Urban Freeway Traffic Flow Prediction: Application of Seasonal Autoregressive Integrated Moving Average and Exponential Smoothing Models

TLDR

Seasonal time series methods have not previously been applied to traffic flow forecasting, despite traffic data exhibiting clear periodic cycles. The study applies seasonal time series models to single‑interval urban freeway traffic flow forecasting. The authors developed and tested seasonal ARIMA and Winters exponential smoothing models on 15‑minute flow rates from Telegraph Road and the Woodrow Wilson Bridge, using the same data as Smith, and identified best‑fit models for each site. Comparisons with Smith’s results show that ARIMA (2,0,1)(0,1,1)96 and ARIMA (1,0,1)(0,1,1)96 best fit the two sites, and single‑step forecasts from seasonal ARIMA outperform nearest‑neighbor, neural network, and historical average models.

Abstract

The application of seasonal time series models to the single-interval traffic flow forecasting problem for urban freeways is addressed. Seasonal time series approaches have not been used in previous forecasting research. However, time series of traffic flow data are characterized by definite periodic cycles. Seasonal autoregressive integrated moving average (ARIMA) and Winters exponential smoothing models were developed and tested on data sets belonging to two sites: Telegraph Road and the Woodrow Wilson Bridge on the inner and outer loops of the Capital Beltway in northern Virginia. Data were 15-min flow rates and were the same as used in prior forecasting research by B. Smith. Direct comparisons with the Smith report findings were made and it was found that ARIMA (2, 0, 1)(0, 1, 1) 96 and ARIMA (1, 0, 1)(0, 1, 1) 96 were the best-fit models for the Telegraph Road and Wilson Bridge sites, respectively. Best-fit Winters exponential smoothing models were also developed for each site. The single-step forecasting results indicate that seasonal ARIMA models outperform the nearest-neighbor, neural network, and historical average models as reported by Smith.