Relationship between Bikeshare and Transit: Evidence from the BIXI system in Montreal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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) | 0.000 | 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 it