Abstract
Urban flow prediction plays an essential role in public safety and traffic scheduling for a city. By mining the original granularity flow data, current research methods could predict the coarse-grained region flow. However, the prediction of a more fine-grained region is more important for city management, which means cities could derive more details from the original granularity flow data. In this paper, given the future weather information, we aim to predict the fine-grained region flow. We design Weather-affected Fine-grained Region Flow Predictor (WFRFP) model based on the super-resolution scheme. Our model consists of three modules: 1) Key flow maps selection module selects key flow maps from massive historical data as the input instance according to temporal property and weather similarity; 2) Weather condition fusion module processes the original weather information and extracts weather features; 3) Fine-grained flow prediction module learns the spatial correlations by wide activation residual blocks and predicts the fine-grained region flow by the upsampling operation. Extensive experiments on a real-world dataset demonstrate the effectiveness and efficiency of our method, and show that our method outperforms the state-of-the-art baselines.
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This work is supported by the National Natural Science Foundation of China (No. 61572165) and the National Natural Science Foundation of China (No. 61806061).
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Shen, R., Xu, J., Bao, Q., Li, W., Yuan, H., Xu, M. (2020). Fine-Grained Urban Flow Prediction via a Spatio-Temporal Super-Resolution Scheme. In: Wang, X., Zhang, R., Lee, YK., Sun, L., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2020. Lecture Notes in Computer Science(), vol 12318. Springer, Cham. https://doi.org/10.1007/978-3-030-60290-1_29
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DOI: https://doi.org/10.1007/978-3-030-60290-1_29
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