Report 1: Analysis of PTC Usage as Recorded in the 2016 TTS
Bibliographic record
Abstract
This technical report presents the work undertaken by the University of Toronto Transportation Research Institute (UTTRI) to analyze the usage of services provided by Private Transportation Companies (PTCs) as recorded in the 2016 Transportation Tomorrow Survey (TTS) in support of the City of Toronto’s Vehicle for Hire Bylaw Review. The 2016 TTS conducted in the fall (September-December) 2016 time period collected information on Uber trips as an explicit mode of travel (Ashby, 2018; Miller, et al., 2019). The trip records in the data have socioeconomic attributes of trip-makers (e.g., age, sex, income class, etc.), their household characteristics (household size, number of vehicles owned, etc.) and trip purposes attached (Data Management Group – Reports, n.d.). They, therefore, provide a statistically representative description of the users of the services provided by PTCs and their reasons for travel along with time and start/end locations of the trips. Despite the relatively modest market penetration of PTCs (only Uber at the time) in the City during the fall of 2016, TTS provides a considerably richer description of PTC travel and trip-makers than can be obtained from the PTC records alone, since PTC records do not have user-attributes or trip purposes attached (Miller, et al., 2019). This report is one of the deliverables by the UTTRI team, where the main focus is on the demographics of the trip-makers and their household characteristics. PTC trip records from the same September-December 2016 time period are compared to the 2016 TTS PTC trip records to investigate both spatial and temporal usage of the services provide by PTCs in a complementary report (Project Report No. 2), hence, trips are not discussed in detail in this report. The rest of the report is organized as follows. Section 2 reports the results of the analysis of descriptive statistics of PTC user attributes and their household characteristics, along with a concise overview on trips. Section 3 reviews three studies conducted using the 2016 TTS with a focus on PTC usage. Section 4 concludes the report with a summary of findings.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".