  Ride sharing has important implications in terms of environmental, social and
individual goals by reducing carbon footprints, fostering social interactions
and economizing commuter costs. The ride sharing systems that are commonly
available lack adaptive and scalable techniques that can simultaneously learn
from the large scale data and predict in real-time dynamic fashion. In this
paper, we study such a problem towards a smart city initiative, where a generic
ride sharing system is conceived capable of making predictions about ride share
opportunities based on the historically recorded data while satisfying
real-time ride requests. Underpinning the system is an application of a
powerful machine learning convex optimization framework called Network Lasso
that uses the Alternate Direction Method of Multipliers (ADMM) optimization for
learning and dynamic prediction. We propose an application of a robust and
scalable unified optimization framework within the ride sharing case-study. The
application of Network Lasso framework is capable of jointly optimizing and
clustering different rides based on their spatial and model similarity. The
prediction from the framework clusters new ride requests, making accurate price
prediction based on the clusters, detecting hidden correlations in the data and
allowing fast convergence due to the network topology. We provide an empirical
evaluation of the application of ADMM network Lasso on real trip record and
simulated data, proving their effectiveness since the mean squared error of the
algorithm's prediction is minimized on the test rides.
