Predicting Carsharing Station-Based Trip Generation Using a Growth Model
Why this work is in the frame
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Bibliographic record
Abstract
Carsharing is a service that allows members to rent cars for a limited time. In Montreal, Quebec, Canada, two types of services exist: a station-based and a free-floating service. This paper proposes a trip generation model for the station-based service of the Communauto carsharing operator for 2016. To better understand relations between space and time, a growth model is used, considering these factors at different levels. For example, some factors can impact all stations similarly, while other factors may impact each station differently. Thus, this model allows to consider both spatial and temporal variables allowing more precise estimations. The aim of this research is to estimate carsharing trip generation at the station level and provide insights into the impacts of implementing new stations on demand. A step-by-step approach was adopted to define the best predictive model for the use of carsharing stations. While more complex model formulations need to be tested to enhance the analysis, the final growth model obtained indicates that, in addition to the number of vehicles available at the stations, several exogenous factors have a significant impact on the trip generation rate of a carsharing station. For instance, the model shows that demographic factors, walkability level and number of bus stations have significant impacts on the use of carsharing stations.
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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 | 0.001 |
| 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