EAPSI: Providing Smart User Feedback Based on Bayesian Models

Martin William D, Atlanta GA

Investigators

Abstract

Anthropogenic climate change is one of the biggest challenges of the century and part of the solution lies with people adopting more sustainable behavior; this has led to the development of eco-feedback devices for water consumption, power use, and most importantly driving behavior. However, despite the plethora of eco-driving feedback devices, very few of them tailor their feedback based on user behavior and situation. There is a need for proactive situation-aware feedback like "leave 30 minutes early to avoid traffic" because it drastically reduces emissions as compared to a reactive feedback that tells a driver "use your brakes less" every morning when they are stuck in traffic. This research will build the framework for such a situationally aware feedback device by combining on-board diagnostic and global positioning system (OBD/GPS) data, belonging to Dr. Lynette Cheah of the Singapore University of Technology and Design (SUTD), with traffic and weather data to create training data for a Bayesian Network (BN) structure learning algorithm. The resulting BN will act as a behavior and situation specific model capable of determining what variables are the root cause of poor fuel economy. The BN of the system that characterizes driving in Singapore will be learned from a dataset created from the combination of multiple datasets. The amalgamation of multiple datasets and the involvement of human decisions in that data?s generation mean that the training dataset will be noisy and filled with outliers. The goal of this research is to learn the BN that best describes the true nature of the system given this type of training data. To learn the best BN, different BN learning algorithms drawing upon different mathematical principles will be investigated. These different learning algorithm?s results will be compared to a human expert generated BN. The BN learning algorithm that most faithfully recreates the human expert generated network will be identified and the future eco-feedback device will be built off this algorithm. This award, under the East Asia and Pacific Summer Institutes program, supports summer research by a U.S. graduate student is jointly funded by NSF and the National Research Foundation of Singapore.