… Therefore, we design a calibration layer to slightly adjust the prediction results. The goal of
this research is to develop an effective long-term traffic flow forecasting method, which we call …

… a traffic prediction method based on a deep neural network for daily long-term traffic
prediction by considering the relationship between external factors and traffic flow [13]. Recently, …

… As shown in Table 2, LSGCN performs well in both longterm and short-term prediction for …
ly in the long-term prediction of PeMSD4, both long-term prediction and short-term prediction of …

… term traffic prediction models using unidirectional and bidirectional deep learning long short-term
… through its ability to recognize longer sequences of traffic time series data. In this work, …

… defects in traffic flow prediction. In this paper, we propose a long-term traffic flow prediction
method … The traffic network is modeled by dynamic traffic flow probability graphs, and graph …

… The main contributions of this paper are, • We proposed a novel end-to-end framework for
traffic prediction, which can model spatial, short-term and longterm periodical dependencies …

… 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 …

… speed data into a series of static images from which traffic predictions were made. In spite of
… traffic during short-term and long-term time horizons. Furthermore, in long-term predictions, …

… in the long-term horizon (eg, 1 hour ahead). We argue that the long-term traffic prediction
is … time to take actions to optimize the traffic according to the prediction. We also use the T-Test …

… in traffic prediction, with an emphasis on multivariate traffic time … traffic prediction context
are categorized, and the prediction … research challenges in traffic prediction and discuss some …