Relationship between Bikeshare and Transit: Evidence from the BIXI system in Montreal
Bibliographic record
Abstract
The purpose of this study is to identify the relationship between bikesharing ridership and transit-related variables in Montreal, Canada. Preliminary analysis of BIXI trip logs in 2016 shows there are similarities between the spatial distribution patterns of BIXI ridership and metro ridership, and temporal patterns of bikesharing trips indicate that some annual members are using BIXI as one of their daily commuting modes. The OLS model and the spatial lag model are used to investigate the relationship between bikesharing ridership and independent variables in transit service levels, connectivity and transport-related demographics. The 5-minute walk time buffer is chosen as the measure for the dependent variable and 12 independent variables. The log-log transformed OLS model has a 75% goodness of fit and identifies a number of variables significantly associated with BIXI ridership. In the stepwise regression model, significant variables are metro ridership, street network connectivity, daytime population, number of people that travel to work by walking, number of people that travel to work by bicycle, population aged 20 to 24 (negative), and average household expenditure on private transportation (negative). For 10% increase in metro ridership, a 4.16% increase in BIXI ridership is expected. The highly significant Moran's I indicates strong spatial autocorrelation. LM and robust LM tests justify our choice of the spatial lag model. This study uses both GeoDa and Stata to run the spatial lag model, in order to test the model with different spatial weighting matrices. Results show that the structure of the inverse distance matrix in Stata is more suitable for representing spatial interactions in bikesharing trips between metro buffers. Metro ridership is highly significant and is the second strongest predictor. After removing spatial effects, 10 percent increase in metro ridership is associated with 5.44 percent increase in BIXI ridership. In both the OLS model and the spatial lag model, metro ridership, street network connectivity, number of people that travel to work by walking and number of people that travel to work by bicycle are positively associated with BIXI ridership. This result indicates that transit riders and active travelers are potential users of bikesharing services. Through collaborative and intermodal planning, the relationship between bikeshare and transit could be strengthened and bikeshare could fulfill its potential to be a powerful complement to public transit.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".