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AI Based Traffic Flow prediction Model for Connected and Autonomous Electric Vehicles

Computers, Materials & Continua 2022年第2期70卷 3333-3347页

作者： p.thamizhazhagan M.Sujatha S.Umadevi K.priyadarshini Velmurugan Subbiah parvathy Irina V.pustokhina Denis A.pustokhin Department of Electrical and Electronics Engineering University College of EngineeringPanruti607106India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education FoundationVijayawada522502India Centre for Nanoelectronics and VLSI Design SENSEVIT UniversityChennai CampusChennai600127India Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gainedmomentum in the recent years among potential *** and Autonomous Electric Vehicle(CAEV)technologies are fascinating the automakers and inducing them to manufacture connected autonomous vehicles with self-driving features such as autopilot and ***,Traffic Flow prediction(TFp)is identified as a major issue in CAEV technologies which needs to be addressed with the help of Deep Learning(DL)*** this view,the current research paper presents an artificial intelligence-based parallel autoencoder for TFp,abbreviated as AIpAE-TFp model in *** presented model involves two major processes namely,feature engineering and *** feature engineering process,there are multiple stages involved such as feature construction,feature selection,and feature *** addition to the above,a Support Vector Data Description(SVDD)model is also used in the filtration of anomaly points and smoothen the raw ***,AIpAE model is applied to determine the predictive values of traffic *** order to illustrate the proficiency of the model’s predictive outcomes,a set of simulations was performed and the results were investigated under distinct *** experimentation outcomes verified the effectual performance of the proposed AIpAE-TFp model over other methods.

关键词： Autonomous electric vehicle traffic flow predictive automation industry connected vehicles seep learning

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