Real-time highway traffic flow estimation based on 3D Markov Random Field

Nowadays, traffic flow estimation is the one of the most important topics in intelligent transportation systems (ITS). Accordingly, we propose a traffic flow estimation method using time-series analysis and geometric correlation. Firstly, we define a 3D heat-map to present the traffic state and spatial and temporal adjacent traffic condition. Thereafter, we model the dependency heat-map using spatiotemporal Markov Random Field and estimate the probability using logistic regression. To evaluate the performance of the proposed method, it was tested using data collected from expressway traffic that were provided by the Korean Expressway Corporation, and its performance was compared with those of other existing approaches. The results showed that the proposed method has a superior accuracy to others method, which has the accuracy of 85%.