Cluster-Based Destination Prediction in Bike Sharing System-Reference-Cited by-同舟云学术

Cluster-Based Destination Prediction in Bike Sharing System

Published:2018 Issue: Volume: Page:
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Container-title:Proceedings of the 2018 Artificial Intelligence and Cloud Computing Conference on ZZZ - AICCC '18
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Author:

Dai Pengcheng¹,Song Changxiong¹,Lin Huiping¹,Jia Pei¹,Xu Zhipeng¹

Affiliation:

1. School of Software and Microelectronics, Peking University, Beijing, China

Publisher

ACM Press

Reference31 articles.

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2. Froehlich, J., Neumann, J., & Oliver, N. (2009, July). Sensing and predicting the pulse of the city through shared bicycling. In IJCAI (Vol. 9, pp. 1420--1426).

3. Chen, L., Zhang, D., Wang, L., Yang, D., Ma, X., Li, S., ... & Jakubowicz, J. (2016, September). Dynamic cluster-based over-demand prediction in bike sharing systems. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 841--852). ACM.

4. Liu, J., Sun, L., Chen, W., & Xiong, H. (2016, August). Rebalancing bike sharing systems: A multi-source data smart optimization. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1005--1014). ACM.

5. Hu, J., Yang, Z., Shu, Y., Cheng, P., & Chen, J. (2017, November). Data-Driven Utilization-Aware Trip Advisor for Bike-Sharing Systems. In Data Mining (ICDM), 2017 IEEE International Conference on (pp. 167--176). IEEE.

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