Evaluation of new mobility services with API data : investigating equity of Uber’s wheelchair accessible service
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 have been used for data collection in the past. However, 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. This thesis is comprised of two main parts. The objectives of part 1 are 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), to create, apply, and share web data extraction tools (Python scripts) for each API, and to assess the usefulness of the extracted data in quantifying supply and demand for each service. The objective of part 2 is to use the data extracted in part 1 to assess the equity performance of Uber’s wheelchair accessible service, UberWAV, by itself and in comparison to the standard Uber service, UberX. In part 2 the temporal and spatial distributions of the availability and accessibility of each service is investigated. Results of part 1 reveal some measures of supply and demand that can be extracted from API data and useful in future analysis. 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. Results of part 2 show that unlike UberX which is almost universally available, UberWAV is only available 60% of the time with an average wait time of 16 minutes on average (4 times that of UberX). The distributional analysis shows no inequitable distribution of availability or accessibility of UberWAV in Portland, Oregon with regards to income, and race. To make UberWAV more available and accessible, cities must enforce stronger licensing schemes to ridesourcing companies.
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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 | 0.015 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".