CO2NNIE: Personalized Fuel Consumption and CO2 Emissions

Benjamin Bjerre Krogh, Ove Andersen, Edwin Lewis-Kelham, Kristian Torp

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Abstract

We propose a system for calculating the personalized annual fuel consumption and CO2 emissions from transportation. The system, named CO2NNIE, estimates the fuel consumption on the fastest route between the frequent destinations of the user. The travel time and fuel consumption estimated are based on 3.8 billion GPS records from 16 thousand cars and 198 million records from 218 cars annotated with fuel consumption data, respectively. The fuel consumption estimates from the system are validated using fuel-pump data. We find that estimates have good accuracy, i.e., are generally within 10% of the actual fuel consumption (4.6% deviation on average). We conclude, that the system provides new detailed information on CO2 emissions and fuel consumption for any make and model.
Original languageEnglish
Title of host publication23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
PublisherAssociation for Computing Machinery
Publication date2015
Article number92
ISBN (Print)978-1-4503-3967-4
DOIs
Publication statusPublished - 2015
EventSIGSPATIAL '15: International Conference on Advances in Geographic Information Systems - Bellevue, WA, United States
Duration: 3 Nov 20156 Nov 2015

Conference

ConferenceSIGSPATIAL '15
CountryUnited States
City Bellevue, WA
Period03/11/201506/11/2015

Keywords

  • Fuel-consumption
  • Transportation,
  • GPS
  • CANBus
  • CO2

Fingerprint Dive into the research topics of 'CO2NNIE: Personalized Fuel Consumption and CO2 Emissions'. Together they form a unique fingerprint.

  • Cite this

    Krogh, B. B., Andersen, O., Lewis-Kelham, E., & Torp, K. (2015). CO2NNIE: Personalized Fuel Consumption and CO2 Emissions. In 23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems [92] Association for Computing Machinery. https://doi.org/10.1145/2820783.2820790