… -term prediction is still limited. To this end, this paper focuses on solving the problem of
long-term traffic predictions. Existing traffic prediction models mainly include datadriven statistical …

… 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 …

… 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 …

… 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, …

… This paper presents a traffic prediction method for … prediction method for daily long-term
traffic flow was presented based on mining the relationship between contextual factors and traffic …

… 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, …

… long short-term memory neural network (LSTMNN) [37] is developed. It can recognize inherent
correlations inside the long-time span traffic … [38] proposed a traffic prediction research via …

… 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 …

… 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 …

… to perform long-term forecasting than short-term forecasting … prediction capacity for long-term
traffic flow, this paper proposes a novel approach for long-term traffic flow prediction with …