Prediction of Crowd Flow in City Complex with Missing Data

Shiyang Qiu, Peng Xu, Wei Zheng, Junjie Wang, Guo Yu, Mingyao Hou, Hengchang Liu
; Proceedings of The Eleventh Asian Conference on Machine Learning, PMLR 101:758-773, 2019.

Abstract

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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v101-qiu19a, title = {Prediction of Crowd Flow in City Complex with Missing Data}, author = {Qiu, Shiyang and Xu, Peng and Zheng, Wei and Wang, Junjie and Yu, Guo and Hou, Mingyao and Liu, Hengchang}, pages = {758--773}, year = {2019}, editor = {Wee Sun Lee and Taiji Suzuki}, volume = {101}, series = {Proceedings of Machine Learning Research}, address = {Nagoya, Japan}, month = {17--19 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v101/qiu19a/qiu19a.pdf}, url = {http://proceedings.mlr.press/v101/qiu19a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Prediction of Crowd Flow in City Complex with Missing Data %A Shiyang Qiu %A Peng Xu %A Wei Zheng %A Junjie Wang %A Guo Yu %A Mingyao Hou %A Hengchang Liu %B Proceedings of The Eleventh Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Wee Sun Lee %E Taiji Suzuki %F pmlr-v101-qiu19a %I PMLR %J Proceedings of Machine Learning Research %P 758--773 %U http://proceedings.mlr.press %V 101 %W PMLR %X 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.
APA
Qiu, S., Xu, P., Zheng, W., Wang, J., Yu, G., Hou, M. & Liu, H.. (2019). Prediction of Crowd Flow in City Complex with Missing Data. Proceedings of The Eleventh Asian Conference on Machine Learning, in PMLR 101:758-773

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