Estimating dynamic roadway travel times using automatic vehicle identification data for low sampling rates

The paper describes a low-pass adaptive filtering algorithm for predicting average roadway travel times using automatic vehicle identification (AVI) data. The algorithm is unique in three aspects. First, it is designed to handle both stable (constant mean) and unstable (varying mean) traffic conditions. Second, the algorithm can be successfully applied for low levels of market penetration (less than 1%). Third, the algorithm works for both freeway and signalized arterial roadways. The proposed algorithm utilizes a robust data-filtering procedure that identifies valid data within a dynamically varying validity window. The size of the validity window varies as a function of the number of observations within the current sampling interval, the number of observations in the previous intervals, and the number of consecutive observations outside the validity window. Applications of the algorithm to two AVI datasets from San Antonio, one from a freeway link and the other from an arterial link, demonstrate the ability of the proposed algorithm to efficiently track typical variations in average link travel times while suppressing high frequency noise signals.