Multi-Model Traffic Forecasting in Smart Cities using Graph Neural Networks and Transformer-based Multi-Source Visual Fusion for Intelligent Transportation Management
Congestion forecasting; Graph neural networks; Multi-modal traffic forecasting; Recursive feature elimination; Smart cities; Transformer-based multi-source visual fusion
In the intelligent transportation management of smart cities, traffic forecasting is crucial. The optimization of traffic flow, reduction of congestion, and improvement of theoverall transportation systemefficiency all depend on accurate traffic pattern projections. In order to overcome the difficulties causedby the complexity and diversity of urban traffic dynamics, this research suggests a unique method for multi-modal traffic forecasting combining Graph Neural Networks (GNNs) and Transformer-based multi-source visual fusion. GNNs are employed in this method to capture the spatial connections betweenvarious road segments and to properly reflect the basic structure of the road network. The model's ability to effectively analyse traffic dynamics and relationships between nearby locations is enhanced by graphsrepresenting the road layout, which also increases theoutcome of traffic predictions. Recursive Feature Elimination (RFE) is employed to improve the model's feature selection process and choose the most pertinent features for traffic prediction, producing forecasts that are more effective and precise. Utilizing real-time data, the performance of the suggested strategywasassessed, enabling it to adjust to shifting traffic patterns and deliver precise projections for intelligent transportation management. The empirical outcomes show exceptional results ofperformance metrics for the proposed approach, achieving anamazing accuracy of 99%. The resultsshow that the suggested techniques findings have the ability to anticipate traffic and exhibit a superior level of reliability whichsupports efficient transportation management in smart cities. The Author(s), under exclusive licence to Intelligent Transportation Systems Japan 2024.
Dhanasekaran S.; Gopal D.; Logeshwaran J.; Ramya N.; Salau A.O.
International Journal of Intelligent Transportation Systems Research, Vol-22, No. 3, pp. 518-541.
https://doi.org/10.1007/s13177-024-00413-4 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201395364&doi=10.1007%2fs13177-024-00413-4&partnerID=40&md5=dae60889095fac07bf59c2caacfff581
Dhanasekaran S., Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Tamil Nadu, Coimbatore, 641202, India; Gopal D., Department of Electronics and Communication Engineering, AVN Institute of Engineering and Technology, Hyderabad, India; Logeshwaran J., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Ramya N., Department of Electronics and Communication Engineering, M.Kumarasamy College of Engineering, Karur, India; Salau A.O., Department of Electrical/Electronics and Computer Engineering, Afe Babalola University, Ado-Ekiti, Nigeria, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Tamil Nadu, Chennai, India