Graph convolutional neural network based traffic prediction
NAN Qiucai¹, YANG Liu²
1. Huanghe Jiaotong University, Jiaozuo 450062, China;
2. Changsha University of Science and Technology, Changsha 410114, China
Abstract: Due to the spatial and temporal complexity of the traffic prediction problem, accomplishing prediction in intelligent transportation systems is a challenging task. Although the time dependence of traffic prediction has been well studied and discussed, relatively little research has been done on spatially dependent traffic prediction due to the large variability of spatial dependence, especially in complex urban traffic environments. In this paper, a new graph convolution prediction network model is proposed and applied to two urban traffic networks with different geometric constraints. First, the model performs graph convolution operations on speed data using multiple weighted adjacency matrices to combine features such as speed limits, distances, and road angles. Second, spatially isolated dimensionality reduction operations are performed on the combined features to learn the dependencies between the features and reduce the size of the output to a computationally feasible level. Then, the output of the multi-weighted graph convolutional network is applied to a model with long and short-term memory units to learn temporal dependencies. Finally, the proposed prediction network is applied to two traffic networks in urban core and mixed urban areas, and its performance not only outperforms the remaining six comparative models, but also reduces the prediction variance of the traffic network in mixed urban areas. The results show that the proposed prediction network can provide robust traffic prediction performance under different spatial complexities, which is a strong advantage in urban traffic prediction.
Keywords: defect traffic forecast;graph convolution neural network;reduced dimension convolution;feature learning
2023, 49(9):123-132  收稿日期: 2022-08-28;收到修改稿日期: 2022-10-26
基金项目: 湖南省教育厅重点项目（20A009）
作者简介: 南秋彩（1981-）女，河南郏县人，副教授，硕士，研究方向为交通工程管理
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