Bibliographic record
Abstract
Taxi is a collective transportation mode that is suffering from under examination. Still, it can certainly contribute to the adoption of more sustainable travel behaviours as part of co-mobility strategies to reduce dependency towards the private car and all the negative impacts it has. This paper focuses on the role of taxis in the daily travel behaviours of Montrealers. Using a global positioning system (GPS) dataset over one month of operation (October 2011) of a fleet of 968 taxis (app. 22% of the entire fleet of the region), various descriptive analysis are conducted to understand how, when and where the taxis are used. Analysis is conducted at various levels: first, a single taxi is examined and then indicators are generalised to the entire set of data namely trip distance, mean duration, runs per day. The study also reveals important spatial and temporal trends: 95% of the runs are conducted during weekdays, between 6 am and 9 am and 32% of the origins of the runs are concentrated within an are a of 12.3 km² (2.5% of the Montreal Island). Hence, incidence of various factors such as weather or public holiday on usage is examined. For instance, the authors observe that during rainy days, the number of runs increases significantly and that their average length decreases. Specific studies of a main trip generator, the international airport, and of the central business district (CBD), are also conducted confirming the as symmetry of trip ends namely generated by the way the industry is managed i.e. with permits linked to specific zones. Based on spatial - temporal structure of taxi travel demand, we conclude that this transportation mode is often used for constraint trips (work) or to travel when other services are not in operation (at night for instance).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".