Regression Performance of Relational Fusion Networks on Urban Road Networks

• Thales E. Cervi UTFPR
• Ricardo Lüders UTFPR
• Thiago H. Silva UTFPR
• Myriam R. Delgado UTFPR

DOI: https://doi.org/10.5753/courb.2024.3270

Resumo

Urban transportation planning in densely populated areas is a problem in constant need of efficient solutions. Graphs can represent urban street networks and be used to train algorithms, enriching decisions with information learned from structural and topological data of cities. Relational Fusion Networks (RFNs) are Graph Neural Networks specifically tailored for learning on road networks. This work explores the use of RFNs in estimating free-flow travel times and includes experiments on relevant cities from all continents. Results demonstrate the significance of fusion functions and city characteristics in both the learning process of RFNs on regression tasks and the capacity to extrapolate acquired knowledge to different cities.

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