Real-Time Short-Term Traffic Speed Level Forecasting and Uncertainty Quantification Using Layered Kalman Filters

Short-term traffic condition forecasting has long been argued as essential for developing proactive traffic control systems that could alleviate the growing congestion in the United States. In this field, short-term traffic condition level forecasting and short-term traffic condition uncertainty forecasting play an equally important role. Past literature showed that linear stochastic time series models are promising in modeling and hence forecasting traffic condition levels and traffic conditional variance with workable performance. On the basis of this finding, an autoregressive moving average plus generalized autoregressive conditional heteroscedasticity structure was proposed for modeling the station-by-station traffic speed series. An online algorithm based on layered Kalman filter was developed for processing this structure in real time. Empirical results based on real-world station-by-station traffic speed data showed that the proposed online algorithm can generate workable short-term traffic speed level forecasts and associated prediction confidence intervals. Future work is recommended to develop and test a proactive traffic control system in a simulated environment, to refine the uncertainty modeling through a stochastic volatility model, and to extend uncertainty modeling and forecasting to link level and network level.