What Can Publicly Available API Data Tell Us about Supply and Demand for New Mobility Services?
Bibliographic record
Abstract
Better understanding of the impacts of new mobility services (NMS) is needed to inform evidence-based policy, but cities and researchers are hindered by a lack of access to detailed system data. Application programming interface (API) services can be a medium for real-time data sharing and access, and have been used for data collection in the past, but the literature lacks a systematic examination of the potential value of publicly available API data for extracting policy-relevant information, specifically supply and demand, on NMS. The objectives of this study are: 1) to catalogue all the publicly available API data streams for NMS in three major cities known as the Cascadia Corridor (Vancouver, British Columbia; Seattle, Washington; and Portland, Oregon); 2) to create, apply, and share web data extraction tools (Python scripts) for each API; and 3) to assess the usefulness of the extracted data in quantifying supply and demand for each service. Results reveal some measures of supply and demand that can be extracted from API data and be useful in future analysis (mostly for bikeshare and carshare services, not ridesourcing). However, important information on supply and demand of most of the NMS in these cities cannot be obtained through API data extraction. Stronger open data policies for mobility services are therefore needed if policymakers want to obtain useful and independent insights on the usage of these services.
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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 | 0.011 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.039 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".