By Topic

Comparison of modelling approaches for short term traffic prediction under normal and abnormal conditions

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 $13
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

3 Author(s)
Fangce Guo ; Centre for Transport Studies, Imperial College London, SW7 2AZ, UK ; John W. Polak ; Rajesh Krishnan

Short-term prediction of traffic flows is an integral component of proactive traffic management systems. Prediction during abnormal conditions, such as incidents, is important for such systems. In this paper, three different models with increasing information in explanatory variables are presented. Time Delay and Recurrent Neural Networks and the k-Nearest Neighbour (kNN) algorithms are chosen as the machine learning tools in these models. The models are tested during both normal and incident conditions. The results indicate that historical patterns provide less predictive information during incidents.

Published in:

Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on

Date of Conference:

19-22 Sept. 2010