By Topic

Traffic congestion identification based on image processing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $33
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
H. Jianming ; Department of Automation, Tsinghua University ; M. Qiang ; W. Qi ; Z. Jiajie
more authors

Accurate and real-time traffic information is the foundation of intelligent transportation systems (ITS). In general, density, velocity and flow are used to describe traffic status of certain road segment. However, these macroscopic parameters are not able to reflect detailed traffic scenarios. It is more valuable to detect traffic congestion, which can be the basis of dynamic control and real-time guidance. This study proposes a novel approach towards traffic congestion identification based on vehicle trajectories in intelligent vehicle infrastructure co-operation system (IVICS). Considering spatial-temporal trajectories as image, this study uses self-correlation to extract propagation speed of congestion wave. Based on this, this study constructs congestion template; by matching algorithm, congestion is further identified as well as its intensity. Simulations on next generation simulation (NGSim) dataset verify the effectiveness of the above methods.

Published in:

IET Intelligent Transport Systems  (Volume:6 ,  Issue: 2 )