Enhancement of traffic forecasting through graph neural network-based information fusion techniques
•This study investigates information fusion methods for GNN-based traffic predictions, including their benefits and challenges.•A GNN-based information fusion technique improves traffic forecasting accuracy over conventional methods.•Integration of multi-source data improves traffic forecasting mode...
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| Published in: | Information fusion Vol. 110; p. 102466 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.10.2024
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| ISSN: | 1566-2535, 1872-6305 |
| Online Access: | Get full text |
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| Abstract | •This study investigates information fusion methods for GNN-based traffic predictions, including their benefits and challenges.•A GNN-based information fusion technique improves traffic forecasting accuracy over conventional methods.•Integration of multi-source data improves traffic forecasting models.•Integration of GNNs with AI methods like evolutionary algorithms or reinforcement learning could be effective.•Hybrid models could improve overall performance and flexibility in challenging traffic situations.
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. |
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| AbstractList | •This study investigates information fusion methods for GNN-based traffic predictions, including their benefits and challenges.•A GNN-based information fusion technique improves traffic forecasting accuracy over conventional methods.•Integration of multi-source data improves traffic forecasting models.•Integration of GNNs with AI methods like evolutionary algorithms or reinforcement learning could be effective.•Hybrid models could improve overall performance and flexibility in challenging traffic situations.
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. |
| ArticleNumber | 102466 |
| Author | Liu, Gang Hoque, Mahfara Gandomi, Amir H. Rafa, Sabiha Jannat Kuldeep, Sweety Angela Fazal, Javeria Ahmed, Shams Forruque |
| Author_xml | – sequence: 1 givenname: Shams Forruque orcidid: 0000-0003-3360-3636 surname: Ahmed fullname: Ahmed, Shams Forruque email: shams.forruque@northsouth.edu organization: Department of Mathematics & Physics, North South University, Dhaka, 1229, Bangladesh – sequence: 2 givenname: Sweety Angela surname: Kuldeep fullname: Kuldeep, Sweety Angela organization: Science and Math Program, Asian University for Women, Chattogram, 4000, Bangladesh – sequence: 3 givenname: Sabiha Jannat surname: Rafa fullname: Rafa, Sabiha Jannat organization: Science and Math Program, Asian University for Women, Chattogram, 4000, Bangladesh – sequence: 4 givenname: Javeria surname: Fazal fullname: Fazal, Javeria organization: Science and Math Program, Asian University for Women, Chattogram, 4000, Bangladesh – sequence: 5 givenname: Mahfara surname: Hoque fullname: Hoque, Mahfara organization: Science and Math Program, Asian University for Women, Chattogram, 4000, Bangladesh – sequence: 6 givenname: Gang surname: Liu fullname: Liu, Gang organization: School of Energy Science and Engineering, Central South University, Changsha, 410083, China – sequence: 7 givenname: Amir H. orcidid: 0000-0002-2798-0104 surname: Gandomi fullname: Gandomi, Amir H. email: gandomi@uts.edu.au organization: Faculty of Engineering & Information Technology, University of Technology Sydney, NSW, 2007, Australia |
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| Keywords | Deep learning Spatial–temporal graph Graph neural networks Traffic forecasting GNNs Graph convolution network |
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