Combining the Statistical Model and Heuristic Model to Predict Flow Rate
Publication: Journal of Transportation Engineering
Volume 140, Issue 7
Abstract
Statistical and heuristic models have been proposed as applications that are well suited to short-term traffic flow prediction. However, traffic flow data often contain both linear and nonlinear patterns. Therefore, neither statistical nor heuristic models are adequate to model and predict traffic flow data. This paper discusses the relative merits of statistical and heuristic models for traffic flow prediction and summarizes the findings from a comparative study for their performances. Based on that, a hybrid support vector machine for regression (SVR) methodology that combines both statistical and heuristic models is proposed to take advantage of their unique strength in linear and nonlinear modeling. In addition, the dynamics of spatial-temporal patterns in traffic flow are considered in this study, and they are treated as part of the input data. The experiment results based on the real field data of a test region in Beijing suggest that the proposed method is able to provide accurate and reliable flow rate predictions under both low- and high-flow traffic conditions. The benefit from combining statistical and heuristic models as opposed to not combining [autoregressive integrated moving average (ARIMA) model or Elman neural network (NN)] is much more evident in all cases, and the accuracy can be improved by 9.04% on average. Regarding the incorporation of a combination of temporal and spatial characteristics, the use of the hybrid model is found helpful in a one-step-ahead flow rate prediction under high-flow traffic conditions, with a maximum 9.52% improvement on accuracy.
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Acknowledgments
The authors would like to thank the anonymous reviewers for their useful suggestions and comments to improve the paper. This research is supported by funding provided by the Southeastern Transportation Center, a Regional UTC funded by the USDOT Research and Innovative Technology Administration. Additional funding is provided by the National Natural Science Foundation of China (Grant No. 51178032).
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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: Mar 1, 2013
Accepted: Feb 17, 2014
Published online: Apr 1, 2014
Published in print: Jul 1, 2014
Discussion open until: Sep 1, 2014
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