Improved Traffic State Estimation by Bayesian Network Data Fusion of V2X and Vehicular Bluetooth Data

Abstract

A recently published probabilistic method for vehicle based traffic state estimation on the basis of fusing two wireless communication based technologies, i.e. Bluetooth and Vehicle-to-X communication data, is analyzed in three different scenarios ranging from academic to realistic. On the one hand there is accurate, but extremely rare V2X speed data and on the other, there is frequent, but inaccurate speed data based on a one unit Bluetooth reader approach. Therefore, this analysis takes into account specific traffic related variables, such as traffic flow and traffic density as well as traffic light control (TLC), which affect the Bluetooth based detection results. The findings are used to improve the method. A Bayesian Network (BN) is developed that merges Bluetooth and V2X based speed detection results to provide an improved speed estimation. The novel BN is compared to the previous one for different V2X penetration ratios using the open source microscopic traffic simulation package SUMO (Simulation of Urban MObility). The results show that the novel method can improve the vehicular speed estimation in the academic as well as in the realistic scenarios.