Open AccessLong-term forecasting oriented to urban expressway traffic situation
Author(s) -
Fei Su,
Hui Dong,
Limin Jia,
Yong Qin,
Tian Zhao
Publication year - 2016
Publication title -
advances in mechanical engineering/advances in mechanical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.318
H-Index - 40
eISSN - 1687-8140
pISSN - 1687-8132
DOI - 10.1177/1687814016628397
Subject(s) - traffic flow (computer networking) , functional principal component analysis , nonparametric statistics , beijing , computer science , traffic congestion , term (time) , autocorrelation , nonparametric regression , regression analysis , principal component analysis , transport engineering , econometrics , engineering , statistics , mathematics , artificial intelligence , machine learning , geography , physics , computer security , archaeology , quantum mechanics , china
Long-term traffic forecasting has become a basic and critical work in the research on road traffic congestion. It plays an important role in alleviating road traffic congestion and improving traffic management quality. According to the problem that long-term traffic forecasting is short of systematic and effective methods, a long-term traffic situation forecasting model is proposed in this article based on functional nonparametric regression. In the functional nonparametric regression framework, autocorrelation analysis (ACF) is introduced to analyze the autocorrelation coefficient of traffic flow for selecting the state vector, and the functional principal component analysis is also used as distance function for computing proximities between different traffic flow time series. The experiments based on the traffic flow data in Beijing expressway prove that the functional nonparametric regression model outperforms forecast methods in accuracy and effectiveness