Evaluating the Influence of Ride Sourcing Services on Travel Patterns and Transportation Networks in Toronto
Bibliographic record
Abstract
This study provides a detailed analysis of the evolving impact of ride-sourcing platforms such as Uber and Lyft on travel behavior and mobility patterns within Toronto. Utilizing origindestination data from approximately 100 million trips recorded between 2016 and 2019, we examine spatial and temporal trends in ride-sourcing activities. Our methodology integrates these data with car travel times, public transportation networks, and city regulations to assess the influence of ride-sourcing on overall traffic flow, public transit usage, and cyclist safety. Through route analysis and trip linkage techniques, we estimate that ride-sourcing vehicles contributed to 5–8% of the total daily vehicle kilometers traveled (VKT) in September 2018—approximately double the figures from October 2016. While ride-sourcing activity surged, our findings indicate that downtown travel times remained largely stable. Additionally, curbside pick-up and drop-off patterns highlight the necessity for improved curb management strategies to enhance safety and efficiency. These insights provide a foundation for future policy decisions regarding urban mobility and ride-sourcing regulation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| 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) | 0.002 | 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".