Haversine Formula and RPA Algorithm for Navigation System

Nyein Chan Soe; Thin Lai Lai Thein

doi:doi:10.11648/j.ijdsa.20200601.14

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Haversine Formula and RPA Algorithm for Navigation System

Nyein Chan Soe, Thin Lai Lai Thein

Published in International Journal of Data Science and Analysis (Volume 6, Issue 1)

Received: 22 January 2020 Accepted: 11 February 2020 Published: 19 February 2020

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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. 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).

Published in	International Journal of Data Science and Analysis (Volume 6, Issue 1)
DOI	10.11648/j.ijdsa.20200601.14
Page(s)	32-40
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Traffic Jams, Cloud Database, Public Transportation, Haversine

References

[1]	Cory. Brose Saint Mary's University of Minnesota, Graduate Studies in Resource Analysis, 700 “Geographic Information Systems for Spatial Analysis of Traffic Collision Locations in La Crosse, Wisconsin”, Terrace Heights #10; Winona, Minnesota, 55987, USA.
[2]	ESRI, 380 New York Street Redlands, California 92373-8100 usa, “GIS Solutions for Highway and Roadway Management”, Copyright © 2011 Esri. All rights reserved. Esri.
[3]	G. Leduc, “Road Traffic Data: Collection Methods and Applications, Working Papers on Energy, Transport and Climate Change,” N. 1, JRC European Commission, 47967, 2008.
[4]	Cheng Hian Goh*, Hongjun Lu, Beng-Chin Ooi, Kian-Lee Tan, Department of Information Systems and Computer Science, National University of Singapore, “Indexing temporal data using existing B +-trees”, Data & Knowledge Engineering 18 (!996) 147-165.
[5]	K. Shekhar. Rahane, Prof. U. R. Saharkar, Pg student in Civil Engineering (Construction and Management), Dr. D. Y. Patil Institute of Engineering and Technology Ambi, University of Pune, Maharashtra, India, “Technique Identification For Road Traffic Congestion Solution In TalegaonDabhade State Highway-55”, Journal Of Information, Knowledge And Research In Civil EngineeringISSN: 0975 – 6744\| NOV 13 TO OCT 14 \| Volume 3, Issue1.
[6]	S. AnithaSelva S. D., Nithyaa. R, Prince Arulraj G, Assistant Professor, Department of Civil Engineering, SNS College of Technology, Coimbatore, Tamilnadu, India, “Minimizing the Traffic Congestion Using GIS”, IJREAT International Journal of Research in Engineering & Advanced Technology, Volume 1, Issue 1, March, 2013 ISSN: 2320 - 8791 www.ijreat.org.
[7]	Jan Jannink, Stanford University, Computer Science Dept. Stanford, “Implementing Deletion in B+ Trees,” CA 94305.
[8]	Yangon Region Government, Yangon City Development Committee; YCDC, Japan International Cooperation Agency; JICA, JICA Study Team, “Yangon 2040 The Peaceful and Beloved Yangon ¯A City of Green and Gold¯”, The Strategic Urban Development Plan of the Greater Yangon March, 2013.
[9]	Alfio Costanzo, ‘Using GPS data to monitor road traffic flows in a metropolitan area: methodology and case study’, (ICCSEE 2013).
[10]	MD. Al Amin, MD. Rofi Uddin, Supervised by Mrs. Sadia Hamid Kazi, ‘Real Time Traffic Monitoring System Using Crowd Sourced GPS Data’.
[11]	Cui Yu+ James Bailey Julian Montefusco Rui Zhang Jiling Zhong Monmouth University, USA “Enhancing the B+-tree by Dynamic Node Popularity Caching”.
[12]	Henry Stern, Partial Fulfillment of The Requirements For The Degree Of Bachelor Of Computer Science, “Nearest Neighbour Matching Using Kd-Trees”, Dalhousie University Halifax, Nova Scotia August 2002.
[13]	Borko Furht, “Handbook of Cloud Computing – 2010, Cloud Computing Fundamentals”, Return to Bookmetrix summary, Handbook of Cloud Computing, About Affiliation, DOI -10.1007/978-1-4419-6524-0_1.
[14]	Dr. Mohammed Otair, Department of Computer Information Systems, Amman Arab University, Amman, Jordan, “Approximate KNearest Neighbour Based Spatial Clustering Using K-D Tree”, International Journal of Database Management Systems ( IJDMS ) Vol. 5, No. 1, February 2013.

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APA Style

Nyein Chan Soe, Thin Lai Lai Thein. (2020). Haversine Formula and RPA Algorithm for Navigation System. International Journal of Data Science and Analysis, 6(1), 32-40. https://doi.org/10.11648/j.ijdsa.20200601.14

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ACS Style

Nyein Chan Soe; Thin Lai Lai Thein. Haversine Formula and RPA Algorithm for Navigation System. Int. J. Data Sci. Anal. 2020, 6(1), 32-40. doi: 10.11648/j.ijdsa.20200601.14

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AMA Style

Nyein Chan Soe, Thin Lai Lai Thein. Haversine Formula and RPA Algorithm for Navigation System. Int J Data Sci Anal. 2020;6(1):32-40. doi: 10.11648/j.ijdsa.20200601.14

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@article{10.11648/j.ijdsa.20200601.14,
  author = {Nyein Chan Soe and Thin Lai Lai Thein},
  title = {Haversine Formula and RPA Algorithm for Navigation System},
  journal = {International Journal of Data Science and Analysis},
  volume = {6},
  number = {1},
  pages = {32-40},
  doi = {10.11648/j.ijdsa.20200601.14},
  url = {https://doi.org/10.11648/j.ijdsa.20200601.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20200601.14},
  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. 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).},
 year = {2020}
}

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TY - JOUR
T1 - Haversine Formula and RPA Algorithm for Navigation System
AU - Nyein Chan Soe
AU - Thin Lai Lai Thein
Y1 - 2020/02/19
PY - 2020
N1 - https://doi.org/10.11648/j.ijdsa.20200601.14
DO - 10.11648/j.ijdsa.20200601.14
T2 - International Journal of Data Science and Analysis
JF - International Journal of Data Science and Analysis
JO - International Journal of Data Science and Analysis
SP - 32
EP - 40
PB - Science Publishing Group
SN - 2575-1891
UR - https://doi.org/10.11648/j.ijdsa.20200601.14
AB - 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).
VL - 6
IS - 1
ER -

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Author Information

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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APA Style

Nyein Chan Soe, Thin Lai Lai Thein. (2020). Haversine Formula and RPA Algorithm for Navigation System. International Journal of Data Science and Analysis, 6(1), 32-40. https://doi.org/10.11648/j.ijdsa.20200601.14

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ACS Style

Nyein Chan Soe; Thin Lai Lai Thein. Haversine Formula and RPA Algorithm for Navigation System. Int. J. Data Sci. Anal. 2020, 6(1), 32-40. doi: 10.11648/j.ijdsa.20200601.14

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AMA Style

Nyein Chan Soe, Thin Lai Lai Thein. Haversine Formula and RPA Algorithm for Navigation System. Int J Data Sci Anal. 2020;6(1):32-40. doi: 10.11648/j.ijdsa.20200601.14

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@article{10.11648/j.ijdsa.20200601.14,
  author = {Nyein Chan Soe and Thin Lai Lai Thein},
  title = {Haversine Formula and RPA Algorithm for Navigation System},
  journal = {International Journal of Data Science and Analysis},
  volume = {6},
  number = {1},
  pages = {32-40},
  doi = {10.11648/j.ijdsa.20200601.14},
  url = {https://doi.org/10.11648/j.ijdsa.20200601.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20200601.14},
  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. 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).},
 year = {2020}
}

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