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 idling status, engine temperature, engine speed, throttle, vehicle speed, GPS position, fuel usage and state of charge (electrical vehicles) from all vehicles in the fleet of the transit agency such as CARTA. We combine these datasets with information road gradient, 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 evaluating 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.
Complete project dashboards available at http://www.smarttransit.ai.
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