article

EcoMark 2.0: empowering eco-routing with vehicular environmental models and actual vehicle fuel consumption data

Authors:

Christian S. Jensen,

Kristian TorpAuthors Info & Claims

Geoinformatica, Volume 19, Issue 3

Pages 567 - 599

https://doi.org/10.1007/s10707-014-0221-7

Published: 01 July 2015 Publication History

Abstract

Eco-routing is a simple yet effective approach to substantially reducing the environmental impact, e.g., fuel consumption and greenhouse gas (GHG) emissions, of vehicular transportation. Eco-routing relies on the ability to reliably quantify the environmental impact of vehicles as they travel in a spatial network. The procedure of quantifying such vehicular impact for road segments of a spatial network is called eco-weight assignment. EcoMark 2.0 proposes a general framework for eco-weight assignment to enable eco-routing. It studies the abilities of six instantaneous and five aggregated models to estimating vehicular environmental impact. In doing so, it utilizes travel information derived from GPS trajectories (i.e., velocities and accelerations) and actual fuel consumption data obtained from vehicles. The framework covers analyses of actual fuel consumption, impact model calibration, and experiments for assessing the utility of the impact models in assigning eco-weights. The application of EcoMark 2.0 indicates that the instantaneous model EMIT and the aggregated model SIDRA-Running are suitable for assigning eco-weights under varying circumstances. In contrast, other instantaneous models should not be used for assigning eco-weights, and other aggregated models can be used for assigning eco-weights under certain circumstances.

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Cited By

Guo CXu RYang BYuan YKieu TZhao YJensen C(2024)Efficient Stochastic Routing in Path-Centric Uncertain Road NetworksProceedings of the VLDB Endowment10.14778/3681954.368197117:11(2893-2905)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3681971
Qiu XHu JZhou LWu XDu JZhang BGuo CZhou AJensen CSheng ZYang B(2024)TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting MethodsProceedings of the VLDB Endowment10.14778/3665844.366586317:9(2363-2377)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.14778/3665844.3665863
Mokbel MSakr MXiong LZüfle AAlmeida JAnderson TAref WAndrienko GAndrienko NCao YChawla SCheng RChrysanthis PFei XGhinita GGraser AGunopulos DJensen CKim JKim KKröger PKrumm JLauer JMagdy ANascimento MRavada SRenz MSacharidis DSalim FSarwat MSchoemans MShahabi CSpeckmann BTanin ETeng XTheodoridis YTorp KTrajcevski Gvan Kreveld MWenk CWerner MWong RWu SXu JYoussef MZeinalipour DZhang MZimányi E(2024)Mobility Data Science: Perspectives and ChallengesACM Transactions on Spatial Algorithms and Systems10.1145/365215810:2(1-35)Online publication date: 1-Jul-2024
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cover image Geoinformatica

Geoinformatica Volume 19, Issue 3

July 2015

232 pages

ISSN:1384-6175

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Copyright © Copyright © 2015 Springer Science+Business Media New York.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 July 2015

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Cited By

Guo CXu RYang BYuan YKieu TZhao YJensen C(2024)Efficient Stochastic Routing in Path-Centric Uncertain Road NetworksProceedings of the VLDB Endowment10.14778/3681954.368197117:11(2893-2905)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3681971
Qiu XHu JZhou LWu XDu JZhang BGuo CZhou AJensen CSheng ZYang B(2024)TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting MethodsProceedings of the VLDB Endowment10.14778/3665844.366586317:9(2363-2377)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.14778/3665844.3665863
Mokbel MSakr MXiong LZüfle AAlmeida JAnderson TAref WAndrienko GAndrienko NCao YChawla SCheng RChrysanthis PFei XGhinita GGraser AGunopulos DJensen CKim JKim KKröger PKrumm JLauer JMagdy ANascimento MRavada SRenz MSacharidis DSalim FSarwat MSchoemans MShahabi CSpeckmann BTanin ETeng XTheodoridis YTorp KTrajcevski Gvan Kreveld MWenk CWerner MWong RWu SXu JYoussef MZeinalipour DZhang MZimányi E(2024)Mobility Data Science: Perspectives and ChallengesACM Transactions on Spatial Algorithms and Systems10.1145/365215810:2(1-35)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3652158
Wu STorp KSakr MZimányi E(2023)Evaluation of Vessel CO2 Emissions Methods using AIS TrajectoriesProceedings of the 18th International Symposium on Spatial and Temporal Data10.1145/3609956.3609960(65-74)Online publication date: 23-Aug-2023
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Le Rhun ABonnans FDe Nunzio GLeroy TMartinon P(2020)A Stochastic Data-Based Traffic Model Applied to Vehicles Energy Consumption EstimationIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2019.292329221:7(3025-3034)Online publication date: 26-Jun-2020
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