PRNet: A Periodic Residual Learning Network for Crowd Flow Forecasting

• URL: http://arxiv.org/abs/2112.06132v1
• Date: Wed, 8 Dec 2021 12:04:27 GMT
• Title: PRNet: A Periodic Residual Learning Network for Crowd Flow Forecasting
• Authors: Chengxin Wang, Yuxuan Liang and Gary Tan
• Abstract summary: We devise a novel periodic residual learning network (PRNet) for better modeling the periodicity in crowd flow data. PRNet frames the crowd flow forecasting as a periodic residual learning problem by modeling the deviation between the input (the previous time period) and the output (the future time period) Experimental results on two real-world datasets demonstrate that PRNet outperforms the state-of-the-art methods in terms of both accuracy and robustness.
• Score: 8.50942649992681
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Crowd flow forecasting, e.g., predicting the crowds entering or leaving certain regions, is of great importance to real-world urban applications. 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 explicitly model it based on the periodic hidden states or implicitly learn it by feeding all periodic segments into neural networks. In this paper, we devise a novel periodic residual learning network (PRNet) for better modeling the periodicity in crowd flow data. Differing from existing methods, PRNet frames the crowd flow forecasting as a periodic residual learning problem by modeling the deviation between the input (the previous time period) and the output (the future time period). As compared to predicting highly dynamic crowd flows directly, learning such stationary deviation is much easier, which thus facilitates the model training. Besides, the learned deviation enables the network to produce the residual between future conditions and its corresponding weekly observations at each time interval, and therefore contributes to substantially better predictions. We further propose a lightweight Spatial-Channel Enhanced Encoder to build more powerful region representations, by jointly capturing global spatial correlations and temporal dependencies. Experimental results on two real-world datasets demonstrate that PRNet outperforms the state-of-the-art methods in terms of both accuracy and robustness.

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