Volume 20 No 12 (2022)
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Machine Learning Methodes Traffic Prediction for Intelligent Transportation System
Prashant Kumbharkar, Deepak Mane , Santosh Borde , Sunil Sangve
The fields of internet and medicine both make extensive use of machine learning and other data extraction techniques. The purpose of this research is to develop a method that is capable of providing accurate and up-to-date projections about traffic flow statistics. The term "traffic environment" refers to anything that might impact the flow of traffic along a road, including but not limited to traffic signals, accidents, demonstrations, and even road works that could cause a backlog in the flow of traffic. If a driver or passenger has access to prior knowledge that is very close to an estimate of all of the aspects of the level of life that have been stated above plus countless other factors that may have an influence on traffic, they are better equipped to make an educated choice. In addition to this, it contributes to the creation of cars that do not need drivers. Large-scale information ideas for transportation elicit an emotional reaction from us, and the amount of information available on traffic has been rapidly expanding over the last several decades. Although certain models for predicting traffic are employed in the real world to estimate the flow of traffic, these models are still not enough for dealing with applications that take place in the actual world. Because of this reality, a lot of people in the United States have been thinking about the shortcomings of forecasting traffic flow using information and models based on traffic. It is impossible to estimate the flow of traffic with any degree of precision since the quantity of information that is accessible to the transportation system is so large. The last several decades have seen a huge increase in the amount of data collected on traffic, and we are now working on implementing big data principles into the transportation sector. The methodologies that are now being used for estimating traffic flow make use of certain traffic prediction models; nevertheless, these models are still insufficient to deal with real-world scenarios. As a direct consequence of the aforementioned fact, we got to work on solving the challenge of forecasting the flow of traffic by utilizing the traffic data and models. It is difficult to effectively predict the flow of traffic since there is an insane quantity of data accessible for the transportation system. Using approaches such as machine learning, genetic programming, soft computing, and deep learning, our goal was to do an analysis on the huge amounts of data pertaining to the transportation system while simultaneously reducing the amount of complexity involved. In addition, image processing algorithms are employed to detect a traffic sign, which helps in the whole process of training autonomous cars in the correct manner.
Traffic Environment, Genetic algorithm, Machine Learning, Big Data, Image Processing
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