Data Fusion for Multi-Source Sensors Using GA-PSO-BP Neural Network

Abstract

The development of real-time road condition systems will better monitor road network operation status. However, the weak point of all these systems is their need for comprehensive and reliable data. For traffic data acquisition, two sources are currently available: 1) floating vehicles and 2) remote traffic microwave sensors (RTMS). The former consists of the use of mobile probe vehicles as mobile sensors, and the latter consists of a set of fixed point detectors installed in the roads. First, the structure of a three-layer BP neural network is designed to achieve the fusion of the floating car data (FCD) and the fixed detector data (FDD) efficiently. Second, in order to improve the accuracy of traffic speed estimation, a multi-source data fusion model that combines information from floating vehicles and microwave sensors, and that, by using GA-PSO-BP neural network is proposed. The proposed model has combined GA and PSO ingeniously. The hybrid model can not only overcome the difficulties of the traditional fusion model of its estimation inaccuracy, but also compensate the insufficiency of the traditional BP algorithm. Finally, this system has been tested and implemented on actual roads, and the simulation results show the accuracy of data has reached 98%.