This paper is published in Volume-7, Issue-3, 2021
Area
Machine Learning
Author
Dhanashree Dnyaneshwar Raut, Susmita Salvi, Ankita Gupta, Pranali Patil
Org/Univ
New Horizon Institute of Technology and Management, Thane, Maharashtra, India
Pub. Date
08 May, 2021
Paper ID
V7I3-1190
Publisher
Keywords
Random Forest, Decision Tree, SVM (Support Vector Machine), Machine Learning, Traffic Prediction

Citationsacebook

IEEE
Dhanashree Dnyaneshwar Raut, Susmita Salvi, Ankita Gupta, Pranali Patil. Traffic prediction for intelligent transportation system using machine learning, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Dhanashree Dnyaneshwar Raut, Susmita Salvi, Ankita Gupta, Pranali Patil (2021). Traffic prediction for intelligent transportation system using machine learning. International Journal of Advance Research, Ideas and Innovations in Technology, 7(3) www.IJARIIT.com.

MLA
Dhanashree Dnyaneshwar Raut, Susmita Salvi, Ankita Gupta, Pranali Patil. "Traffic prediction for intelligent transportation system using machine learning." International Journal of Advance Research, Ideas and Innovations in Technology 7.3 (2021). www.IJARIIT.com.

Abstract

Traffic is the most major issue in an urban area. The most problem comes because of traffic in all developed cities, So people suffer a lot of time in the road traffic. This time is totally a waste for me personally and for society. In most urbanized areas space is a scarce commodity. Therefore, better management of the existing roads to increase or maintain their capacity level is the only solution. So this project is to develop a tool for predicting accurate and also predict time to help the community. Because of accidents, traffic signals, even repairing the roads these problems are causing traffic. If we get information which is near about all above and many more daily life situations which can affect traffic jam then deriver take the decision on the basis of the situation to move or take other decision. In the current situation, traffic data have been generating exponentially. The already some available prediction methods for traffic flow but those are unsatisfactory to handle real-world application. In this project, we proposed to use a machine-learning algorithm to analyze the traffic with better performance