Vehicular Traffic Density State Estimation Based on Cumulative Road Acoustics

In this work, we propose to use a statistical framework
that uses the cumulative acoustic signal from a roadside
installed single microphone, to classify the vehicular traffic
density state. A typical cumulative acoustic signal on a road
segment is composed of several noise signals such as the tire
noise, engine noise, engine idling noise, occasional honks and the
air turbulence noise of the multiple vehicles. The occurrence and
mixture weightings of these multiple vehicles’ noise signals are
determined by the prevalent traffic density conditions on the road
segment. For example, in a free flowing traffic, the vehicles would
typically be moving with the medium to high speeds thereby
producing mainly the tire noise and the air-turbulence noise and
less of the engine idling noise and the honks. While for a slow
moving congested traffic, the cumulative signal will be largely
dominated by the engine idling noise and the honks; the air
turbulence and the tire noises will be inconspicuous. Further,
these various noise signals have spectral content that are very
different from each other and hence can be used to discriminate
between the different traffic density states that lead to them. In
this work, we extract the short term spectral envelope features of
the cumulative acoustic signals, and model their class-conditional
probability distributions, conditioned on one of the three broad
traffic density states namely Jammed(0-10KpH), Medium-Flow(10-
40Kph) and Free-Flow(40Kph and above) traffic. While, these
states are coarse measures of the average traffic speed, they nevertheless,
can provide useful traffic density information in the often
chaotic and non-lane driven traffic conditions of the developing
geographies where the other techniques (magnetic loop detectors)
are inapplicable. Based on these learned distributions, we use a
Bayes classifier to classify the acoustic signal segments spanning
a duration of 5s to 30s, which resulted in a high classification
accuracy of ( 95%). Using a discriminative classifier such as
Support Vector Machine (SVM), resulted in further classification
accuracy gains over the Bayes classifier.