Rebalancing Docked Bicycle Sharing System with Approximate Dynamic Programming and Reinforcement Learning
Bibliographic record
Abstract
The bicycle, an active transportation mode, has received increasing attention as an alternative in urban environments worldwide. However, effectively managing the stock levels of rental bicycles at each station is challenging as demand levels vary with time, particularly when users are allowed to return bicycles at any station. There is a need for system-wide management of bicycle stock levels by transporting available bicycles from one station to another. In this study, a bicycle rebalancing model based on a Markov decision process (MDP) is developed using a real-time dynamic programming method and reinforcement learning considering dynamic system characteristics. The pickup and return demands are stochastic and continuously changing. As a result, the proposed framework suggests the best operation option every 10 min based on the realized system variables and future demands predicted by the random forest method, minimizing the expected unmet demand. Moreover, we adopt custom prioritizing strategies to reduce the number of action candidates for the operator and the computational complexity for practicality in the MDP framework. Numerical experiments demonstrate that the proposed model outperforms existing methods, such as short-term rebalancing and static lookahead policies. Among the suggested prioritizing strategies, focusing on stations with a larger error in demand prediction was found to be the most effective. Additionally, the effects of various safety buffers were examined.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".