Estimation of Roadway Traffic Density on Freeways Using Presence Detector Data

Existing methods of estimating section (link) density on freeways from data provided by electronic presence (loop) detectors typically require extensive knowledge or strong assumptions on prevailing flow conditions, such as homogeneity. Consequently, these methods are known to produce poor estimates in inhomogeneous conditions, or when a priori knowledge of traffic conditions is not available. In this paper, a new data processing approach is presented which estimates density well over a wide range of traffic conditions. It does this by detecting spatially inhomogeneous traffic conditions and compensating the density estimation algorithm appropriately. The data processing algorithm is computationally simple, is not flow-level dependent, does not require any a priori knowledge of traffic conditions on the road and is insensitive to the types of uncertainty found in detector data. The algorithm uses both flow and occupancy data from adjacent detector stations to track the density on a link. A scalar Kalman filter formulation is used to provide the desired density estimate. The simplicity of the filter algorithm is achieved by using a scalar Generalized Likelihood Ratio (GLR) event detection algorithm to compensate the filter for spatially inhomogeneous conditions. Performance of the algorithm is demonstrated with a microscopic freeway simulation.