Title :
Traffic volume prediction based on improved Grey self-adaptable prediction formula
Author :
Xu, Na ; Zhang, Xin-rui
Author_Institution :
Coll. of Econ. & Manage., Hebei Univ. of Sci. & Technol., Shijiazhuang, China
Abstract :
Traffic volume prediction is the key approach of highway planning stage. Grey forecast model can be used to predict the traffic volume under the condition of lacking traffic volume datum. This paper firstly points out the defect of Grey prediction formula in traditional GM(1,1) model through analyzing the Grey forecast theory, then modifies and expands the old formula, propose the new prediction formula, which gives a new way to improve the prediction accuracy. Next, constitutes the Grey self-adaptable model based on the new prediction formula for prolonging the prediction period. Finally, employs the model in traffic volume prediction for a certain station in Hebei Province. The result obtains that the simulation effect and the prediction accuracy using the new formula is higher than the result of the traditional GM(1,1) model. The case study indicates that the prediction method is not only reasonable in theory but also owns good application value in traffic volume prediction.
Keywords :
forecasting theory; grey systems; prediction theory; road traffic; transportation; Grey forecast model; Grey self adaptable prediction formula; Hebei province; highway planning; traffic volume prediction; Accuracy; Adaptation model; Biological system modeling; Data models; Mathematical model; Predictive models; Solid modeling; GM(1, 1); Grey prediction; MGM(1, 1); Traffic volume;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-6526-2
DOI :
10.1109/ICMLC.2010.5580624