Title :
Congestion Prediction based on NexRad Radar with Application to In-vehicle Information
Author_Institution :
Washington Univ., Seattle
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.
Keywords :
Doppler radar; data mining; meteorological radar; traffic engineering computing; transient response; Doppler radar; Nexrad radar measurement; data mining; impulse response function; in-vehicle information; rain fall rate; real-time traveler information; surface traffic condition; traffic speed reduction prediction; Data mining; Doppler radar; Intelligent vehicles; Radar applications; Radar measurements; Reflection; Traffic control; Transportation; Velocity measurement; Weather forecasting;
Conference_Titel :
Intelligent Vehicles Symposium, 2007 IEEE
Conference_Location :
Istanbul
Print_ISBN :
1-4244-1067-3
Electronic_ISBN :
1931-0587
DOI :
10.1109/IVS.2007.4290297