Abstract
Traffic prediction problem plays a crucial role in the research of intelligent transportation systems. Traffic flow is an important indicator to measure the traffic status. Traffic flow prediction can not only provide a scientific basis for traffic managers but also support other road services. This work proposes a spatial-temporal convolutional neural network with improved graph representation (IGR-TCN) for predicting urban traffic flow, which solves the limitations of traditional methods considering only a single road section or a single detector. IGR-TCN reduces the computational complexity by using a convolutional structure, the temporal convolution layer uses dilated convolution, and causal convolution to optimize the long-term prediction capability. The graph representation proposed in this work improves the existing spatial-temporal correlation model and increases the spatial correlation trend of the data. The IGR-TCN fits better than traditional recurrent neural networks, traditional graph convolution models, and graph spatial-temporal models. It can be more effective for spatial-temporal information prediction.
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Lv, Y., Cheng, Z., Lv, Z., Li, J. (2022). A Spatial-Temporal Convolutional Model with Improved Graph Representation. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13471. Springer, Cham. https://doi.org/10.1007/978-3-031-19208-1_9
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DOI: https://doi.org/10.1007/978-3-031-19208-1_9
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