In this paper, a meaningful representation of the road network using multiplex networks and a novel feature selection framework that enhances the predictability of future traffic conditions of an entire network are proposed. Using data on traffic volumes and tickets’ validation from the transportation network of Athens, we were able to develop prediction models that not only achieve very good performance but are also trained efficiently, do not introduce high complexity and, thus, are suitable for real-time operation. More specifically, the network’s nodes (loop detectors and subway/metro stations) are organized as a multilayer graph, each layer representing an hour of the day. Nodes with similar structural properties are then classified in communities and are exploited as features to predict the future demand values of nodes belonging to the same community. The results reveal the potential of the proposed method to provide reliable and accurate predictions.
Traffic demand prediction using a social multiplex networks representation on a multimodal and multisource dataset
2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
Elsevier | 2024
|Bidirectional Spatial–Temporal Network for Traffic Prediction with Multisource Data
Transportation Research Record | 2020
|DOAJ | 2024
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