Traffic Prediction System Using Machine Learning Algorithms

Ramchandra, Nazirkar Reshma and Rajabhushanam, Dr. C. (2021) Traffic Prediction System Using Machine Learning Algorithms. In: I3CAC 2021, 7-8 June 2021, Bharath University, Chennai, India.

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Traffic congestion is defined as the state on transport which is characterized by slower speeds of vehicles this is also because of the bad condition of the roads, weather, concern zone, temperature, etc. This traffic flow prediction is mainly based on the real-time dataset which is collected with the help of various cameras and sensors. In recent day the deep learning concepts has dragged the attention for the detection of traffic flow predictions. In this paper, some of the common and familiar machine learning concepts like Deep Autoencoder (DAN), Deep Belief Network (DBN), and Random Forest (RF) are applied on the online dataset for the traffic flow predictions. The important attributes of weather, temperature, zone name, and day are used to predict the traffic flow of the particular zone. The performance of the proposed system can be evaluated by using accuracy, precision, and RMSE, and MSE value. Among the three methods, the DT technique produces a better result.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: traffic flow machine learning prediction accuracy recall performance
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor IV
Date Deposited: 21 Jun 2021 08:09
Last Modified: 21 Jun 2021 08:09

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