Green Navigation: A Big Data Approach Towards Sustainable Transportation

Gas Pump

The Transportation sector currently accounts for one of the largest shares of energy consumption in the nation, consuming 28% of the total energy. It is responsible for the majority of fossil energy consumption, making it a primary source of Green House Gas emissions.

According to the US Energy Information Administration, in their May 2016 monthly energy review the transportation sector currently accounts for one of the largest shares of energy consumption in the nation, among all sectors. Consuming 28% of the total energy, it is responsible for roughly the same amount of energy as that consumed by the entire industrial sector, and significantly outpaces that consumed by the residential and commercial sectors. According to the US Environmental Protection Agency (EPA), the transportation sector is responsible for roughly 124% of the total emissions of the industrial sector, and 217% of emissions of the commercial and residential sectors combined. More than half of the sector’s emissions (as well as 59% of the sector’s energy consumption are attributed to passenger cars and light trucks. They are therefore an obvious target when it comes to attempts for improving the overall energy and environmental sustainability of modern society. A research investment is needed to improve the general understanding of driver-vehicle-infrastructure interactions and offer computationally-enabled solutions for drivers to reduce their energy cost, emissions, delay and carbon footprint. In particular, solutions are needed that offer individualized advice to drivers.

This project aims to develop a novel green navigation system, called Green Nav, that gives a driver the most fuel efficient route for his vehicle as opposed to the shortest or fastest route. Green Nav relies on the crowdsourced data collected by individuals from their vehicles and a generalization framework that predicts the fuel consumption of an arbitrary vehicle on an arbitrary road segment. Although there are some existing work on achieving sustainability-related objectives in transportation, none of them has explored an integrated design that addresses not only the challenges in fine-grained traffic data inference, general-type vehicle energy consumption modeling, and large-scale vehicle routing and speed control, but also the human factor issues in real deployment. The goal of this project is to address these open challenges and, for the first time, offers a holistic solution for green transportation.

Although this project focuses on Green Navigation in Transportation as an example, it will expedite and accelerate the adoption of big data techniques in a wide range of real-world applications.