CFP last date
22 April 2024
Reseach Article

Traffic Density Identification based on Neural Network and Histogram

by Luong Anh Tuan Nguyen, Thi-Ngoc-Thanh Nguyen
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 172 - Number 9
Year of Publication: 2017
Authors: Luong Anh Tuan Nguyen, Thi-Ngoc-Thanh Nguyen
10.5120/ijca2017915202

Luong Anh Tuan Nguyen, Thi-Ngoc-Thanh Nguyen . Traffic Density Identification based on Neural Network and Histogram. International Journal of Computer Applications. 172, 9 ( Aug 2017), 8-13. DOI=10.5120/ijca2017915202

@article{ 10.5120/ijca2017915202,
author = { Luong Anh Tuan Nguyen, Thi-Ngoc-Thanh Nguyen },
title = { Traffic Density Identification based on Neural Network and Histogram },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2017 },
volume = { 172 },
number = { 9 },
month = { Aug },
year = { 2017 },
issn = { 0975-8887 },
pages = { 8-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume172/number9/28277-2017915202/ },
doi = { 10.5120/ijca2017915202 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:19:51.999112+05:30
%A Luong Anh Tuan Nguyen
%A Thi-Ngoc-Thanh Nguyen
%T Traffic Density Identification based on Neural Network and Histogram
%J International Journal of Computer Applications
%@ 0975-8887
%V 172
%N 9
%P 8-13
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The traffic density identification will support the traffic problems such as intelligent traffic signal control, traffic planning, etc. This paper proposes a traffic density identification method based on histogram and neural network. The system model was designed and evaluated with the traffic image datasets of Ho Chi Minh city. The best identifying result can obtain 96%.

References
  1. Ozkurt C, Camci F. Automatic traffic density estimation and vehicle classification for traffic surveillance systems using Neural Networks. Mathematical and Computational Applications. 2009; 14(3):187–96.
  2. C. Stuiz, T. A. Runkler, “Classification and Predicts of Road Traffic using Application Specific Fuzzy Clustering”, Fuzzy Systems, IEEE Transactions, pp. 297-308, 2002.
  3. Luong Anh Tuan Nguyen, Thi-Ngoc-Thanh Nguyen. Traffic Image Classification using Horizontal Slice Algorithm. International Journal of Computer Applications (ISSN: 0975 – 8887), Volume 148 – No.11, pp. 30-34, August 2016.
  4. Al Bovik (2000), Handbook of Image and Video Processing, Academic Press.
  5. Rafael C.Gonzalez, Richard E. Woods ( 1993), Digital Image Processing, Addison Wesley Pub.Comp.
  6. Luong Anh Tuan Nguyen, Huu Khuong Nguyen. Traffic Density Identification Based On Histogram. Journal of Transportation Science and Technology, ISSN: 1859-4263, Vol 15-05/2015, pp 23-27.
  7. Xiangyun Ye, Mohamed Cheriet, Senior Member, Ching Y. Suen (2001), Stroke-Model-Based Character Extraction from Gray-Level Document Images, IEEE.
  8. C. C. Sun. S. J. Ruan, M. C. Shie, T. W. Pai, “Dynamic Contrast Enhancement based on Histogram Specification,” IEEE Transactions on Consumer Electronics, 51(4), pp.1300-1305, 2005.
  9. Jiawei Han, Micheline Kamber (2006), Data Mining: Concepts and Techniques, Second Edition, Elsevier Inc. All rights reserved.
  10. C. Willmott, and K. Matsuura (2005), Advantages of the Mean Absolute Error (MAE) over the Root Mean Square Error (RMSE) in assessing average model performance, Clim. Res., 30, 79–82.
Index Terms

Computer Science
Information Sciences

Keywords

Traffic Image Histogram Neural Network Traffic Density.