The goal of the project is to enable the development and evaluation of tools to promote energy efficiency within mobility as a service system currently operational in Chattanooga. For this purpose, we are developing real-time data sets containing information about engine telemetry, including engine speed, GPS position, fuel usage and state of charge (electrical vehicles) from all vehicles in addition to traffic congestion, current events in the city and the braking and acceleration patterns. These high-dimensional dataset allow us to train accurate data-driven predictors using deep neural networks, for energy consumption given various routes and schedules. CARTA is planning to use these predictors for the energy optimization of its fleet of vehicles. We are planning to evaluate our framework by comparing the energy consumption, comfort, etc. of the routes and schedules found using our data-driven framework to existing routes and schedules. We believe that such predictors will revolutionize the transportation sector in a way that is similar to the capabilities provided by high-definition maps used in autonomous driving. This project complements the DOE national labs effort on vehicle energy consumption model by exploiting new data to investigate impacts of road/driver factors on vehicle energy consumption. We collaborate actively with Prof. Aron Lazka, University of Houston and Philip Pugliese, Chattanooga Regional Transit Authority and Prof. Yuche Chen from University of South Carolina in this project.