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Traveling and transporting are essential demands of human beings to do daily activities. Due to the increasing demands of traffic usage, and various factors (e.g., weather, public infrastructure, etc.), traffic congestion often happens in large cities. Early warnings of congestion could help traffic managers to adjust the traffic flow for congestion prevention. Congestion prediction could be considered as a time-series forecasting problem. Recently, Niu et al. [1] proposed an efficient method, namely Mu2ReST, outperforming previous studies for long-term time-series forecasting. In this paper, we apply Mu2ReST to the problem of congestion prediction and evaluate its performance for long-term prediction.
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