Comparison of parametric and nonparametric models for traffic flow forecasting

摘要

Single point short-term traffic flow forecasting will play a key role in supporting demand forecasts needed by operational network models. Seasonal autoregressive integrated moving average(ARIMA), a classic parametric modeling approach to time series, and nonparametric regression models have been proposed as well suited for application to single point short-term traffic flow forecasting. Past research has shown sea- sonal ARIMA models to deliver results that are statistically superior to basic implementations of non- parametric regression.