… similarity of the traffic flow series, a novel long-term forecasting method for traffic flow is
developed, and hybrid forecasting algorithms for short-/long-term traffic flow prediction are also …

… To the best of authors’ knowledge, existing studies on data missing imputation in traffic …
, a short-term traffic prediction framework is proposed to deal with insufficient traffic historical …

… Essentially, the aim of traffic prediction is to predict a traffic-… In this section, we discuss the
related work on traffic prediction … short-term representation hi,t and longterm representation …

… a geographical graph G , predicting the traffic state of the road network for the … -term single-step
traffic prediction scenario, S is set to 1 in the experiments. Therefore, the core of the traffic …

… Routing algorithms that do not include traffic prediction … -GAN model in traffic prediction
to increase pattern sensitivity … the Long Term optical traffic prediction (3 months of traffic), …

… (Long Term Span Traffic Prediction Model). This model is used for network traffic prediction.
… [6] Here the authors have investigated the traffic matrix prediction. They have proposed an …

… historical data, and makes a reasonable guess on the traffic data … traffic data, traffic prediction
can be divided into traffic speed prediction, traffic flow prediction, traffic accident prediction, …

… , long short-term memory (LSTM) is employed in the proposed traffic prediction model. …
study to enable the short-term input sequence to convey long-term periodic information. Finally, …

… recurrent neural network to predict urban long-term traffic flows. A representation of the …
long-term traffic flows. To the best of our knowledge, we are the first to predict the long-term traffic …

… the rich information in traffic data. In this paper, we present a novel short-term traffic flow
prediction approach based on dynamic tensor completion (DTC), in which the traffic data are …