Abstract
The prediction of traffic flow is of great importance to urban planning and intelligent transportation systems. Recently, deep learning models have been applied to study this problem. However, there still exist two main limitations: (1) They do not effectively model dynamic traffic patterns in irregular regions; (2) The traffic flow of a region is strongly correlated to the transition-flow between different regions, while this issue is largely ignored by existing approaches. To address these issues, we propose a multitask deep learning model called MTGCN for a more accurate traffic flow prediction. First, to process the input traffic network data, we propose using graph convolution in place of traditional grid-based convolution to model spatial dependencies between irregular regions. Second, as original graph convolution can not well respond to traffic dynamics, we design a novel attention mechanism to capture dynamic traffic patterns. At last, to obtain a more accurate prediction result, we integrate two correlated tasks which respectively predict two types of traffic flows (region-flow and transition-flow) as a whole, by combining the representations learned from each task in a rational way. We conduct extensive experiments on two real-world datasets and the results show that our proposed method achieves better performance compared with other baseline models.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant Nos. 61872258, 61772356, 61876117, and 61802273, the Australian Research Council discovery projects under grant numbers DP170104747, DP180100212, and the Open Program of State Key Laboratory of Software Architecture under item number SKLSAOP1801.
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Wang, F., Xu, J., Liu, C., Zhou, R., Zhao, P. (2020). MTGCN: A Multitask Deep Learning Model for Traffic Flow Prediction. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12112. Springer, Cham. https://doi.org/10.1007/978-3-030-59410-7_30
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