Skip to main content
eScholarship
Open Access Publications from the University of California

Short Term Traffic Forecasting Using the Local Linear Regression Model

No data is associated with this publication.
Abstract

Traffic data is highly nonlinear and also varies with time of day. It changes abruptly when entering or leaving a congestion hour. Therefore,the prediction of travel time requires accurate models. This leads to the problem of approximating nonlinear and time-variant functions. In this paper, we propose and apply local linear regression models to short-term traffic prediction. Local linear regression is one type of local weighted regression methods. It has been applied to many problems, including artificial intelligence, dynamic system identification and data mining. It can be used for nonlinear time series prediction under certain mixing conditions. The performance of the proposed model is compared with previous nonparametric approaches, such as nearest neighborhood and kernel methods using 32-day traffic speed data collected on the Houston, Texas, US-290 Northwest Freeway. We found that the local linear methods consistently outperform the nearest neighborhood and kernel smoothing methods.



The text for this item is currently unavailable.