Performance Evaluation Of Machine Learning Algorithms In Traffic Flow Prediction

Ramchandra, Nazirkar and Rajabhushanam, Dr. C. (2021) Performance Evaluation Of Machine Learning Algorithms In Traffic Flow Prediction. In: I3CAC 2021, 7-8 June 2021, Bharath University, Chennai, India.

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Abstract

The main and foremost reason for traffic congestion is overpopulation and the poor condition of the roads. This mostly happens in the urban cities where all the people in the urban areas go for some work or certain purposes. Due to the current growth of the communication technology various computing techniques are used to predict the outcome based on a given dataset. This research work uses four kinds of machine learning techniques line Deep AutoEncoder (DAN), Deep Belief Network (DBN), Random Forest (RF), and Long Short Term Memory (LSTM) to predict the traffic flow. This proposed system is implemented using Python programming. Lastly, the outcome describes that the proposed model using the LSTM technique produces 94.3% accuracy and less error value.

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
URI: https://eprints.eudl.eu/id/eprint/3859

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