Abstract
In this paper, a data grouping approach based on convolutional neural network (DGCNN) is proposed for forecasting urban short-term traffic flow. This approach includes the consideration of spatial relations between traffic locations, and utilizes such information to train a convolutional neural network for forecasting. There are three advantages of our approach: (1) the spatial relations of traffic flow are adopted; (2) high-quality features are extracted by CNN; and (3) the accuracy of forecasting short-term traffic flow is improved. To verify our model, extensive experiments are performed on a real data set, and the result shows that the model is more effective than other existing methods.
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Acknowledgment
This work was supported in part by the National Basic Research 973 Program of China under Grant No. 2015CB352502, the National Natural Science Foundation of China under Grant Nos. 61272092 and 61572289, the Natural Science Foundation of Shandong Province of China under Grant Nos. ZR2012FZ004 and ZR2015FM002, the Science and Technology Development Program of Shandong Province of China under Grant No. 2014GGE27178, and the NSERC Discovery Grants.
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Yu, D., Liu, Y., Yu, X. (2016). A Data Grouping CNN Algorithm for Short-Term Traffic Flow Forecasting. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9931. Springer, Cham. https://doi.org/10.1007/978-3-319-45814-4_8
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