Abstract
Traffic flow forecasting is important for urban planning and road network recommendation. Traffic flow is affected by time and space which makes the traffic forecasting difficult, so traffic forecasting model should have the characteristics of real-time, accuracy and reliability. In recent years, deep learning has achieved great success in image classification, feature extraction and computer vision which makes the predict of traffic flow based on historical trajectory data possible. In this paper, we introduce ST-SEResNet, we converts the collected traffic data into 2-channel in and out images, and input it into the ST-SEResNet network after a convolutional operation, and use the net to obtain the inflow and outflow channels of the traffic image. The SENet model is used to extract the importance factor of traffic region, and the ResNet model is used to extract the spatial correlation of traffic flow. Compared with the traditional traffic prediction model, The experimental results show that the model not only improves the efficiency of the network but also raise the prediction accuracy of the network.
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Acknowledgement
This work has been supported by the National Science Foundation of China Grant No. 61762092, “Dynamic multi-objective requirement optimization based on transfer learning,” and the Open Foundation of the Key Laboratory in Software Engineering of Yunnan Province, Grant No. 2017SE204, “Research on extracting software feature models using transfer learning,” and the National Science Foundation of China Grant No. 61762089, “The key research of high order tensor decomposition in distributed environment”.
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Kang, Y., Li, H., Niu, R., Yuan, Y., Liu, X. (2019). Urban Traffic Flow Forecast Based on ST-SEResNet. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11633. Springer, Cham. https://doi.org/10.1007/978-3-030-24265-7_42
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DOI: https://doi.org/10.1007/978-3-030-24265-7_42
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