Congestion Prediction based on NexRad Radar with Application to In-vehicle Information

Abstract

This paper develops a quantitative relationship between Nexrad radar reflectivity and surface traffic conditions. Data from two data mines on the University of Washington campus are combined to evaluate the quantitative relationship between freeway speed reduction and rain fall rate as measured by Doppler radar. Radar data are converted into rainfall rates and speed data from the inductance loop speed traps are converted into a deviations from a normal performance measure. The deviation from normal and the rainfall rate are used to construct an impulse response function that can be applied to radar measurements to predict traffic speed reduction. These data can then be made available in-vehicle as a new form of real-time traveler information.