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CDGNet: A Cross-Time Dynamic Graph-based Deep Learning Model for Traffic Forecasting
[article]
2021
arXiv
pre-print
Traffic forecasting is important in intelligent transportation systems of webs and beneficial to traffic safety, yet is very challenging because of the complex and dynamic spatio-temporal dependencies in real-world traffic systems. Prior methods use the pre-defined or learnable static graph to extract spatial correlations. However, the static graph-based methods fail to mine the evolution of the traffic network. Researchers subsequently generate the dynamic graph for each time slice to reflect
arXiv:2112.02736v1
fatcat:qspala2jy5ajbeuxndgonaka7i