In this work, we leverage the Uber movement dataset for the Los Angeles (LA) area where partial TAZ to TAZ (Traffic Analysis Zone) trip time data is available, to predict travel time patterns on the full TAZ to TAZ network. We first create a TAZ-TAZ network based on nearest neighbors and propose a model that allows us to complete the (O −D) (Origin-Destination) travel time matrix, using optimization methods such as non-negative least squares.We apply these algorithms to several communities in the TAZ-TAZ network and present insights in the form of completed (O −D) matrices and associated temporal trends. We qualify the error performance and scalability of our flows. We conclude by pointing out the directions in our ongoing work to improve the quality and scale of travel time estimation.