Notification: Our email services are now fully restored after a brief, temporary outage caused by a denial-of-service (DoS) attack. If you sent an email on Dec 6 and haven't received a response, please resend your email.
CFP last date
20 December 2024
Reseach Article

Automated Traffic Control System using Big Data and Cognitive Analysis

by Pooja Kudav, Pranav Acharya
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 151 - Number 10
Year of Publication: 2016
Authors: Pooja Kudav, Pranav Acharya
10.5120/ijca2016911924

Pooja Kudav, Pranav Acharya . Automated Traffic Control System using Big Data and Cognitive Analysis. International Journal of Computer Applications. 151, 10 ( Oct 2016), 25-28. DOI=10.5120/ijca2016911924

@article{ 10.5120/ijca2016911924,
author = { Pooja Kudav, Pranav Acharya },
title = { Automated Traffic Control System using Big Data and Cognitive Analysis },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2016 },
volume = { 151 },
number = { 10 },
month = { Oct },
year = { 2016 },
issn = { 0975-8887 },
pages = { 25-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume151/number10/26271-2016911924/ },
doi = { 10.5120/ijca2016911924 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:56:46.192725+05:30
%A Pooja Kudav
%A Pranav Acharya
%T Automated Traffic Control System using Big Data and Cognitive Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 151
%N 10
%P 25-28
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Today people spend about 4.8 billion hours every year in congestion which could be used productively. The traffic control system currently being used is outdated and heavily dependent on humans. Thus, there is a dire need to upgrade and automate these systems. Using combination of computer vision, big data and machine learning it is possible to design a reliable and scalable system which will help to resolve these traditional problems. This paper provides an insight of how these technologies can be used and the challenges which we will have to face.

References
  1. Radha Shankarmani, M. Vijayalakshmi,“Big Data Analytics”, Wiley India Pvt. Ltd.
  2. Apache Hadoop, http://hadoop.apache.org/.
  3. Apache Cassandra, http://cassandra.apache.org/.
  4. MongoDB, https://en.wikipedia.org/wiki/MongoDB.
  5. OpenCV, http://opencv.org/.
  6. CouchDB, http://couchdb.apache.org/
  7. Brandon Rohner, “How to choose algorithms for Microsoft Azure Machine Learning”. Web. 10 September 2016. https://azure.microsoft.com/en-in/documentation/articles/machine-learning-algorithm-choice /.
  8. Traffic Data Collection and Analysis,Ministry of Works and Transport Roads Department Private Bag 0026 Gaborone, Botswana-February2004 “http://www.vegvesen.no/_attachment/336339/binary/585485”
  9. Jason Brownlee, “Linear regression for machine learning” (2016). Web.12 September 2016. http://machinelearningmastery.com/linear-regression-for-machine-learning/.
  10. Gang Zeng, “Application of big data in intelligent traffic Systems” IOSR Journal Of Computer Engineering (IOSR-JCE), Volume 17(1) e-ISSN: 2278-0661, p-ISSN: 2278-8727.
  11. Michel Walker, “Machine Learning Work Flow”, Retrieved from http://www.datascienceassn.org/ content/machine-learning-workflow
  12. Fei-Fei Li. (2015, March).Fei-Fei Li: How we’re teaching computers to understand pictures. [Video file].Retrieved from https://www.ted.com/talks/fei_fei_li_how_we_re_teaching_computers_to_understand_picture.
Index Terms

Computer Science
Information Sciences

Keywords

Automated traffic control monitor CCTV