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Neural Computing and Applications

Erschienen in:

06.10.2020 | Original Article

Multiple ACO-based method for solving dynamic MSMD traffic routing problem in connected vehicles

verfasst von: Tri-Hai Nguyen, Jason J. Jung

Erschienen in: Neural Computing and Applications | Ausgabe 12/2021

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Abstract

In this study, we focus on dynamic traffic routing of connected vehicles with various origins and destinations; this is referred to as a multi-source multi-destination traffic routing problem. Ant colony optimization (ACO)-based routing method, together with the idea of coloring ants, is proposed to solve the defined problem in a distributed manner. Using the concept of coloring ants, traffic flows of connected vehicles to different destinations can be distinguished. To evaluate the performance of the proposed method, we perform simulations on the multi-agent NetLogo platform. The simulation results indicate that the ACO-based routing method outperforms the shortest path-based routing method (i.e., given the same simulation period, the average travel time decreases by 8% on average and by 11% in the best case, whereas the total number of arrived vehicles increases by 13% on average and by 23% in the best case).

Nächster Artikel Spatiotemporal dynamic of a coupled neutral-type neural network with time delay and diffusion

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Titel: Multiple ACO-based method for solving dynamic MSMD traffic routing problem in connected vehicles
verfasst von: Tri-Hai Nguyen
Jason J. Jung
Publikationsdatum: 06.10.2020
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 12/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05402-8

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