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Traffic Less Navigation with Haversine Formula and RPA Algorithm |
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| รหัสดีโอไอ | |
| Creator | Nyeinchan Soe |
| Title | Traffic Less Navigation with Haversine Formula and RPA Algorithm |
| Publisher | Faculyt of Informatics, Mahasarakham University |
| Publication Year | 2563 |
| Journal Title | Journal of Applied Informatics and Technology |
| Journal Vol. | 2 |
| Journal No. | 1 |
| Page no. | 30-45 |
| Keyword | Traffic jams, Cloud database, Public transporation, Haversine |
| URL Website | https://ph01.tci-thaijo.org/index.php/jait/article/view/234907 |
| Website title | Journal of Applied Informatics and Technology |
| ISSN | 2586-8136 |
| Abstract | 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. A system that displays changes in the position, speed, and direction of vehicles traveling on the streets of Yangon using traffic speeds and route pattern algorithms. The 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 cloudVirtual Machine (VM). |
