research-article

EcoMark: evaluating models of vehicular environmental impact

Authors:

Christian S. Jensen,

Manohar KaulAuthors Info & Claims

SIGSPATIAL '12: Proceedings of the 20th International Conference on Advances in Geographic Information Systems

Pages 269 - 278

https://doi.org/10.1145/2424321.2424356

Published: 06 November 2012 Publication History

Abstract

The reduction of greenhouse gas (GHG) emissions from transportation is essential for achieving politically agreed upon emissions reduction targets that aim to combat global climate change. So-called eco-routing and eco-driving are able to substantially reduce GHG emissions caused by vehicular transportation. To enable these, it is necessary to be able to reliably quantify the emissions of vehicles as they travel in a spatial network. Thus, a number of models have been proposed that aim to quantify the emissions of a vehicle based on GPS data from the vehicle and a 3D model of the spatial network the vehicle travels in. We develop an evaluation framework, called EcoMark, for such environmental impact models. In addition, we survey all eleven state-of-the-art impact models known to us. To gain insight into the capabilities of the models and to understand the effectiveness of the EcoMark, we apply the framework to all models.

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Index Terms

EcoMark: evaluating models of vehicular environmental impact
1. Information systems
  1. Information systems applications

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Published In

cover image ACM Conferences

SIGSPATIAL '12: Proceedings of the 20th International Conference on Advances in Geographic Information Systems

November 2012

642 pages

ISBN:9781450316910

DOI:10.1145/2424321

General Chairs:
Isabel Cruz
University of Illinois at Chicago
,
Craig Knoblock
University of Southern California
,
Program Chairs:
Peer Kröger
Ludwig-Maximilians-Universität München, Germany
,
Egemen Tanin
University of Melbourne, Australia
,
Peter Widmayer
ETH Zürich, Switzerland

Copyright © 2012 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: 06 November 2012

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SIGSPATIAL'12

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SIGSPATIAL

SIGSPATIAL'12: SIGSPATIAL 2012 International Conference on Advances in Geographic Information Systems

November 6 - 9, 2012

Redondo Beach, California

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

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https://doi.org/10.3390/en17020500
Jensen CYang BGuo CHu JTorp K(2024)Routing with Massive Trajectory Data2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00442(5542-5547)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00442
Maalouf ATukan MBraverman VRus DKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)AutoCoresetProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619386(23451-23466)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619386
Chen YYang XYang LFeng J(2022)An Automatic Approach to Extracting Large-Scale Three-Dimensional Road Networks Using Open-Source DataRemote Sensing10.3390/rs1422574614:22(5746)Online publication date: 14-Nov-2022
https://doi.org/10.3390/rs14225746
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Li NGao YLi WJiang YXia S(2021)H-GPR: A Hybrid Strategy for Large-Scale Gaussian Process RegressionICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP39728.2021.9414474(2955-2959)Online publication date: 6-Jun-2021
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