A new fusion structure model for real-time urban traffic state estimation by multisource traffic data fusion

摘要

In order to meet the requirements of traffic data fusion for real-time urban traffic state estimation, a new kind of fusion structure model is proposed. This fusion model consists of both spatial fusion and temporal fusion. First we use the power average operator as spatial fusion method. Then we propose a temporal correlation based data compression (TCDC) algorithm, based on segment linear regression (SLR) algorithm. Extensive simulation results demonstrate the effectiveness and correctness of TCDC algorithm, as well as TCDC's advantage over SLR on overall performance.