International Journal of Advances in Applied Computational Intelligence
Journal DOI
2833-5600ISSN (Online)
Volume 5 , Issue 2 , PP: 60-72, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Djamal Lhiani 1 * , Othman Al-basheer 2
Doi: https://doi.org/10.54216/IJAACI.050205
Multi-criteria decision-making (MCDM) is employed for analyzing traffic in a Vehicle-to-Everything (V2X) network. V2X suggests communication among vehicles and other entities, containing pedestrians, infrastructure, and other vehicles. Traffic analysis and management in V2X networks need effectual decision-making approaches, which assume several conditions. MCDM contains estimating and choosing alternatives depending on several conditions or objectives. In the context of traffic analysis in V2X networks, MCDM algorithms are employed for decision-making concerning traffic flow optimizer, resource allocation, route planning, and congestion management. Deep learning (DL) approaches are trained to analyze massive counts of data gathered from several sources from the V2X network. These sources contain traffic sensors, GPS data, vehicle-to-infrastructure (V2I) communication, and historical traffic designs. By processing this data, DL approaches extract useful insights and create informed decisions depending on various conditions. Therefore, this article proposes a gorilla troops optimizer with deep learning-based MCDM for traffic analysis (GTODL-MCDMTA) technique in the V2X network. The purpose of the GTODL-MCDMTA algorithm is to identify the traffic flow prediction for improving route planning and resource allocation with the consideration of various factors into account. In the presented GTODL-MCDMTA technique, the input data is pre-processed to remove noise and normalize it for analysis. Next, the GTO algorithm is used for the feature selection process. Besides, the deep extreme learning machine (DELM) model is used for the forecast of traffic movement. Finally, the seeker optimization algorithm (SOA) has been utilized for the parameter tuning of the DELM technique. A brief set of simulation outcomes can be applied to emphasize the promising outcomes of the GTODL-MCDMTA technique. The experimental outcome demonstrates the efficiency and efficiency of the GTODL-MCDMTA approach in handling the complexity and dynamic nature of V2X network traffic analysis.
Sustainable transport , V2X Networks , Traffic analysis , Deep learning , Multi-criteria decision-making
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