Predictive Analytics for COVID-19 Social Distancing
Abstract
The COVID-19 pandemic has disrupted the lives of millions across the globe. In Singapore, promoting safe distancing by managing crowds in public areas have been the cornerstone of containing the community spread of the virus. One of the most important solutions to maintain social distancing is to monitor the crowdedness of indoor and outdoor points of interest. Using Nanyang Technological University (NTU) as a testbed, we develop and deploy a platform that provides live and predicted crowd counts for key locations on campus to help users plan their trips in an informed manner, so as to mitigate the risk of community transmission.
Cite
Text
Teng et al. "Predictive Analytics for COVID-19 Social Distancing." International Joint Conference on Artificial Intelligence, 2021. doi:10.24963/IJCAI.2021/716Markdown
[Teng et al. "Predictive Analytics for COVID-19 Social Distancing." International Joint Conference on Artificial Intelligence, 2021.](https://mlanthology.org/ijcai/2021/teng2021ijcai-predictive/) doi:10.24963/IJCAI.2021/716BibTeX
@inproceedings{teng2021ijcai-predictive,
title = {{Predictive Analytics for COVID-19 Social Distancing}},
author = {Teng, Harold Ze Chie and Jiang, Hongchao and Ho, Xuan Rong Zane and Lim, Wei Yang Bryan and Ng, Jer Shyuan and Yu, Han and Xiong, Zehui and Niyato, Dusit and Miao, Chunyan},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2021},
pages = {5016-5019},
doi = {10.24963/IJCAI.2021/716},
url = {https://mlanthology.org/ijcai/2021/teng2021ijcai-predictive/}
}