Effects of Built Environment and Weather on Bike Sharing Demand: Station Level Analysis of Commercial Bike Sharing in Toronto
Bibliographic record
Abstract
Bike Share Toronto is Canada’s second largest public bike share system. Bike Share Toronto provides a unique case study as it is one of the few bike share programs in a North American city that experiences severe cold climates and operates throughout the entire year. Using year-round real time trip data, this study analyzes the factors affecting Toronto’s bike share ridership. A comprehensive spatial analysis is performed and three regression models are developed at the station level. Results of the trip attraction and generation models provide meaningful insights on the influence of socio-demographic attributes, land use and built environment, as well as different weather measures on bicycle share ridership. The developed models can be used to assist policy makers and city planners to predict the monthly trip activity at potential station locations. A station pair (origin-destination) regression model is developed based on station to station paths’ level of service attributes along with other zonal level factors. Results show that station-to-station distance and the number of intersections with major roads have negative impacts on bike share ridership. In addition, for a given origin-destination pair, the higher the percentage of bicycle infrastructure with respect to the total route length, the higher the corresponding ridership. This model can be used to predict the trip distribution between station pairs based on the total trip activity at each station. The model can also be used to assess potential bike infrastructure development based on expected bicycle routes from/to potential station locations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".