Open Access

ARTICLE

Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile Ad Hoc Networks (MANETS)

Ahmed Alhussen¹, Arshiya S. Ansari^2,*

1 Department of Computer Engineering, College of Computer and Information Sciences, Majmaah University, Al-Majmaah, 11952, Saudi Arabia
2 Department of Information Technology, College of Computer and Information Sciences, Majmaah University, Al-Majmaah, 11952, Saudi Arabia

* Corresponding Author: Arshiya S. Ansari. Email:

Computers, Materials & Continua 2024, 79(2), 1903-1923. https://doi.org/10.32604/cmc.2024.049260

Received 01 January 2024; Accepted 06 March 2024; Issue published 15 May 2024

Abstract

Traffic in today’s cities is a serious problem that increases travel times, negatively affects the environment, and drains financial resources. This study presents an Artificial Intelligence (AI) augmented Mobile Ad Hoc Networks (MANETs) based real-time prediction paradigm for urban traffic challenges. MANETs are wireless networks that are based on mobile devices and may self-organize. The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts. This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network (CSFPNN) technique to assess real-time data acquired from various sources within the MANETs. The framework uses the proposed approach to learn from the data and create prediction models to detect possible traffic problems and their severity in real time. Real-time traffic prediction allows for proactive actions like resource allocation, dynamic route advice, and traffic signal optimization to reduce congestion. The framework supports effective decision-making, decreases travel time, lowers fuel use, and enhances overall urban mobility by giving timely information to pedestrians, drivers, and urban planners. Extensive simulations and real-world datasets are used to test the proposed framework’s prediction accuracy, responsiveness, and scalability. Experimental results show that the suggested framework successfully anticipates urban traffic issues in real-time, enables proactive traffic management, and aids in creating smarter, more sustainable cities.

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Keywords

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