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Development of Traffic Congestion Prediction Module Using Vehicle Detection System for Intelligent Transportation System  

Sin, Won-Sik (Korea Institute Robot Industry Advancement)
Oh, Se-Do (Department of Industrial and Management Systems Engineering, Kyung Hee University)
Kim, Young-Jin (Department of Industrial and Management Systems Engineering, Kyung Hee University)
Publication Information
IE interfaces / v.23, no.4, 2010 , pp. 349-356 More about this Journal
Abstract
The role of Intelligent Transportation System (ITS) is to efficiently manipulate the traffic flow and reduce the cost in logistics by using the state of the art technologies which combine telecommunication, sensor, and control technology. Especially, the hardware part of ITS is rapidly adapting to the up-to-date techniques in GPS and telematics to provide essential raw data to the controllers. However, the software part of ITS needs more sophisticated techniques to take care of vast amount of on-line data to be analyzed by the controller for their decision makings. In this paper, the authors develop a traffic congestion prediction model based on several different parameters from the sensory data captured in the Vehicle Detection System (VDS). This model uses the neural network technology in analyzing the traffic flow and predicting the traffic congestion in the designated area. This model also validates the results by analyzing the errors between actual traffic data and prediction program.
Keywords
ITS(Intelligent Transportation System); neural network; VDS(Vehicle Detection System); traffic congestion;
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