Title :
Nonparametric regression for the short-term traffic flow forecasting
Author :
Zhang, Tao ; Hu, Lifang ; Liu, Zhixin ; Zhang, Yuejie
Author_Institution :
Sch. of Inf. Manage. & Eng., Shanghai Univ. of Finance & Econ., Shanghai, China
Abstract :
This paper uses the K-NN based nonparametric regression to forecast the short term traffic flow, applies the prediction interval calculated by K to forecast during unconventional road condition, and improves the forecasting results. Finally, nonparametric regression´s advantages of high accuracy and strong transplant ability are showed while being compared with neural network.
Keywords :
forecasting theory; nonparametric statistics; regression analysis; road traffic; transportation; K-NN based nonparametric regression; neural network; prediction interval; road condition; short-term traffic flow forecasting; transplant ability; Databases; Economic forecasting; Finance; Nearest neighbor searches; Predictive models; Runtime; Technology forecasting; Telecommunication traffic; Traffic control; Weather forecasting; K nearest neighbor; nonparametric regression; prediction interval; short-term traffic flow forecasting; state vector;
Conference_Titel :
Mechanic Automation and Control Engineering (MACE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-7737-1
DOI :
10.1109/MACE.2010.5536292
