CFP last date
22 April 2024
Reseach Article

Traffic Congestion Control based In-Memory Analytics: Challenges and Advantages

by Aiman Abdul-Razzak Fatehi Al-Sabaawi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 170 - Number 6
Year of Publication: 2017
Authors: Aiman Abdul-Razzak Fatehi Al-Sabaawi
10.5120/ijca2017914890

Aiman Abdul-Razzak Fatehi Al-Sabaawi . Traffic Congestion Control based In-Memory Analytics: Challenges and Advantages. International Journal of Computer Applications. 170, 6 ( Jul 2017), 39-42. DOI=10.5120/ijca2017914890

@article{ 10.5120/ijca2017914890,
author = { Aiman Abdul-Razzak Fatehi Al-Sabaawi },
title = { Traffic Congestion Control based In-Memory Analytics: Challenges and Advantages },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2017 },
volume = { 170 },
number = { 6 },
month = { Jul },
year = { 2017 },
issn = { 0975-8887 },
pages = { 39-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume170/number6/28078-2017914890/ },
doi = { 10.5120/ijca2017914890 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:17:48.673911+05:30
%A Aiman Abdul-Razzak Fatehi Al-Sabaawi
%T Traffic Congestion Control based In-Memory Analytics: Challenges and Advantages
%J International Journal of Computer Applications
%@ 0975-8887
%V 170
%N 6
%P 39-42
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A method for loading detailed data from more than one source into the main memory in traffic system is presented. The challenges include data volume, data velocity, and Data variation of the traffic. The method uses In-memory analytics to improve query evaluation, to analysis and process reports in the traffic system. This occurs with the development of multicore processors, the loading of data, and the way image and video traffic data are stored and transferred into/from the data centers of different divisions and centralizing access to traffic management facilities, equipment and application systems.

References
  1. Herring, R. J. 2010. Real-time traffic modeling and estimation with streaming probe data using machine learning. Retrieved from http://search.proquest.com.ezp 01.library.qut.edu.au/docview/859247684?pq-origsite=summon.
  2. Herrera, J. C., Work, D. B., Herring, R., Ban, X., Jacobson, Q., and Baye, A. M. 2010. Evaluation of traffic data obtained via GPS-enabled mobile phones: The mobile century field experiment. Transportation Research Part C, vol.18, issue 4, (2010), 568-583. doi:10.1016/j.trc.2009.10.006.
  3. How Scoot Works. Retrieved from http://www.scoot-utc.com/HowSCOOTWorks.php
  4. Garber, L. 2012. Using in-memory analytics to quickly crunch big data. Computer, vol. 45, issue 10, (2012),16-18. doi:10.1109/MC.2012.358.
  5. Obuhuma, J. I. and Moturi, C.A., 2012. Use of GPS with road mapping for traffic analysis. International Journal of Scientific and technology research.
  6. Cárdenas-Benítez, N., Aquino-Santos, R., Magaña-Espinoza, P., Aguilar-Velazco, J., Edwards-Block, A. and Medina Cass, A., 2016. Traffic congestion detection system through connected vehicles and big data. Sensors, 16(5), p.599.
  7. OECD, 2015. Big Data and Transport: Understanding and Assessing Options. Retrieved from http://www.internationaltransportforum.org.
  8. IBM, 2016. Traffic Management for a Smarter Planet. Retrieved from http://www.ibm.com/smarterplanet/us/en/ traffic_congestion/article/traffic-management-and-prediction.html.
  9. Hota, J. 2013. Adoption of In-Memory Analytics. CSI Communications, 20. Chicago. Retrieved from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2250411.
  10. Woo, B. 2014. Oracle’s ‘In-Memory’ option may be costly. Retrieved from http://www.neuralytix.com/blog/ 2014/06/11/oracles-in-memory-option-may-be-costly/
  11. Sheldon, R. 2015. Oracle in-memory database option: Intro and implementation. Retrieved from http://searchoracle.techtarget.com/tip/Oracle-in-memory-database-option-Intro-and-implementation.
  12. Haitao, C. 2013. Improving traffic management with big data analytics. Retrieved from http://www.intel.com.au/content/dam/www/public/us/en/documents/case-studies/big-data-xeon-e5-trustway-case-study.pdf.
  13. Karena, C. 2012. Top 6 big data issues. Retrieved from http://www.smh.com.au/it-pro/business-it/top-6-big-data-issues-20120816-24a6w.html#ixzz3km2jR0OC.
  14. Stelle, G., Olivier, S. L., Stark, D., Rodrigues, A. F. and Hemmert, K. S. 2014. Using a complementary emulation-simulation co-design approach to assess application readiness for processing-in-memory systems. (2014), 64-71. doi:10.1109/Co-HPC.2014.5.
Index Terms

Computer Science
Information Sciences

Keywords

Traffic Congestion In-memory analytics Real-time Data volume Data variety Data velocity Big data.