Abstract:
This paper, based on the Kalman filter theory, established short-term traffic volume model. Kalman filter model has a good static stability, and adopts iterative method f...Show MoreMetadata
Abstract:
This paper, based on the Kalman filter theory, established short-term traffic volume model. Kalman filter model has a good static stability, and adopts iterative method for optimal estimation of traffic. But the regular Kalman filtering traffic volume prediction established without considering the influencing factors on traffic and time-lag. Analyzing the influence factors of traffic volume based on grey entropy, and selects the main influencing factors by the size of grey entropy to establish the prediction of short-term traffic volume. Based on this, utilize the internet of things for data collection, and make a simulation experiment on a road in Shenyang. Simulation results show that model has good adaptability, high prediction accuracy on various states of traffic volume. It is a kind of effective traffic flow forecasting model.
Published in: 2013 6th International Conference on Intelligent Networks and Intelligent Systems (ICINIS)
Date of Conference: 01-03 November 2013
Date Added to IEEE Xplore: 24 July 2014
ISBN Information: