A Sequential Convolution Network for Population Flow Prediction with Explicitly Correlation Modelling

Population flow prediction is one of the most fundamental components in many applications from urban management to transportation schedule. It is challenging due to the complicated spatial-temporal correlation. While many studies have been done in recent years, they fail to simultaneously and effectively model the spatial correlation and temporal variations among population flows. In this paper, we propose Convolution based Sequential and Cross Network (CSCNet) to solve them. On the one hand, we design a CNN based sequential structure with progressively merging the flow features from different time in different CNN layers to model the spatial-temporal information simultaneously. On the other hand, we make use of the transition flow as the proxy to efficiently and explicitly capture the dynamic correlation between different types of population flows. Extensive experiments on 4 datasets demonstrate that CSCNet outperforms the state-of-the-art baselines by reducing the prediction error around 7. 7%∼10. 4%.

Authors

• Jie Feng
• Ziqian Lin
• Tong Xia
• Funing Sun
• Diansheng Guo
• Yong Li

Keywords

• Data Mining: Applications
• Data Mining: Mining Spatial, Temporal Data
• Machine Learning Applications: Environmental
• Multidisciplinary Topics and Applications: Transportation