Air route link prediction based on the PSO-CLP model

To optimize the structure of an air route network, accurate forecasting of future new routes is vital given the rapid growth in demand for air transportation. Based on the theory and method of link prediction, considering the joint influence of network endogenous factors and external attributes, we...

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Veröffentlicht in:Journal of air transport management Jg. 120; S. 102669
Hauptverfasser: Zhang, Pei-wen, Zhao, Lian-zheng, Wang, Yu, Ding, Rui, Du, Fu-min
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.09.2024
Schlagworte:
Air route prediction
Link prediction
Network optimization
Particle swarm algorithm
Link prediction
Particle swarm algorithm
Network optimization
Air route prediction
ISSN:0969-6997
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Zusammenfassung:To optimize the structure of an air route network, accurate forecasting of future new routes is vital given the rapid growth in demand for air transportation. Based on the theory and method of link prediction, considering the joint influence of network endogenous factors and external attributes, we construct a system of network endogenous factors and external attribute indices and explore the prediction effect of each index. We further construct a prediction index system, explore the prediction effect of coupled indices, design a particle swarm algorithm to determine the weights of each index, and propose a coupled link prediction model based on particle swarm optimization (PSO-CLP). A comparison of the prediction accuracy of this model with the dual-indicator coupled link prediction model and the traditional link prediction model is also conducted to test the stability and reasonableness of the PSO-CLP model. In this study, we use the 2015–2020 Chinese air route network as an example. The instance test shows that the PSO-CLP models significantly outperform the traditional link prediction models and dual-indicator coupled link prediction models in terms of prediction accuracy, stability and computational simplicity, among which the PSO-CLP model, which considers both endogenous factors and external attributes, such as the RWR + Sor + Pop and RWR + RA + GDP indices, has the best forecasting effect. The PSO-CLP model is an effective tool for route prediction, providing a new perspective on route link prediction and air route network optimization.
ISSN:0969-6997
DOI:10.1016/j.jairtraman.2024.102669