Haversine Formula and RPA Algorithm for Navigation System
International Journal of Data Science and Analysis
Volume 6, Issue 1, February 2020, Pages: 32-40
Received: Jan. 22, 2020; Accepted: Feb. 11, 2020; Published: Feb. 19, 2020
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Nyein Chan Soe, GIS Department, University of Computer Studies, Yangon, Myanmar
Thin Lai Lai Thein, GIS Department, University of Computer Studies, Yangon, Myanmar
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The system uses a geographic information system to analyze and monitor traffic congestion and use GPS data for public transport planning in Yangon, Myanmar. The system provides accurate maps for estimating traffic conditions more efficiently from GPS data, saving more time. The proposed system displays changes in the position, distance and direction of vehicles traveling on the streets of Yangon by using traffic state and routing pattern algorithm. There established centralized GPS server database infrastructure provides any kind of analysis that requires GPS traffic data stored in a distributed client-server environment. In this system, a statement of user desired traffic jams between the source and destination is estimated and the results are presented with a Map. This system is for analyzing traffic data, avoiding traffic congestion and obtaining optimal routes with a modified A* algorithm. GPS data (current location) and user search area using the K-d tree and Haversine algorithm are required. Second, look for traffic jam data with Google's traffic layer and the routing matrix pattern algorithm. Finally, Analysis the traffic by Smart-A* and then show the result of traffic congestion statement and best optimal route. In the case, there are three main components: Data Collection, Data Extraction and Implementation. And this is Client-Server database system that storing the data and server in the cloud Virtual Machine (VM).
Traffic Jams, Cloud Database, Public Transportation, Haversine
To cite this article
Nyein Chan Soe, Thin Lai Lai Thein, Haversine Formula and RPA Algorithm for Navigation System, International Journal of Data Science and Analysis. Vol. 6, No. 1, 2020, pp. 32-40. doi: 10.11648/j.ijdsa.20200601.14
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