rail stations, there is room for improvement in the rapidity of these transfers. Using GIS and open-source routing tools, the thesis finds an average walk time of 2.1 minutes from CTA entrances to the nearest Divvy station and suggests high-priority relocations. The third part of the thesis presents preliminary results from a survey of Chicago-area residents probing their attitudes and behaviors regarding shared micromobility and public transit. The survey results showed some evidence of complementary use between the two modes. The thesis concludes with a set of recommendations for the CTA regarding improvements in its integration with Divvy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".