  Accurate prediction of traffic signal duration for roadway junction is a
challenging problem due to the dynamic nature of traffic flows. Though
supervised learning can be used, parameters may vary across roadway junctions.
In this paper, we present a computer vision guided expert system that can learn
the departure rate of a given traffic junction modeled using traditional
queuing theory. First, we temporally group the optical flow of the moving
vehicles using Dirichlet Process Mixture Model (DPMM). These groups are
referred to as tracklets or temporal clusters. Tracklet features are then used
to learn the dynamic behavior of a traffic junction, especially during on/off
cycles of a signal. The proposed queuing theory based approach can predict the
signal open duration for the next cycle with higher accuracy when compared with
other popular features used for tracking. The hypothesis has been verified on
two publicly available video datasets. The results reveal that the DPMM based
features are better than existing tracking frameworks to estimate $\mu$. Thus,
signal duration prediction is more accurate when tested on these datasets.The
method can be used for designing intelligent operator-independent traffic
control systems for roadway junctions at cities and highways.
