… traffic prediction problems have received wide attention for decades. Essentially, the aim of
traffic prediction is to predict a traffic-… the related work on traffic prediction problems. In time …

… Recognizing the sequential nature of traffic data, similar to language, we introduce TPLLM,
a novel traffic prediction framework leveraging LLMs. In this framework, we construct a …

… Motivated by these, we propose the multi-source deep traffic prediction framework, … traffic
prediction performance but also supply a multi-prediction mechanism to forecast various traffic …

… SDN architecture provides precise prediction of network traffic with fine … efficient and intelligent
traffic prediction framework, fusion … Fusion learning provides the prediction model with the …

… In this paper, a correlated traffic prediction framework (LSTM-CTP) is proposed to address
… and provide accurate predictions. The framework includes steps: data preprocessing, trend …

… scalable wireless traffic prediction framework that can well-balance the prediction accuracy
and … Specifically, each traffic prediction model is performed on an individual BBU and trained …

… methods rarely mine the internal relationship between multi-source traffic data and … traffic
prediction framework. The basic idea is to share latent features between two parallel prediction …

… the prediction accuracy. Figure 10 shows our framework for predicting the RMSE and MAPE
of traffic volume … We believe that the framework achieves superior performance in predicting …

… This paper proposes a framework called Meta Graph Transformer (MGT) to solve traffic
prediction problems. MGT makes full use of attention mechanisms in both temporal and spatial …

… how traffic information transmission is affected by other nodes in the road network, and the
GNN-based traffic prediction model, as a benchmark for traffic prediction, … by transmitting traffic …