Analysis of Intelligent Transportation Systems A Review of Architecture Models, Traffic Management Strategies, and Accident Prevention Technologies

Chahardoli, Mostafa; Yasinian, Hamid

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Volume 3, Issue 6 (1-2025)

3 2025, 3(6): 1-10

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Analysis of Intelligent Transportation Systems A Review of Architecture Models, Traffic Management Strategies, and Accident Prevention Technologies

Mostafa Chahardoli

, Hamid Yasinian

Computer Engineering Department, Hamedan Branch, Islamic Azad Universit, Hamedan, Iran

Abstract: (247 Views)

Intelligent Transportation Systems (ITS) are recognized as a key solution in optimizing traffic management, reducing congestion, and increasing road safety. This research aims to investigate architecture models, traffic management solutions, and accident prevention technologies. In this study, architecture models based on Wireless Sensor Networks (WSN), Dynamic Route Guidance Systems (DRGS), and machine learning algorithms for traffic prediction and control are analyzed. Furthermore, emerging technologies including collision detection and avoidance systems, real-time data-driven algorithms, and hybrid methods based on neural networks and genetic algorithms are examined. The data for this research is extracted from previous studies and a comparative analysis of intelligent traffic control methods.

The results indicate that combining methods based on traffic data modeling, traffic flow behavior analysis, and short-term and long-term prediction algorithms provides higher accuracy in traffic management. In particular, systems based on real-time analysis of sensor data and deep learning can effectively reduce traffic delays and improve accident rates. Additionally, the proposed models have higher adaptability in urban environments with dynamic and unbalanced traffic conditions. The findings of this research demonstrate that integrating new technologies such as artificial intelligence algorithms, smart sensor networks, and Internet of Things (IoT) systems in urban transportation leads to increased efficiency of traffic management systems. Finally, optimizing machine learning models and developing self-organizing systems can reduce operational costs and increase the independence of ITS systems from expensive equipment.

Article number: 1

Keywords: Intelligent Transportation Systems, Traffic Management, Wireless Sensor Networks, Machine Learning, Traffic Prediction, Artificial Intelligence, Real-time Data Analysis, Accident Prevention, Intelligent Control Systems, Internet of Things

Full-Text [PDF 466 kb] (127 Downloads)

Type of Study: Research | Subject: General
Received: 2024/07/20 | Accepted: 2025/02/7 | Published: 2025/02/8

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Chahardoli M, Yasinian H. Analysis of Intelligent Transportation Systems A Review of Architecture Models, Traffic Management Strategies, and Accident Prevention Technologies. 3 2025; 3 (6) : 1
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