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Smart city article details

Title	Periodic Residual Learning For Crowd Flow Forecasting
ID_Doc	41869
Authors	Wang C.; Liang Y.; Tan G.
Year	2022
Published	GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
DOI	http://dx.doi.org/10.1145/3557915.3560947
Abstract	Crowd flow forecasting, which aims to predict the crowds entering or leaving certain regions, is a fundamental task in smart cities. One of the key properties of crowd flow data is periodicity: a pattern that occurs at regular time intervals, such as a weekly pattern. To capture such periodicity, existing studies either fuse the periodic hidden states into channels for networks to learn or apply extra periodic strategies to the network architecture. In this paper, we devise a novel periodic residual learning network (PRNet) for a better modeling of periodicity in crowd flow data. Unlike existing methods, PRNet frames the crowd flow forecasting as a periodic residual learning problem by modeling the variation between the inputs (the previous time period) and the outputs (the future time period). Compared to directly predicting crowd flows that are highly dynamic, learning more stationary deviation is much easier, which thus facilitates the model training. Besides, the learned variation enables the network to produce the residual between future conditions and its corresponding weekly observations at each time interval, and therefore contributes to substantially more accurate multi-step ahead predictions. Extensive experiments show that PR-Net can be easily integrated into existing models to enhance their predictive performance. © 2022 ACM.
Author Keywords	convolutional neural networks; crowd flow; deep learning; periodic residual; spatio-temporal data mining; urban computing

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