Mining geographic data for fuel consumption estimation

Mobility is one of the greatest contributors to the personal carbon footprint and to pollution and noise in urban areas. Still, these factors are not yet easily quantifiable in personal or urban scale, e.g. impact of each car trip or areas most exposed to CO2 emissions. In this article, we propose an innovative solution for estimating fuel consumption and emissions leveraging the opportunities generated by the ubiquitous availability of mobile devices. We collect a large data set of GPS and fuel consumption data crowd-sourced by volunteer participants with an Android mobile application that logs the smartphone's embedded GPS data and gathers vehicle data using an external On-Board Diagnostics (OBD) device. This data is used to develop a model that estimates the instantaneous fuel consumption from the smartphone's GPS data alone, using the OBD data as ground truth. We use speed, acceleration and steepness as predictor variables to train polynomial models with and without cross-product terms. With the best general model (trained and tested on all participant vehicles), we obtain an average residual standard deviation of 1.58 l/100km for average consumption on 1min intervals. For individual models (trained and tested on each participant vehicle), we obtain an average residual standard deviation of 1.43 l/100km. The average fuel consumption for the used data set was 6.7 l/100km.