Although traffic prediction has been receiving considerable attention with a
number of successes in the context of intelligent transportation systems, the
prediction of traffic states over a complex transportation network that
contains different road types has remained a challenge. This study proposes a
multi-scale graph wavelet temporal convolution network (MSGWTCN) to predict the
traffic states in complex transportation networks. Specifically, a multi-scale
spatial block is designed to simultaneously capture the spatial information at
different levels, and the gated temporal convolution network is employed to
extract the temporal dependencies of the data. The model jointly learns to
mount multiple levels of the spatial interactions by stacking graph wavelets
with different scales. Two real-world datasets are used in this study to
investigate the model performance, including a highway network in Seattle and a
dense road network of Manhattan in New York City. Experiment results show that
the proposed model outperforms other baseline models. Furthermore, different
scales of graph wavelets are found to be effective in extracting local,
intermediate and global information at the same time and thus enable the model
to learn a complex transportation network topology with various types of road
segments. By carefully customizing the scales of wavelets, the model is able to
improve the prediction performance and better adapt to different network
configurations.

通过使用多尺度图小波时域卷积网络 (MSGWTCN) 模型，同时捕捉不同层次的空间信息和提取数据的时间依赖性，可以更好地预测复杂交通网络中的交通状态，并在实验中证明了该模型的卓越表现。