Deriving Traffic Flow Patterns from Historical Data
Publication: Journal of Transportation Engineering
Volume 138, Issue 12
Abstract
The development and decreased cost of technology and communications have brought about a huge increase in the availability of traffic data. With every passing day, traffic management centers must deal with an increased amount of detailed data. Once the real time use of these data is complete, they must be stored for long periods of time. In this long-term context, the vast amount of raw data is meaningless, which is a clear example of data asphyxiation. Traffic management centers must aggregate and synthesize the data to extract the maximum information from them. Pattern classification is a way to deal with this issue. Traditionally, traffic demand patterns have been easily constructed using ad hoc methods, where the experience and judgment of the analyst are their main attribute. These procedures lack the required rigor to support current needs in terms of planning and operational management. This paper proposes a quantitative method to systematically derive traffic demand patterns from historical data. The method is based on the cluster analysis technique and allows for the inclusion of preexisting knowledge, which eases the interpretation and practical use of the results. The proposed pattern classification procedure is applied to 5 years of hourly traffic volumes on a Spanish highway. The obtained results prove the validity and utility of the method in accurately summarizing the seasonal and daily characteristics of traffic demand.
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Acknowledgments
The author acknowledges the collaboration and data provision of Joan Altarriba and José V. Solano from Abertis and the dedication of Eduard Alberich, Pere Martí, and Eduard Molins in the early stages of the research. Thanks also go to my former colleague at CENIT, Dr. Dulce María Rosas, for her extensions on the model, which helped in its improvement. Last but not least, I would also like to acknowledge the support I have received from Prof. Francesc Robusté in this research. The contents of this paper reflect the views of the authors, who are responsible for the facts and accuracy of the data presented herein. This research was partially funded by the Abertis Foundation and by the Spanish Ministry of Science and Innovation (TRA2009-14270).
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Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 138 • Issue 12 • December 2012
Pages: 1430 - 1441
Copyright
© 2012 American Society of Civil Engineers.
History
Received: Oct 17, 2011
Accepted: May 16, 2012
Published online: May 21, 2012
Published in print: Dec 1, 2012
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