Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
8-2017
Keywords
Unit Original Taxi Demands, Prediction, Feature Engineering
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, Canada, 2017 August 13-17
First Page
1653
Last Page
1662
Identifier
10.1145/3097983.3098018
Publisher
ACM
City or Country
Halifax, Canada
Citation
TONG, Yongxin; CHEN, Yuqiang; ZHOU, Zimu; CHEN, Lei; WANG, Jie; YANG, Qiang; YE, Jieping; and LV, Weifeng.
The simpler the better: A unified approach to predicting original taxi demands on large-scale online platforms. (2017). Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, Canada, 2017 August 13-17. 1653-1662.
Available at: https://ink.library.smu.edu.sg/sis_research/4740
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3097983.3098018
