Efficient Spatial-Temporal Rebalancing of Shareable Bikes (Student Abstract)

AAAI 2020 pp. 13775-13776

doi:10.1609/AAAI.V34I10.7159 /aaai/2020/deng2020aaai-efficient/

Abstract

Bike sharing systems are popular worldwide now. However, these systems are facing a problem - rebalancing of shareable bikes among different docking stations. To address this challenge, we propose an approach for the spatial-temporal rebalancing of shareable bikes which allows domain experts to optimize the rebalancing operation with their knowledge and preferences without relying on learning by trial-and-error.

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Text

Deng et al. "Efficient Spatial-Temporal Rebalancing of Shareable Bikes (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I10.7159

Markdown

[Deng et al. "Efficient Spatial-Temporal Rebalancing of Shareable Bikes (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/deng2020aaai-efficient/) doi:10.1609/AAAI.V34I10.7159

BibTeX

@inproceedings{deng2020aaai-efficient,
  title     = {{Efficient Spatial-Temporal Rebalancing of Shareable Bikes (Student Abstract)}},
  author    = {Deng, Zichao and Tu, Anqi and Liu, Zelei and Yu, Han},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {13775-13776},
  doi       = {10.1609/AAAI.V34I10.7159},
  url       = {https://mlanthology.org/aaai/2020/deng2020aaai-efficient/}
}