research-article

Evaluating eco-driving advice using GPS/CANBus data

Authors:

Karsten Jakobsen,

Sabrine C. H. Mouritsen,

Kristian TorpAuthors Info & Claims

SIGSPATIAL'13: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 44 - 53

https://doi.org/10.1145/2525314.2525358

Published: 05 November 2013 Publication History

Abstract

Vehicles in the US use approximately 1.4 billion liters of fuel a day. This number can be reduced by driving more fuel efficiently. Several sources provide fuel saving eco-driving advice, but the advices are often quite abstract. This paper uses a large set of high-frequent GPS and Controller Area Network Bus (CANBus) data from four similar vehicles to evaluate the eco-driving advice. The CANBus data provides information such as the fuel consumption per second and rounds per minute of the engine.

The vehicles are compared and evaluated on the drivers' ability to follow the eco-driving advice. This comparison shows that there is a 23% difference in the fuel efficiency. This difference is due to quite different speed and acceleration profiles of the drivers but also that stopping at a traffic light consumes double the fuel compared to not stopping.

Overall it is shown that there is good potential for saving fuel by following the eco-driving advice. However, no single advice dominates, and multiple advices are found to have a positive effect on the fuel efficiency.

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Cited By

Ma ZJørgensen BMa Z(2024)A Scoping Review of Energy-Efficient Driving Behaviors and Applied State-of-the-Art AI MethodsEnergies10.3390/en1702050017:2(500)Online publication date: 19-Jan-2024
https://doi.org/10.3390/en17020500
Chu HZhuang HWang WNa XGuo LZhang JGao BChen H(2023)A Review of Driving Style Recognition Methods From Short-Term and Long-Term PerspectivesIEEE Transactions on Intelligent Vehicles10.1109/TIV.2023.32794258:11(4599-4612)Online publication date: Nov-2023
https://doi.org/10.1109/TIV.2023.3279425
Ma ZJørgensen BMa Z(2023)Eco-driving Intelligent Systems and Algorithms: A Patent Review2023 8th International Conference on Power and Renewable Energy (ICPRE)10.1109/ICPRE59655.2023.10353603(285-290)Online publication date: 22-Sep-2023
https://doi.org/10.1109/ICPRE59655.2023.10353603
Show More Cited By

Index Terms

Evaluating eco-driving advice using GPS/CANBus data
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Geographic visualization
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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Published In

cover image ACM Conferences

SIGSPATIAL'13: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2013

598 pages

ISBN:9781450325219

DOI:10.1145/2525314

General Chairs:
Craig Knoblock
University of Southern California
,
Markus Schneider
University of Florida
,
Program Chairs:
Peer Kröger
Ludwig-Maximilians-Universität München, Germany
,
John Krumm
Microsoft Research
,
Peter Widmayer
ETH Zürich, Switzerland

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2013

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REDUCTION project

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SIGSPATIAL'13

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SIGSPATIAL

SIGSPATIAL'13: 21st SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 5 - 8, 2013

Florida, Orlando

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Overall Acceptance Rate 220 of 1,116 submissions, 20%

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Cited By

Ma ZJørgensen BMa Z(2024)A Scoping Review of Energy-Efficient Driving Behaviors and Applied State-of-the-Art AI MethodsEnergies10.3390/en1702050017:2(500)Online publication date: 19-Jan-2024
https://doi.org/10.3390/en17020500
Chu HZhuang HWang WNa XGuo LZhang JGao BChen H(2023)A Review of Driving Style Recognition Methods From Short-Term and Long-Term PerspectivesIEEE Transactions on Intelligent Vehicles10.1109/TIV.2023.32794258:11(4599-4612)Online publication date: Nov-2023
https://doi.org/10.1109/TIV.2023.3279425
Ma ZJørgensen BMa Z(2023)Eco-driving Intelligent Systems and Algorithms: A Patent Review2023 8th International Conference on Power and Renewable Energy (ICPRE)10.1109/ICPRE59655.2023.10353603(285-290)Online publication date: 22-Sep-2023
https://doi.org/10.1109/ICPRE59655.2023.10353603
Hozhabr Pour HLi FWegmeth LTrense CDoniec RGrzegorzek MWismüller R(2022)A Machine Learning Framework for Automated Accident Detection Based on Multimodal Sensors in CarsSensors10.3390/s2210363422:10(3634)Online publication date: 10-May-2022
https://doi.org/10.3390/s22103634
David RZimányi ETorp KSakr M(2022)Speed and energy consumption for electrical vehiclesProceedings of the 15th ACM SIGSPATIAL International Workshop on Computational Transportation Science10.1145/3557991.3567802(1-10)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1145/3557991.3567802
Yu PYamauchi H(2022)A Machine Learning Based Fuel Consumption Saving Method with Time and Environment Dependency Aware ManagementProceedings of the 2022 5th International Conference on Electronics, Communications and Control Engineering10.1145/3531028.3531035(40-49)Online publication date: 25-Mar-2022
https://dl.acm.org/doi/10.1145/3531028.3531035
Vogt MKrisp J(2022)Räumliche Lärmanalyse anhand von erweiterten Floating-Car-Daten (xFCD)Spatial Analysis of Noise Using Extended Floating Car Data (xFCD)KN - Journal of Cartography and Geographic Information10.1007/s42489-021-00095-y72:1(73-83)Online publication date: 11-Feb-2022
https://doi.org/10.1007/s42489-021-00095-y
Xu JTu RAhmed UAmirjamshidi GHatzopoulou MRoorda M(2021)An eco-score system incorporating driving behavior, vehicle characteristics, and traffic conditionsTransportation Research Part D: Transport and Environment10.1016/j.trd.2021.10286695(102866)Online publication date: Jun-2021
https://doi.org/10.1016/j.trd.2021.102866
Evin EAydin MKardas G(2020)Design and Implementation of a CANBus-Based Eco-Driving System for Public Transport Bus ServicesIEEE Access10.1109/ACCESS.2020.29641198(8114-8128)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2964119
Türker GGündüz F(2020)Identifying Driver Behaviour Through Onboard Diagnostic Using CAN Bus SignalsArtificial Intelligence and Applied Mathematics in Engineering Problems10.1007/978-3-030-36178-5_21(266-275)Online publication date: 3-Jan-2020
https://doi.org/10.1007/978-3-030-36178-5_21
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