Modeling Taxi Trip Generation Using GPS Data: The Montreal Case
Bibliographic record
Abstract
With the emergence of new transport alternatives in the last decade, the taxi industry is struggling to keep its market share. Technological shift is one key element: taxi companies are implementing on-board GPS devices that produce large sets of data useful for strategic planning. There is currently no systematic processing of the available data to understand travel demand and this affects the ability to optimize the service. This article uses a month of GPS data from 1,000 taxis operating in Montreal to develop a taxi trip generation model. Daily taxi trips from and to each census tract are modeled using a multiple regression model in which variables describing demographic composition, land use features, transit availability and weather are used as explanatory variables. The performance of the final models is a R² =0.3300 for pickups and R² =0.5545 for drop-offs. Results reveal that the variables having the greater impact on taxi trips generation are income, age, transit access time, number of parking spots and, only affecting the drop-offs, job types. The research also provides a comparison between estimates of taxi trips using the 2008 large-scale Origin-Destination survey conducted in Montreal and the GPS data. The comparison reveals that some patterns are similar (temporal and distance distributions) but that the amount of trips differs a lot. It is not possible to draw full conclusion since the available taxi data are partials and that the survey only collects data from residents. Further analysis need to be conducted.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".