Development and transferability of advanced econometric models of bikesharing demand in urban settings
Bibliographic record
Abstract
Bikesharing systems (BSS) are becoming increasingly popular in urban areas around the world, as demonstrated by the rapid growth of both the number and the size of these systems in recent years. Understanding and predicting BSS usage patterns is complex, especially because these patterns are often tied to local factors. This thesis aims to contribute to the existing literature on BSS in two ways. First, an econometric model featuring bicycle availability at a station level as a direct metric of analysis is developed. This behaviorally quantitative model accounts for the influence of temporal, meteorological, bicycle infastructure, built environment and land-use attributes on bicycle availability. More specifically, an ordered regression model-panel mixed generalized ordered logit model-is estimated to accommodate for the influence of exogenous variables and station level unobserved factors. The model estimation is undertaen using BIXI-Montreal data from the summer of 2012. The results show BIXI is used more in the afternoon than in the morning, dense areas tend to be associated with lower availability levels, and interactions of time of day with land use impact avialability. The estimated model is validated using a old-out sample of data from the summer of 2013. The results clearly highlight the satisfactory performance of the proposed framework. The model developed can be employed by BSS operators to arrive at hourly system state prediciton and used for rebalancing operations. To illustrate its applicability, an availability prediction exercise is also undertaken. Second, a BSS model transferability exercise is conducted using data from Montreal - to data from New York and comparing the influence of temporal, meterological, bicylce infastructure, built environment and land-use variables on BSS usage in these two cities. Results show significant overlap in the influence of weather variables, bicycle infastructure, and several land-use attributes. However, temporal trends-especially weekend usage patterns-are very different in both cities. Overall, our results are promising for the development of transferable models of bicycle flows in urban areas.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".