Research On The Applications Of Information Fusion In Traffic Parameter Estimation Based On Probe Vehicles

Intelligent transportation system (ITS) is an information-based system, where each sub-system and its function attach most importance to the application of traffic information. As a result, the validity of ITS depends highly on the quality of traffic information. To collect real-time, all-around, and accurate traffic information is the key of making urban transportation intellectualized and the important premise of implementing of ITS successfully. This dissertation makes researches on the information fusion of urban traffic information. Several related research works, such as FCD (Floating Car Data) algorithm and traffic information prediction etc., have been studied in this dissertation as well.The main works in the thesis are introduced as follows.1. To study the basic theory of information fusion technology. From a point of view, the hierarchy, the functional model, the structural model and the mathematical model of information fusion are discussed respectively.2. To study the traffic information collection method based on FCD. Firstly presented a theoretical method for floating-car sampling cycle optimization: regarding speed as a stochastic signal, analyzed its frequency spectrum using Fourier transform, and decided the optimal sampling frequency by Shannon sampling theory, then this paper discusses the estimation algorithm of vehicle mean speed based on GPS data, especially under different conditions of the data according to the communication. The experiment shows the algorithm is reasonable.3. Fixed detectors and mobile detectors are different at coverage area. They are complementary. The necessity of data fusion for fixed detectors and floating cars was analyzed. The architecture of traffic data fusion was proposed, which wad on the basis of discussing average speed calculating models based on fixed detectors and floating cars.4. Using Rough Set theory to syncretize multi-sensor information rooted in property reduction, value reduction, and nucleus and incomplete information system etc. According to complete information system and incomplete information system, the corresponding amalgamation algorithms are shown, which provide an effective method to deal with overloading data of sensors and information amalgamation for incomplete sensor.