Bibliographic record
Abstract
This technical report presents the work undertaken in support of the City of Toronto’s Vehicle for Hire Bylaw Review by the University of Toronto Transportation Research Institute (UTTRI) to analyze the patterns in taxi usage over a 20-year period as recorded in the five most recent Transportation Tomorrow Surveys (TTSs): 1996 TTS; 2001 TTS; 2006 TTS; 2011 TTS; and 2016 TTS. The trip records in the TTS data have socioeconomic attributes of trip-makers (e.g., age, sex, etc.), their household characteristics (household size, number of vehicles owned, etc.) and trip attributes attached (Data Management Group – Reports, n.d.). They therefore provide a statistically representative description of taxi-users and their reasons for travel along with the spatiotemporal attributes of the trips. Previous studies have shown that the profiles of Uber-users and taxi-users are considerably different (Habib, 2019; Ozonder and Miller, 2019). Thus, the purpose of this study as documented in this report is to identify changes or stabilities in the taxi-user group and their trip patterns by comparing various distributions through a longitudinal analysis. This report is one of a series of project reports by the UTTRI team. It complements Report No. 1 (which examined PTC usage as reported in the 2016 TTS) and Report No. 2 (which compared PTC usage as reported in the 2016 TTS with the VfH PTC data). The rest of the report is organized as follows. Section 2 reports the results of taxi time-series analysis in three parts: in the first part, it discusses the attributes of trip-makers; in the second part, it compares distributions in household attributes over the years; in the third part, it explains the analysis results of the trip patterns. Section 3 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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.019 |
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".