Modeling for Forecasting Sharing Bike Demand in London
Bibliographic record
Abstract
Bike-sharing systems have become a key component of sustainable urban mobility; however, predicting rental demand remains a major challenge due to temporal, environmental, and contextual variability. This study investigates bike-sharing demand in London using a comprehensive dataset of hourly rental records from January 2015 to January 2017. After data preprocessing and aggregation, several modeling methods were applied, including multiple linear regression, moving average trend analysis, a Seasonal Autoregressive Integrated Moving Average (SARIMA) model, and regression trees. Among these, the linear regression model identifies variables such as season, holidays, and weekends as significantly influential, highlighting explanatory factors beyond pure time-series dynamics. The moving average analysis reveals a clear seasonal pattern: demand peaks in summer and drops in winter, reflecting user behavior. The SARIMA model captures long-term seasonality and provides reasonable predictive accuracy. The regression tree model emphasizes the combined impact of temperature, humidity, wind speed, and time-related factors, offering intuitive rule-based insights into environmental effects. Overall, these approaches demonstrate the interplay between temporal and environmental variables in shaping shared bike demand. The findings provide data-driven insights that support dynamic pricing strategies, better resource allocation, and smarter integration of bike-sharing systems into urban transportation planning.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".