Jul 2026· 2026 IEEE 27th China Conference on System Simulation Technology and its Applications (CCSSTA)· pp. 7-12· 0 citations· 25 references
Abstract
To address the challenges of existing traffic volume prediction methods in effectively integrating heterogeneous data and mining deep spatio-temporal semantic correlations, this paper proposes a prediction method based on a Spatio-Temporal Knowledge Graph (STKG). The approach first constructs a knowledge graph to uniformly model road network topology, dynamic traffic flow, and external influencing factors. Then, a knowledge-enhanced spatio-temporal graph neural network is designed to deeply integrate knowledge semantics with real-time data through a unique fusion mechanism, aiming to capture the complex evolutionary patterns of the traffic system. Experimental results demonstrate that the method significantly enhances prediction accuracy in complex road network and sudden event scenarios, improving semantic association accuracy by 17.4% and reducing the Root Mean Square Error by 15.2%.
This framework introduces an adaptive graph learning module that dynamically infers meaningful connectivity relationships among traffic sensors—not relying on fixed geographic or distance-based assumptions—but instead leveraging real-time traffic correlations and node-level embeddings, enabling effective modeling of both localized spatial interactions and multi-scale temporal dependencies across varying prediction horizons.
Zhengxu Luan, Huan Wang, Miaobowen Wang et al.· Computers and artificial int...· 0 citations
An improved Transformer prediction model that integrates a graph convolutional network (GCN) and a self-attention mechanism is proposed for traffic flow prediction, combining temporal self-attention and learnable temporal encoding to capture both long-term traffic evolution patterns and sudden fluctuations.
Jin Zhang, Feng-Min Tan, Wei Bai et al.· Italian National Conference...· 0 citations
A prediction model that incorporates multiple attention mechanisms with spatiotemporal graph convolutional networks (HASTGCN) and designs a spatiotemporal map convolution module to collaboratively model the dynamic spatiotemporal connection of traffic flow collaboratively model is used.
Chu-xia Chen· Proceedings of the 3rd Inter...· 0 citations
TETRA is proposed, a hybrid spatio-temporal traffic forecasting model that integrates Graph Convolutional Networks (GCNs) with Extended Long Short-Term Memory (xLSTM) to capture complex multi-timescale temporal patterns, including congestion propagation and delayed recovery dynamics, which are not well represented by conventional recurrent models.
Norman Bereczki, Vilmos Simon· International Journal of Int...· 0 citations
Experimental results on the public PEMS04 and PEMS08 datasets demonstrate that the proposed ESDG-ALSTM model significantly improves forecasting accuracy, confirming that ESDG-ALSTM is more sensitive to abrupt events and multimodal evolution patterns and can effectively enhance prediction performance in complex traffic flow scenarios.
Guozheng Li, Bai-Jing Wu, Ke Gao et al.· Frontiers of Computer Scienc...· 0 citations
The proposed MSG-LLM traffic flow forecasting framework significantly outperforms existing mainstream models in both short-term and long-term traffic flow forecasting tasks, validating the effectiveness and strong generalization ability of MSG-LLM in modeling complex traffic systems.
