Modified Bayesian data fusion model for travel time estimation considering spurious data and traffic conditions

Abstract

This paper presents a framework for the development of the travel time estimation model using multiple sources of data with consideration of spurious data and traffic conditions. A modified Bayesian data fusion approach, combined with the Gaussian mixture model, is used to fuse the travel time data, which are estimated from different types of sensors to improve accuracy, precision, as well as completeness of data, in terms of spatial and temporal distribution. Two additional features are added into existing models including the difference of traffic conditions classified by the Gaussian mixture model and the bias estimation from individual sensor by introducing a non-zero mean Gaussian distribution which learned from the training dataset. The methodology and computational procedure are presented. The Gaussian mixture model is used to classify states of traffic into predefined number of traffic regimes. Once a traffic condition is classified, the modified Bayesian data fusion approach is used to estimate travel time. The proposed model provides explicit advantages over the basic Bayesian approach, such as being robust to noisy data, reducing biases of an individual estimation, and producing a more precise estimation of travel time. Two different real-world datasets and one simulated dataset are used to evaluate the performance of the proposed model under three different traffic regimes: free flow, transitional flow and congested flow regimes. The results when compared with the results from benchmark models show significant improvement in the accuracy of travel time estimation in terms of mean absolute percentage errors (MAPE) in the range of 3.46% to 16.3%.