Time Series Prediction for Traffic Flow Forecasting Using CNN
N Prakash
Department of Information Technology, Sreenidhi Institute of Science and Technology (Autonomous), Hyderabad, India.
B Sharan *
Department of Information Technology, Sreenidhi Institute of Science and Technology (Autonomous), Hyderabad, India.
K Anil
Department of Information Technology, Sreenidhi Institute of Science and Technology (Autonomous), Hyderabad, India.
K Rithwik
Department of Information Technology, Sreenidhi Institute of Science and Technology (Autonomous), Hyderabad, India.
*Author to whom correspondence should be addressed.
Abstract
Traffic problems are very common nowadays throughout the world. In India also heavy traffic also occurred in many populated cities. For this reason, the public loses their time and life, and this pollution impacts human health. So, traffic research is necessary for this situation. We concentrate on the traffic network to find a better solution or model to predict future traffic. Our proposed model uses a time series for traffic forecasting. It deals with time series analysis for traffic congestion, traffic control, and traffic prediction. This paper focuses on appropriate datasets with different vehicles in various time series. A novel time series forecasting model was used for this research, and it also predicted a 99% accuracy rate. A comparative study is also presented in this research.
Keywords: Time series, previous trends, machine learning, traffic flow