| With the development of society, the problem of traffic, such as traffic jam, traffic block, traffic accident, traffic pollution, has become bottleneck for the urban development, more and more. This matter not just trouble our country, even in the developed countries, the traffic problem is a very practical and serous problem. Just traffic jam costs the United States an estimated 100 billion per year. To confront with the traffic problem, people have been scratching our heads for a solution to the problem. Intelligent transportation system is the best acknowledged method to solve problems in traffic field at present. Travel time is a core parameter in intelligent transportation system. Real.time or quasi.Real.time travel time is a foundational base to subsystems of Intelligent Transport System (ITS), such as dynamic traffic information service system, signals coordination and control system and traffic guidance system, so, the effective estimation of travel time is a key problem. The accuracy of travel time estimation plays an important role in architecture of subsystems of Intelligent Transport System, the decrease of the losses of traffic problem and to improve the efficiency of traffic network. The estimation of travel time has been attracted more and more attention. This thesis briefly presents the present situation of the research on the estimation of travel time home and abroad,then, Upon the station of our urban traffic and the shortages of estimation methods nowadays,launches research.This dissertation is supported by"Technology of Feature Extraction and Integration of Regional Traffic System State"launched by the Chinese National Programs for High Technology Research and Development. Based on the characters of our urban traffic and The detection of traffic information, and integrating the estimation methods nowadays,development new method fitting our cities qualifications. In addition,to increase the accuracy of estimation,Research on the fusion of the estimated result. This research consists of five chapters. The main works and contributions are described as follows:Chapter 1: Introduction. This part firstly introduce the project the research based on and the research backgrounds of the dissertation, then looks back to the studied history of the estimation methods f travel time, and analyses the station of our urban traffic and the shortages of estimation methods nowadays, and illuminates the research purpose and significance of this dissertation. At last, provides the main contents and chapter arrangement of this paper.Chapter 2: the Study on the Estimation of Travel Time Based on Fixed Detectors. Before the estimation, there is a pivotal step, pre.processing of the traffic information Detected by fixed detectors to do first. Then,based on the need of the study in this chapter, defining the urban Road Section. In the estimation of travel time in this chapter, the travel time was divided into three part, running time, queue delay and crossing intersection time. Each part uses corresponding estimations. At last, using the simulator software, VISSIM4.20 makes a simulation to validate the effectiveness of the estimation in this Chapter, and making a profound analysis of the factors that influence the estimation of travel time.Chapter 3: the Study on the Estimation of Travel Time Based on Floating Cars. In this Chapter, the author introduces briefly Global Positioning System and Geographical Information System, and mainly analyses the system error of this two systems. Before estimating travel time using GPS information, pre.processing of GPS information based on floating cars should be do first,to weed out the fault information. The emphasis of this Chapter was map matching, estimation of the passed-time of road boundary point, and interrupted stop, and proposed new methodes to ameliorate this key steps. Using the actual taxi data proved the reasonableness and effectiveness of individual vehicle travel-time estimation method. At last, this part made a profound analysis of the factors,the sample size of floating cars and traffic state that influence the estimation of travel time by floating cars.Chapter 4: the Study on Estimation of Travel Time Based on Information Fusion. At the first, this Chapter analyzed the characteristic of estimation method presented in the paper and the necessity of data fusion, and improved the data fusion method of travel time estimating. In this paper, using adaptive weighted averaging fusion method to fuse the travel time that was estimated by information of fixed detectors and GPS floating cars. The paper ensured the weight factors using improved optimal estimation method. And based on the different factors, the travel time fusion was divided into eleven distribution types. Finally, this part validated the effectiveness of the fusion method.Chapter 5: Summary and Prospect. A summation was made to generalize the work in this paper. And suggestions were proposed for further research and improvement.
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