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Title	Expression Of Concern: Implementation Of Machine Learning Techniques For Predicting Traffic Flow In Smart Cities (2023 6Th International Conference On Contemporary Computing And Informatics (Ic3I) Doi: 10.1109/Ic3I59117.2023.10397998)
ID_Doc	25858
Authors
Year	2023
Published	Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2023
DOI	http://dx.doi.org/10.1109/IC3I59117.2023.10703726
Abstract	Computers use machine learning, which is a collection of calculations and quantifiable models, to carry out anticipated tasks. Face recognition, conversation recognition, clinical determination, quantifiable exchange, traffic forecasting, and more tasks can be accomplished using machine learning. With the aid of central traffic-monitoring servers, GPS route has become widely used in recent years in determining traffic proportion in large cities. A concept illustrating the city's current traffic could be developed using the information acquired, and it could be used in the future to make traffic forecasts and allow for a clog analysis. Thus, forecasting traffic flow is the only emphasis of this endeavour. Two open data sets are used to prepare, approve, and test the proposed ML and DL models. The first contains the total number of vehicles counted at 6 intersections using various sensors over 56 days. The ML and DL models for this study are built using four of the six convergences. Slope Supporting, Intermittent Brain Organizations (RNNs), and Arbitrary Woods, Direct Relapse, and Stochastic Angle followed closely by the Multi-facet Perceptron Brain Organization (MLP-NN), which achieved better results while requiring less preparation time. All ML and DL calculations had excellent execution measurements, demonstrating their viability for use with intelligent traffic signal regulators. © 2023 IEEE.
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