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Scalable Intelligent Traffic Balancing: Advancing Efficiency, Safety, and Sustainability in Urban Transportation Through Machine Learning and AIM Integration

Chamideh, Seyedezahra LU ; Tärneberg, William LU and Kihl, Maria LU (2024) 2023 IEEE Global Conference on Artificial Intelligence and Internet of Things, GCAIoT
Abstract
With the growing demand for efficient, safe and sustainable transportation systems, the imperative to design intelligent routing and traffic management solutions within urban settings, requiring minimal data exchange and ensuring scalability, becomes evident. This paper introduces an innovative paradigm for traffic management. By seamlessly integrating machine learning and Autonomous Intersection Management (AIM), we propose a highly scalable routing and traffic balancing system. Our innovation significantly reduces data exchange requirements while optimizing traffic flow. By leveraging the power of machine learning algorithms, our proposed system aims to elevate the efficiency, safety, and sustainability of urban transportation networks.... (More)
With the growing demand for efficient, safe and sustainable transportation systems, the imperative to design intelligent routing and traffic management solutions within urban settings, requiring minimal data exchange and ensuring scalability, becomes evident. This paper introduces an innovative paradigm for traffic management. By seamlessly integrating machine learning and Autonomous Intersection Management (AIM), we propose a highly scalable routing and traffic balancing system. Our innovation significantly reduces data exchange requirements while optimizing traffic flow. By leveraging the power of machine learning algorithms, our proposed system aims to elevate the efficiency, safety, and sustainability of urban transportation networks. This paper provides an insightful overview of our AIM method, explores the application of machine learning in routing, and delineates our approach to achieve effective traffic balancing. Through extensive experimental results and evaluations, we demonstrate the efficacy of our proposed system in enhancing traffic flow and alleviating congestion in urban scenarios. (Less)
Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2023 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2023 IEEE Global Conference on Artificial Intelligence and Internet of Things, GCAIoT
conference location
Dubai, United Arab Emirates
conference dates
2023-12-10 - 2023-12-11
external identifiers
  • scopus:85184659085
DOI
10.1109/GCAIoT61060.2023.10385120
language
English
LU publication?
yes
id
b4d1feaa-e50f-4c19-b2a5-3f48a5618c88
date added to LUP
2024-02-14 11:18:54
date last changed
2024-03-01 14:54:59
@inproceedings{b4d1feaa-e50f-4c19-b2a5-3f48a5618c88,
  abstract     = {{With the growing demand for efficient, safe and sustainable transportation systems, the imperative to design intelligent routing and traffic management solutions within urban settings, requiring minimal data exchange and ensuring scalability, becomes evident. This paper introduces an innovative paradigm for traffic management. By seamlessly integrating machine learning and Autonomous Intersection Management (AIM), we propose a highly scalable routing and traffic balancing system. Our innovation significantly reduces data exchange requirements while optimizing traffic flow. By leveraging the power of machine learning algorithms, our proposed system aims to elevate the efficiency, safety, and sustainability of urban transportation networks. This paper provides an insightful overview of our AIM method, explores the application of machine learning in routing, and delineates our approach to achieve effective traffic balancing. Through extensive experimental results and evaluations, we demonstrate the efficacy of our proposed system in enhancing traffic flow and alleviating congestion in urban scenarios.}},
  author       = {{Chamideh, Seyedezahra and Tärneberg, William and Kihl, Maria}},
  booktitle    = {{2023 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)}},
  language     = {{eng}},
  month        = {{01}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Scalable Intelligent Traffic Balancing: Advancing Efficiency, Safety, and Sustainability in Urban Transportation Through Machine Learning and AIM Integration}},
  url          = {{http://dx.doi.org/10.1109/GCAIoT61060.2023.10385120}},
  doi          = {{10.1109/GCAIoT61060.2023.10385120}},
  year         = {{2024}},
}