  Traffic flows are studied in terms of their noise of sound, which is an
easily accessible experimental quantity. The sound noise data is studied making
use of scaling properties of wavelet transforms and Hurst exponents are
extracted. The scaling behavior is used to characterize the traffic flows in
terms of scaling properties of the memory function in Mori-Lee stochastic
differential equations. The results obtained provides for a new theoretical as
well as experimental framework to characterize the large-time behavior of
traffic flows. The present paper outlines the procedure by making use of
one-lane computer simulations as well as sound-data measurements from a real
two-lane traffic flow. We find the presence of conventional diffusion as well
as 1/f-noise in real traffic flows at large time scales.
