Use of Local Linear Regression Model for Short-Term Traffic Forecasting

Hongyu Sun · Henry X. Liu · Heng Lin Xiao · Rachel R. He · Bin Ran

247 citations0/0 files storedTransportation Research Record Journal of the Transportation Research Board

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Traffic Prediction and Management Techniques Traffic control and management Forecasting Techniques and Applications

Abstract

The traffic-forecasting model, when considered as a system with inputs of historical and current data and outputs of future data, behaves in a nonlinear fashion and varies with time of day. Traffic data are found to change abruptly during the transition times of entering and leaving peak periods. Accurate and real-time models are needed to approximate the nonlinear time-variant functions between system inputs and outputs from a continuous stream of training data. A proposed local linear regression model was applied to short-term traffic prediction. The performance of the model was compared with previous results of nonparametric approaches that are based on local constant regression, such as the k-nearest neighbor and kernel methods, by using 32-day traffic-speed data collected on US-290, in Houston, Texas, at 5-min intervals. It was found that the local linear methods consistently showed better performance than the k-nearest neighbor and kernel smoothing methods.

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References

1984Dynamic prediction of traffic volume through Kalman filtering theory 1975Transportation research 1991Nonparametric Regression and Short‐Term Freeway Traffic Forecasting 2001Dynamic Freeway Travel-Time Prediction with Probe Vehicle Data: Link Based Versus Path Based

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