Improved -nn for Short-Term Traffic Forecasting Using Temporal and Spatial Information
Publication: Journal of Transportation Engineering
Volume 140, Issue 7
Abstract
This paper proposes an improved -nearest neighbor (-nn) model for short-term traffic forecasting and examines its applicability to forecasting for different links and time periods. The traditional -nn model is adapted by formulating the weighted distance metric and the state vector, which consider both the temporal and spatial information. The adapted model’s performance is examined in a numerical test where the data are derived from global positioning system (GPS) devices in 180 taxis running in Guiyang, China. The test results demonstrate that the model that considers both the temporal and spatial information outperforms models that only consider temporal information and that adaptation of distance metrics could significantly improve the forecasting accuracy. The adapted model shows the promising performance in comparison with the historical average (HA) model and the artificial neural network (ANN) model. The test results also indicate that information from the upstream and the downstream links plays almost the same important role in predicting the traffic conditions at the target link.
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Acknowledgments
This work was supported in National Natural Science Foundation of China 51078049 and 51108053, the Doctoral Program Foundation of Institutions of Higher Education of China through project 20112125110003, the Trans-Century Training Program Foundation for Talents from the Ministry of Education of China NCET-12-0752.
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Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 140 • Issue 7 • July 2014
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Jul 25, 2013
Accepted: Feb 5, 2014
Published online: Apr 18, 2014
Published in print: Jul 1, 2014
Discussion open until: Sep 18, 2014
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