Abstract
Real-time and accurate traffic speed is important for a successful traffic management system. However, the most common form of the single-loop detector is incapable of providing speed measurements. This paper presents a method of speed estimation from single-loop detector data using Bayesian network method. After analyzing the causal relationship between volume, occupancy, and speed, a Bayesian network model of speed estimation is proposed using volume and occupancy from single-loop outputs. The Gaussian mixture model (GMM) and the expectation-maximization (EM) algorithm are used to represent model and train model parameters, respectively. The proposed method is implemented and evaluated using the field data from urban expressways in Beijing. Estimated speeds are compared with the observed speed data and also with results from conventional algorithm. The results show that the proposed method is robust for every kind of sampling intervals, lanes, and traffic condition. The mean absolute error holds more than 2 km/h decrease. This method can be efficiently applied in traffic management system.
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