Insight into Montreal's Bikesharing System
Bibliographic record
Abstract
In the summer of 2009, Montreal launched the BIXI. Within a few weeks, it has gained popular attention and phase 2 of the deployment, which was initially planned in 2010 was launced. This year, with its 5,000 bikes and increasing number of members, the system cannot solely rely on good intuition to increase its performance. This paper provides insight into the quantitative life of a fast-growing system. It proposes a set of indicators aiming to objectively describe the state of the main objects of the system in space and time: bikes, stations, members, anchor points and trips. It relies on data gathered during three months of operation during the summer of 2009 (July, August and September). First analyses show that regular members and occasional users have different behaviors, the former accounting for 67.8% of the studied trips. Results also show that bikesharers do between 1.7 and 2.1 trips per day. On typical weekdays, 72.6% of the trips are due to members while this proportion decreases during week-ends. Results show that stations face various states that require balancing transfers and that these seem to be related to spatial location. Systematic and continuous assessment of object-related indicators will help better understand supply and demand and propose efficient operational and planning strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
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".