Next Article in Journal
Evaluation of the Bitterness of Traditional Chinese Medicines using an E-Tongue Coupled with a Robust Partial Least Squares Regression Method
Previous Article in Journal
Improved Circuits with Capacitive Feedback for Readout Resistive Sensor Arrays
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(2), 147; doi:10.3390/s16020147

3D Markov Process for Traffic Flow Prediction in Real-Time

Visual Information Processing Laboratory, Department of Internet & Multimedia Engineering, Konkuk University, Seoul 05029, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 8 August 2015 / Revised: 15 January 2016 / Accepted: 15 January 2016 / Published: 25 January 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [4252 KB, uploaded 25 January 2016]   |  

Abstract

Recently, the correct estimation of traffic flow has begun to be considered an essential component in intelligent transportation systems. In this paper, a new statistical method to predict traffic flows using time series analyses and geometric correlations is proposed. The novelty of the proposed method is two-fold: (1) a 3D heat map is designed to describe the traffic conditions between roads, which can effectively represent the correlations between spatially- and temporally-adjacent traffic states; and (2) the relationship between the adjacent roads on the spatiotemporal domain is represented by cliques in MRF and the clique parameters are obtained by example-based learning. In order to assess the validity of the proposed method, it is tested using data from expressway traffic that are provided by the Korean Expressway Corporation, and the performance of the proposed method is compared with existing approaches. The results demonstrate that the proposed method can predict traffic conditions with an accuracy of 85%, and this accuracy can be improved further. View Full-Text
Keywords: traffic flow forecasting; Markov process; spatio-temporal domain; vehicle detection sensor; heat map traffic flow forecasting; Markov process; spatio-temporal domain; vehicle detection sensor; heat map
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Ko, E.; Ahn, J.; Kim, E.Y. 3D Markov Process for Traffic Flow Prediction in Real-Time. Sensors 2016, 16, 147.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top