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Ride sharing has important implications in terms of environmental, social andindividual goals by reducing carbon footprints, fostering social interactionsand economizing commuter costs. The ride sharing systems that are commonlyavailable lack adaptive and scalable techniques that can simultaneously learnfrom the large scale data and predict in real-time dynamic fashion. In thispaper, we study such a problem towards a smart city initiative, where a genericride sharing system is conceived capable of making predictions about ride shareopportunities based on the historically recorded data while satisfyingreal-time ride requests. Underpinning the system is an application of apowerful machine learning convex optimization framework called Network Lassothat uses the Alternate Direction Method of Multipliers (ADMM) optimization forlearning and dynamic prediction. We propose an application of a robust andscalable unified optimization framework within the ride sharing case-study. Theapplication of Network Lasso framework is capable of jointly optimizing andclustering different rides based on their spatial and model similarity. Theprediction from the framework clusters new ride requests, making accurate priceprediction based on the clusters, detecting hidden correlations in the data andallowing fast convergence due to the network topology. We provide an empiricalevaluation of the application of ADMM network Lasso on real trip record andsimulated data, proving their effectiveness since the mean squared error of thealgorithm's prediction is minimized on the test rides.

An Application of Network Lasso Optimization For Ride Sharing Prediction