In this study, we propose a trajectory data-driven network representation method, specifically leveraging directional statistics. This approach allows us to extract major intersections and define links from observed trajectories, thereby mitigating the reliance on existing network data and map matching. We apply Graph Convolutional Networks and Long-Short Term Memory models to the trajectory data-driven network representation, suggesting the potential for fast and accurate traffic state prediction. The results imply significant reduction in computational complexity while demonstrating promising prediction accuracy. Our proposed method offers a valuable approach for analyzing and modeling transportation networks using real-world trajectory data, providing insights into traffic patterns and facilitating the exploration of more efficient traffic management strategies.
Trajectory Data-Driven Network Representation for Traffic State Prediction using Deep Learning
Int. J. ITS Res.
International Journal of Intelligent Transportation Systems Research ; 22 , 1 ; 136-145
2024-04-01
10 pages
Article (Journal)
Electronic Resource
English
Network-Wide Vehicle Trajectory Prediction in Urban Traffic Networks using Deep Learning
Transportation Research Record | 2018
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