Smart City Gnosys
Smart city article details
| Title | Enhancement Of Traffic Forecasting Through Graph Neural Network-Based Information Fusion Techniques |
|---|---|
| ID_Doc | 23725 |
| Authors | Ahmed S.F.; Kuldeep S.A.; Rafa S.J.; Fazal J.; Hoque M.; Liu G.; Gandomi A.H. |
| Year | 2024 |
| Published | Information Fusion, 110 |
| DOI | http://dx.doi.org/10.1016/j.inffus.2024.102466 |
| Abstract | To improve forecasting accuracy and capture complex interactions within transportation networks, information fusion approaches are crucial for traffic predictions based on graph neural networks (GNNs). GNNs offer a potentially effective framework for capturing complex patterns and interactions among diverse elements, such as road segments and crossings, by considering both temporal and geographical dependencies. Although GNN-based traffic forecasting has recently been investigated in many studies, there is a need for comprehensive reviews that examine information fusion approaches for GNN-based traffic predictions, including an analysis of their benefits and challenges. This study addresses this knowledge gap and offers future insights into the potential advancements and developing fields of research in GNN-based fusion techniques, as well as their implications in urban planning and smart cities. Existing research demonstrates that the accuracy of traffic forecasting is substantially enhanced by information fusion techniques based on GNNs in comparison to more conventional approaches. By integrating information fusion methods with GNNs, the model is capable of capturing complex temporal and spatial relationships between various locations in a traffic network. Multi-source data integration benefits traffic forecasting models, including social events, weather conditions, real-time traffic sensor data, and historical traffic patterns. In addition, combining GNNs with other artificial intelligence (AI) methods like evolutionary algorithms or reinforcement learning could be an efficient strategy. With the potential to combine the best features of several methods, hybrid models could improve overall performance and flexibility in challenging traffic situations. © 2024 |
| Author Keywords | Deep learning; GNNs; Graph convolution network; Graph neural networks; Spatial–temporal graph; Traffic forecasting |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
38283  | 0.92 | Dhanasekaran S.; Gopal D.; Logeshwaran J.; Ramya N.; Salau A.O. | Multi-Model Traffic Forecasting In Smart Cities Using Graph Neural Networks And Transformer-Based Multi-Source Visual Fusion For Intelligent Transportation Management | International Journal of Intelligent Transportation Systems Research, 22, 3 (2024) |
| 6360 | 0.906 | Khairy M.; Mokhtar H.M.O.; Abdalla M. | Adaptive Traffic Prediction Model Using Graph Neural Networks Optimized By Reinforcement Learning | International Journal of Cognitive Computing in Engineering, 6 (2025) |
| 28556 | 0.897 | Jinia S.N.; Azad S.B.; Akter R.; Dipto T.R.; Khaliluzzaman M. | Gtn-Gcn: Real-Time Traffic Forecasting Using Graph Convolutional Network And Transformer | Applied Computational Intelligence and Soft Computing, 2025, 1 (2025) |
| 38415 | 0.894 | Zhao W.; Zhang S.; Zhou B.; Wang B. | Multi-Spatio-Temporal Fusion Graph Recurrent Network For Traffic Forecasting | Engineering Applications of Artificial Intelligence, 124 (2023) |
| 24078 | 0.89 | Mohsin K.S.; Mettu J.; Madhuri C.; Usharani G.; Silpa N.; Yellamma P. | Enhancing Urban Traffic Management Through Hybrid Convolutional And Graph Neural Network Integration | Journal of Machine and Computing, 4, 2 (2024) |
| 1982 | 0.889 | Sharma A.; Sharma A.; Nikashina P.; Gavrilenko V.; Tselykh A.; Bozhenyuk A.; Masud M.; Meshref H. | A Graph Neural Network (Gnn)-Based Approach For Real-Time Estimation Of Traffic Speed In Sustainable Smart Cities | Sustainability (Switzerland), 15, 15 (2023) |
| 53044 | 0.888 | Meng X.; Xie W.; Cui J. | Stmgfn: Spatio-Temporal Multi-Graph Fusion Network For Traffic Flow Prediction | Lecture Notes in Computer Science, 15291 LNCS (2025) |
| 34723 | 0.887 | Zhou J.; Shuai S.; Wang L.; Yu K.; Kong X.; Xu Z.; Shao Z. | Lane-Level Traffic Flow Prediction With Heterogeneous Data And Dynamic Graphs | Applied Sciences (Switzerland), 12, 11 (2022) |
| 58599 | 0.885 | Han S.-Y.; Sun Q.-W.; Zhao Q.; Han R.-Z.; Chen Y.-H. | Traffic Forecasting Based On Integration Of Adaptive Subgraph Reformulation And Spatio-Temporal Deep Learning Model | Electronics (Switzerland), 11, 6 (2022) |
| 20800 | 0.884 | Diao Z.; Wang X.; Zhang D.; Xie G.; Chen J.; Pei C.; Meng X.; Xie K.; Zhang G. | Dmstg: Dynamic Multiview Spatio-Temporal Networks For Traffic Forecasting | IEEE Transactions on Mobile Computing, 23, 6 (2024) |