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Implementation of a GPS-based AVL System for Mazandaran Province
M.R.Mosavi and A.Ghadiri Department of Electrical Engineering, Behshahr University of Science and Technology, Behshahr 48518-78413, Iran Tel.: 0152-1525242001, Fax.: 0152-1525242004 Email: M_Mosavi@iust.ac.ir, Ghadiri@iust.ac.ir
Abstract:
Implementation of a GPS-based Automatic Vehicle Location (AVL) system for Mazandaran province, a northern province of Iran, has been presented in this paper. This system is composed of three main parts: computer-aided dispatch software, mobile data terminals and digital communications. A fuzzy map matching method was implemented for software, which uses a digital road map to correct the GPS position error. The proposed algorithm is easy to calculate. It requires little computation time without need to extra sensors and can find the mobile exact position which moves on the road. Mobile data terminals are composed of low cost GPS receiver and a microcontroller chip. The real road experiments demonstrate the effectiveness and applicability of the implemented algorithm for accurate navigation with low cost GPS receivers. The results show that real-time positioning error reduces from 25m to less than 1.5m using proposed fuzzy map matching algorithm. The software was equipped with many capabilities which can be classified in two main categories. The first category was implemented as a Graphic User Interface (GUI). Instantaneous position of mobile vehicle on the map with zooming capability and the momentary geographical position of mobile vehicle can be viewed graphically. Moreover, contents of message103 such as date and time, velocity, error factors and channels status of GPS receiver can be extracted for each mobile vehicle. Other category of software capabilities is searching and path-finding. The best path for a mobile vehicle to a desired destination can be found instantaneously based on determined criteria. The implemented AVL system is used in some cities of Mazandaran province. I. Introduction Automatic Vehicle Location (AVL) is a computer-based vehicle tracking system. For transit, the actual real-time position of each vehicle is determined and relayed to a control center. Actual position determination and relay techniques vary, depending on the needs of the transit system and the technologies employed. Simple AVL systems include: computer-aided dispatch software, mobile data terminals, emergency alarms, and digital communications. The dominant AVL technology deployed today is GPS. The mobile determined positions using GPS receivers could be displayed in electronic map. However, because of errors exist in GPS and also in digital maps, it is not possible to ensure that mobile positions register properly on a digital map. To avoid this problem, map matching method can be used to improve mobile position display precision over an electronic map [1]. A map matching method using a digital road map is a useful approach to correct these errors. There are a lot of map matching methods based on the geometric approaches. These methods find the road which is close to the mobile trajectory. In these algorithms, it is difficult to calculate the nearest road and it requires hard calculations, which leads to a time consuming. Another map matching algorithm is to use conditional probability. This algorithm is more robust than geometric method and it can cover from false positioning quickly. However, it requires more computation time and more memory to store the mobile trajectory [2,3]. In this paper, a new fuzzy map matching algorithm is proposed for reducing of GPS position error in AVL application. It is easy to implement and requires little computation. Also, this method can find the mobile exact position on the road. Implemented software of the GPS-based AVL system exploits this method for precise map matching. The effectiveness and applicability of the proposed algorithm is verified by the real road experiments. This paper is organized as follow. Section II explains proposed fuzzy map matching algorithm. Section III explains computer-aided dispatch center and path finding approaches will be discussed in section IV. Finally conclusions are explained in section V. II. Proposed Fuzzy Map Matching Algorithm Design The block diagram of proposed fuzzy system is shown in Fig.1. (error between the mobile measured position and the desired route) and (the mobile measured position distance to the desired route) are used as input fuzzy variables to this fuzzy system. is fuzzy system output and is defined as rotation angle of trajectory vector for mapping. , , and are divided into five segments for partition the rule space. The membership functions are defined as Fig.2, where LN, SN, ZE, SP and LP express Large Negative, Small Negative, Zero, Small Positive and Large Positive, respectively. Seventeen rules in the rule base are defined as Table1.  Fig.1. The block diagram of proposed fuzzy system  Fig.2. Membership functions for q, q', and d 
The Mamdani-Style method with is used for the inference process and the center of area method is employed for the defuzzification [4,5,6]. III. Computer-Aided Dispatch Center Vector-scan maps were used in computer-aided dispatch center. In order to generate a map the following process applied. At first, precise paper map was prepared for each city since digital maps were not accessible while this project was being done. Prepared maps were scanned, resized and modified for each city of Mazandaran province. To modify scanned maps Macromedia software along with Flash software was used. Then a new layer was added to map file and the lines of main layer were redrawn in new layer based on scanned map. After deleting the main layer, new layer was saved as SWF file and used by software. Since WGS84 geographical system used for GPS is 3-D systems and our maps are 2-D, and usual mapping approaches such as UTM have computational overload, we used proportional approach to map received data of GPS. Neglecting partial effect of earth curvature we can use following relations to compute and coordinate on the map: 
where Longmin, Longmax, Latmin and Latmax are minimum longitude, maximum longitude, minimum latitude and maximum latitude on the map respectively. The software was equipped with many capabilities which can be classified in two main categories. The first category was implemented as a Graphic User Interface (GUI). Instantaneous position of mobile vehicle on the map with zooming capability and the momentary geographical position of mobile vehicle can be viewed graphically. Moreover, contents of message103 such as date and time, velocity, error factors and channels status of GPS receiver can be extracted for each mobile vehicle. Other category of software capabilities is searching and path-finding. The best path for a mobile vehicle to a desired destination can be found instantaneously based on determined criteria. Fig.3 shows the main page of implemented GUI. User can find a location in a city by entering the location and city names. Related information of the desired location and the adjacent node will be displayed on the message and results window. Nodes will be explained later. Connecting and disconnecting time of mobile vehicle are of other information which is shown in this window. Information section displays important data of a connected mobile vehicle such as mobile position based on WGS-84 coordination system. Map matching implemented on right section of the main page. City map along with other information such as its population and geographical position will be demonstrated on right section by choosing the name of a city. For easy mobile tracking and effective implementation of path finding algorithm, each city of Mazandaran province will be determined by some nodes. Fig.4 shows the map of part of Behshahr city, eastern city of Mazandaran province, along with nodes used for map matching.  Fig.3. Main page of implemented GUI  Fig.4. Map of part of Behshahr city, eastern city of Mazandaran province
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