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

作　　者：Ahmed Alhussen Arshiya S.Ansari 

机构地区：[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

出　　处：《Computers, Materials & Continua》2024年第5期1903-1923,共21页计算机、材料和连续体（英文）

基　　金：the Deanship of Scientific Research at Majmaah University for supporting this work under Project No.R-2024-1008.

摘　　要：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)augmentedMobile 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 theMANETs.The framework uses the proposed approach to learn from the data and create predictionmodels 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.

关 键 词：Mobile AdHocNetworks(MANET) urban traffic prediction artificial intelligence(AI) traffic congestion chaotic spatial fuzzy polynomial neural network(CSFPNN) 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]