A Simplified Deep Residual Network for Citywide Crowd Flows Prediction
2018 14th International Conference on Semantics, Knowledge and Grids (SKG)(2018)
摘要
Crowd flows prediction is an important problem of urban computing. The existing best-known method adopts deep residual networks to model spatio-temporal properties and often achieves good prediction performance. However, since three separated network structures are used to model the properties, the time cost is often expensive for the best-known method. In this paper, we propose an improved method to reduce the running time of the best-known method by simplifying its architecture. Compared with the best-known method, the training time and predicting time of our method can be reduced dramatically. Moreover, the improved method can achieve similar prediction performance with the best-known method. Extensive experiments on the real-world datasets were conducted to show the efficiency of our proposed method.
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关键词
Market research,Urban areas,Meteorology,Training,Trajectory,Roads,Safety
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