Multi-agent systems in the field of urbane-mobility: A Systematic Review

Gauna, David Eneko Ruiz de; Villalonga, Claudia; Sánchez, Luís Enrique

doi:10.1109/tla.2020.9400447

Cited by 5 publications

(4 citation statements)

References 92 publications

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“…Moreover, the paper delves into the concept of multi-agent systems, which offer an effective approach to managing distributed and complex traffic problems. The study introduces different traffic signal control agent architectures, each with its own advantages and disadvantages, but collectively demonstrating the superiority of multi-agent-based traffic signal controllers over conventional methods [28][29][30][31][32][33][34].…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Based Real-Time Traffic Management

2023

J Electrical Electron Eng

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The paper proposes applying the ant colony optimization algorithm within a distributed multi-agent architecture, leveraging IoT technology, to address path routing challenges in urban traffic. The study emphasizes the potential of advanced AI techniques and multi-agent systems in revolutionizing traffic management for efficient and sustainable urban transportation systems. Our study addresses the challenging issue of traffic congestion in modern urban areas and the limitations of traditional solutions like road expansion and network indicators. To effectively tackle traffic congestion, the study explores various strategies that analyze traffic elements, falling into the Macroscopic and Microscopic Models. However, conventional traffic modeling faces significant challenges in dealing with complex traffic systems. The rise of the Internet of Things (IoT) offers opportunities for effective traffic analysis by collecting vast and uncertain data. Nevertheless, most existing systems focus on local events, leaving a gap in their effectiveness. To address this, multi-agent systems become a novel strategy, deploying distributed agents across road intersections for comprehensive traffic management. Artificial Intelligence (AI) techniques, including fuzzy logic, evolutionary algorithms, neural networks, and reinforcement learning, emerge as promising solutions for traffic control. For instance, artificial neural networks accurately predict urban traffic flow, and meta-heuristic AI approaches like the artificial bee colony algorithm optimize signal timings.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Based Real-Time Traffic Management

2023

J Electrical Electron Eng

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“…Continuando con la línea de las redes inteligentes y los mercados de distribución, se identifican interesantes trabajos en la literatura sobre modelado de electromovilidad en procesos de expansión de subtransmisión [26][27][28][29][30][31] y el análisis de impacto de la figura del comercializador de energía [32][33][34]. Respecto a este último, es interesante el impacto que puede tener sobre las redes de distribución o subtransmisión dependiendo de sus servicios asociados.…”

Section: Revisión Bibliográficaunclassified

Oportunidades y desafíos en la planificación de la transmisión zonal de Chile considerando particularidades del sistema de distribución

Altamirano¹,

Samper

García-Santander³

et al. 2023

Ingeniare. Rev. chil. ing.

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El desarrollo de nuevas tecnologías y usos de la energía eléctrica, como los recursos energéticos distribuidos, plantean nuevos desafíos y oportunidades de negocio para la industria eléctrica. Ello en pro de realizar un mejor uso de la información para que las empresas puedan gestionar de forma eficiente sus activos (minimizando los costes de inversión y operación, mejorando a su vez la calidad del servicio) y que los usuarios gestionen racionalmente su demanda (haciendo un uso eficiente de los recursos energéticos disponibles). En este sentido, Chile está experimentando un cambio de paradigma en el sector eléctrico, buscando principalmente mejorar la eficiencia energética. En este trabajo se presenta un análisis de las principales problemáticas de los recursos presentes en los Sistemas de Distribución Eléctrica, y se proponen algunas métricas con el fin de que estos sean considerados como insumos (inputs) en los modelos de Planificación de la Transmisión, específicamente en la Transmisión Zonal (o subtransmisión) en Chile.Palabras clave: Sistemas de distribución eléctrica, transmisión zonal, planificación de la expansión, recursos energéticos distribuidos, modelado y simulación sistemas eléctricos.

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“…In particular, Taniguchi, Thompson, and Qureshi (2020) identify Intelligent Transportation Systems (ITS), Autonomous Vehicles, Parcel Lockers, Crowd Shipping Platforms, and Electric Vehicles, as promising technologies to foster future CL systems especially if combined with Internet of Things (IoT) and Big Data. These technologies can be considered as pillars for CL implementations in the coming years ( de Gauna, Villalonga, & Sánchez, 2020 ; Dolati Neghabadi, Espinouse, & Lionet, 2021 ). Big Data Analytics (BDA) are also foreshadowing benefits in terms of real-time prediction, adaptation, quality of life and greater ease of movement ( Kandt & Batty, 2021 ).…”

Section: Introductionmentioning

confidence: 99%