Short-term traffic flow forecasting using Sampling Markov Chain method with incomplete data

Abstract

Short-term traffic flow forecasting is an important problem in the research area of intelligent transportation system. In practical situations, flow data may be incomplete, that is, partially missing or unavailable, where few methods could implement forecasting successfully. A method called Sampling Markov Chain is proposed to deal with this circumstance. In this paper, the traffic flow is modeled as a high order Markov Chain; and the transition probability from one state to the other state is approximated by Gaussian Mixture Model (GMM) whose parameters are estimated with Competitive Expectation Maximum (CEM) algorithm. The incomplete data in forecasting the trend of Markov Chain is represented by enough points sampled using the idea of Monte Carlo integration. Experimental results show that the Sampling Markov Chain method is applicable and effective for short-term traffic flow forecasting in case of incomplete data.