Study On Critical Theories And Methods About Advanced Traffic Management System(ATMS)

Advanced traffic management system(ATMS)refers to the advanced traffic control and information processing system, which is the core of Intelligent Transportation System(ITS). One of the main characteristics of ATMS is the high level integration of the system. It uses the advanced communication technology, computer technology, automatic control technology and video supervise technology to integrate some subsystems such as traffic planning, traffic signal control, traffic incident identification and information service into a whole system. So it can control and manage the traffic system real-timely through the computer network system. Another characteristic of ATMS is high-speed centralized communication and fast process. Because of advanced network technology, ATMS can get information fast, real-timely and accurately, so it can make the human-road-car system more harmonious. Finally, it can achieve the goals of shortening the travel time, reducing the traffic congestion, reducing the traffic incidents, saving energy and protecting the environment and smoothening the traffic flow. The research line of this dissertation is organized according to the process flow of a normal information system, namely from the demand analysis to information collecting, information processing, information organizing and information managing. The information demand of ATMS and some related problems are studied in this dissertation. ATMS is a classical information system, and its main users can be divided into two groups: traffic managers and traffic participators, who demand traffic information separately. In this dissertation, we analyze the information demand of kinds of users, and on the basis, we build the demand system of the traffic information, make clear the basic functions and define the basic traffic information and their characteristics. Furthermore, we design a hierarchical information fusion system structure. The first level, we process the basic traffic parameters, and the goal of this level is to gain the more précised data. And in the second level, it can process the data furthermore and provide the users more useful and decisive traffic information.Second, we study the techniques of the collection of traffic information. The basic traffic information can be divided into statistic traffic information and dynamic traffic information. The statistic traffic information can be usually collected by traditional survey or connected from other systems. And statistic information is usually inputted and stored manually. But dynamic traffic information is mainly collected by kinds of detectors real-timely. We analyze the characteristics and suitable situations of some detectors, including ultrasonic, active infrared, passive infrared, microwave radar, acoustic, video-image processing, aerial-video-image processing, inductive loops, and so on. On the basis we select some indexes, including data type, data precision, installation and maintenance, cost and environment affection, to compare the characteristics of these detectors. Furthermore, we provide a detector selection flow, so it can provide some scientific rules for the design of the collection subsystem of ATMS. Third, at the information processing part of ATMS, we study in three levels. Pre-treatment, the fusion process of basic traffic parameters and the aggregative process of traffic information. Because of the adverse circumstance of traffic information collection, it cannot be ensured that each data collected by the detectors is correct, so the raw data must be pretreated, including the supplement of the omitted data and the deleting of abnormal data. We analyze some pre-treatment methods in this dissertation, such as threshold value method, traffic flow mechanism method and so on, and we analyzed and contrasted their characteristics, at last we propose a combined pretreatment method that is called "threshold-traffic mechanism" method which is suitable to delete the "bad" data. And we use the time sequence prediction method to supple the omitted dat...