2019-10-15-qiu19a.md

title	crossref	abstract	layout	series	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	publisher	container-title	volume	genre	issued	pdf	extras
Prediction of Crowd Flow in City Complex with Missing Data	acml19	Crowd flow forecasting plays an important role in risk assessment and public safety. It is a difficult task due to complex spatial-temporal dependencies as well as missing values in data. A number of models are proposed to predict crowd flow on city-scale, yet the missing pattern in city complex environment is seldomly considered. We propose a crowd flow forecasting model, Imputed Spatial-Temporal Convolution network(ISTC) to accurately predict the crowd flow in large complex buildings. ISTC uses convolution layers, whose structures are configured by graphs, to model the spatial-temporal correlations. Meanwhile ISTC adds imputation layers to handle the missing data. We demonstrate our model on several real data sets collected from sensors in a large six-floor commercial complex building. The results show that ISTC outperforms the baseline methods and is capable of handling data with as much as 40% missing data.	inproceedings	Proceedings of Machine Learning Research	qiu19a	0	Prediction of Crowd Flow in City Complex with Missing Data	758	773	758-773	758	false	Qiu, Shiyang and Xu, Peng and Zheng, Wei and Wang, Junjie and Yu, Guo and Hou, Mingyao and Liu, Hengchang	given family Shiyang Qiu given family Peng Xu given family Wei Zheng given family Junjie Wang given family Guo Yu given family Mingyao Hou given family Hengchang Liu	2019-10-15		PMLR	Proceedings of The Eleventh Asian Conference on Machine Learning	101	inproceedings	date-parts 2019 10 15	http://proceedings.mlr.press/v101/qiu19a/qiu19a.pdf