Spatial Characteristics of Transit-Integrated Ridesourcing Trips and Their Competitiveness with Transit and Walking Alternatives
Bibliographic record
Abstract
Municipal transit agencies are exploring integrations with ridesourcing vehicles to extend the reach of their fixed-route transit networks. Ridesourcing integrations have been piloted in some regions, but these pilots tend not to be externally evaluated because of an inability to access the trip data. The primary objective of this research was to determine the types of trips passengers are taking through a transit-integrated ridesourcing pilot, and their competitiveness with transit and walking alternatives. The analysis focused on the 903 Flex pilot operated by the Region of Waterloo, Ontario, Canada. A set of 585 completed ridesourcing trips (rides) were studied and compared with the alternative transit and walking trips. Each ride was assigned a type, based on its proximity to transit and walking alternatives, for calculation and comparison of trip attributes. Terminology for types of rides is introduced and the categorization process applied to the ridesourcing pilot. Trip categories include: feeders, transit replacements, inconvenient trips, and remote trips. Results suggest that most trips in the study operated on an indirect feeder-like system (65%), which brought passengers between virtual ridesourcing stops and a transit stop, but not the transit stop closest to them. The alternative fixed-route transit trips mainly operated on 30-min headways, and alternative walking times were often long. The trips were found to mostly support or maintain transit usage, but the transit agency should be cautious of cases in which rides occur alongside transit (18%), instead of bringing people to it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".