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Complexity
Volume 2017, Article ID 3515272, 16 pages
https://doi.org/10.1155/2017/3515272
Research Article

Forecasting the Short-Term Traffic Flow in the Intelligent Transportation System Based on an Inertia Nonhomogenous Discrete Gray Model

1College of Science, Wuhan University of Technology, Wuhan 430070, China
2College of Science, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
3School of Business Administration, Zhejiang University of Finance & Economics, Hangzhou 310018, China

Correspondence should be addressed to Xinping Xiao; nc.ude.tuhw@pxoaix

Received 15 April 2017; Accepted 28 May 2017; Published 3 July 2017

Academic Editor: Bo Zeng

Copyright © 2017 Huiming Duan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The traffic-flow system has basic dynamic characteristics. This feature provides a theoretical basis for constructing a reasonable and effective model for the traffic-flow system. The research on short-term traffic-flow forecasting is of wide interest. Its results can be applied directly to advanced traffic information systems and traffic management, providing real-time and effective traffic information. According to the dynamic characteristics of traffic-flow data, this paper extends the mechanical properties, such as distance, acceleration, force combination, and decomposition, to the traffic-flow data vector. According to the mechanical properties of the data, this paper proposes four new models of structural parameters and component parameters, inertia nonhomogenous discrete gray models (referred to as INDGM), and analyzes the important properties of the model. This model examines the construction of the inertia nonhomogenous discrete gray model from the mechanical properties of the data, explaining the classic NDGM modeling mechanism in the meantime. Finally, this paper analyzes the traffic-flow data of Whitemud Drive in Canada and studies the relationship between the inertia model and the traffic-flow state according to the data analysis of the traffic-flow state. A simulation accuracy and prediction accuracy of up to 0.0248 and 0.0273, respectively, are obtained.