Modelling Annual Bike Share Ridership at Hubs with Bike Share Expansion in Mind
Bibliographic record
Abstract
Public bike share systems have been recognized as an effective way to promote active and sustainable public transportation. With the health benefits of bike share becoming better understood, North American cities have continued to invest in cycling infrastructure and impose new policies to not only encourage the usage of bike share systems but also expand their operations to new cities. The city of Hamilton, Ontario, implemented its own bike share system in March 2015. Using the system’s global positioning system (GPS) data for annually aggregated trip departures, arrivals, and totals in 2017, this research explores various environment factors that have an impact on users’ bike share usage at hub level. Nine predictive linear regression models were developed for three different scenarios depending on the type of hubs and members for trip departures, arrivals, and totals. In terms of variance explained across the core service area, the models suggested the main factors that attract users were distance to McMaster University and the number of racks available at hubs. Furthermore, the working population and distance to the Central Business District and the closest bike lane in the immediate vicinity (200 m buffer) also played important roles as contributing factors. Based on the primary predictors, this research takes one step further and estimates potential trips at candidate sites to inform future expansion of public bike share system. The candidate locations were created on appropriate land uses by applying a continuous surface of regularly shaped cells, a hexagonal tessellation, on the area of interest. The estimated potential usage at candidate sites demonstrated that the east part of the city should be targeted for future bike share expansion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.106 | 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".