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Extracting multi-objective multigraph features for the shortest path cost prediction:Statistics-based or learning-based?

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Green Energy and Intelligent Transportation 2024年第1期3卷 1-15页

作者： Songwei Liu Xinwei Wang Michal Weiszer Jun Chen School of Engineering and Materials Science Queen Mary University of LondonMile End RoadLondon E14NSUnited Kingdom

Efficient airport airside ground movement(AAGM)is key to successful operations of urban air *** studies have introduced the use of multi-objective multigraphs(MOMGs)as the conceptual prototype to formulate *** calculation of the shortest path costs is crucial for the algorithmic heuristic search on MOMGs,however,previous work chiefly focused on single-objective simple graphs(SOSGs),treated cost enquires as search problems,and failed to keep a low level of computational time and storage *** paper concentrates on the conceptual prototype MOMG,and investigates its node feature extraction,which lays the foundation for efficient prediction of shortest path *** extraction methods are implemented and compared:a statistics-based method that summarises 22 node physical patterns from graph theory principles,and a learning-based method that employs node embedding technique to encode graph structures into a discriminative vector *** former method can effectively evaluate the node physical patterns and reveals their individual importance for distance prediction,while the latter provides novel practices on processing multigraphs for node embedding algorithms that can merely handle *** regression models are applied to predict the shortest path costs to demonstrate the performance of *** experiments on randomly generated benchmark MOMGs show that(i)the statistics-based method underperforms on characterising small distance values due to severe overestimation;(ii)A subset of essential physical patterns can achieve comparable or slightly better prediction accuracy than that based on a complete set of patterns;and(iii)the learning-based method consistently outperforms the statistics-based method,while maintaining a competitive level of computational complexity.

关键词： multi-objective multigraph Feature extraction Shortest path cost prediction Node patterns Node embeddings Regression

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