On the prediction of large-scale road-network constrained trajectories

Author(s)

de Sousa, Roniel S. ; Boukerche, Azzedine ; Loureiro, Antonio A. F.

• Document type: Journal article
• Source: Computer Networks; v. 206, p. 13-pg., 2022-02-05.

Abstract

Trajectory data mining-based applications benefit from the increasing availability of vehicle trajectory and road network datasets. For instance, the application of trajectory prediction makes it possible to design more efficient routing protocols for vehicular networks. This paper proposes a novel cluster-based framework for the long-term prediction of road-network constrained trajectories. The framework employs a new hierarchical agglomerative clustering algorithm and trains prediction models from historical trajectory datasets. Experimental results show the framework's effectiveness and efficiency to predict trajectories with different characteristics in a new real-world, large-scale scenario. Furthermore, the framework outperformed the related work in terms of prediction accuracy and time complexity. (AU)

• FAPESP's process: 18/23064-8 - Mobility in urban computing: characterization, modeling and applications (MOBILIS)
• Grantee: Antonio Alfredo Ferreira Loureiro
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 15/24494-8 - Communications and processing of big data in cloud and fog computing
• Grantee: Nelson Luis Saldanha da Fonseca
• Support Opportunities: Research Projects - Thematic Grants