Estimating Fuel Consumption from GPS Data

• Type: Article in International Conference Proceedings Book
• Date: 2015

Authors

Vilaca, A (Author) Other; Ana Aguiar (Author) FEUP; Carlos Soares (Author) FEUP

Conference proceedings International

Title: PATTERN RECOGNITION AND IMAGE ANALYSIS (IBPRIA 2015)

Pages: 672-682

7th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA)

Santiago de Compostela, SPAIN, JUN 17-19, 2015

Indexing

ISI Web of Knowledge - 9 Citations

Scopus - 11 Citations

Scientific classification

CORDIS: Physical sciences > Computer science > Cybernetics > Artificial intelligence

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-00G-69B

DOI: 10.1007/978-3-319-19390-8_75

Abstract (EN): The road transportation sector is responsible for 87% of the human CO2 emissions. The estimation and prediction of fuel consumption plays a key role in the development of systems that foster the reduction of those emissions through trip planing. In this paper, we present a predictive regression model of instantaneous fuel consumption for diesel and gasoline light-duty vehicles, based on their instantaneous speed and acceleration and on road inclination. The parameters are extracted from GPS data, thus the models do not require data from dedicated vehicle sensors. We use data collected by 17 drivers during their daily commutes using the SenseMyCity crowdsensor. We perform an empyrical comparison of several regression algorithms for prediction across trips of the same vehicle and for prediction across vehicles. The results show that models trained for a vehicle show similar RMSE when are applied to other vehicles with similar characteristics. Relying on these results, we propose fuel type specific models that provide an accurate prediction for vehicles with similar characteristics to those on which the models were trained.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11