research-article

Spatial big data for eco-routing services: computational challenges and accomplishments

Authors:

Venkata M. V. Gunturi,

Shashi ShekharAuthors Info & Claims

SIGSPATIAL Special, Volume 6, Issue 2

Pages 19 - 25

https://doi.org/10.1145/2744700.2744703

Published: 10 March 2015 Publication History

Abstract

The size, variety, and update rate of spatial datasets are increasingly exceeding the capacity of commonly used spatial computing technologies to learn, manage, and process the data with reasonable effort. We refer to these datasets as Spatial Big Data (SBD). Examples of emerging SBD datasets include temporally detailed (TD) roadmaps that provide speeds every minute for every road-segment, GPS track data from cell-phones, and engine measurements of fuel consumption, greenhouse gas (GHG) emissions, etc. Harnessing SBD has a transformative potential. For example, a 2011 McKinsey Global Institute report estimates savings of "about $600 billion annually by 2020" in terms of fuel and time saved by helping vehicles avoid congestion and reduce idling at red lights or left turns. In this paper, we discuss the challenges posed by SBD for a next generation of routing services and we present our work towards addressing these challenges.

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Cited By

Musleh MAbbar SStanojevic RMokbel M(2021)QARTAProceedings of the VLDB Endowment10.14778/3476249.347627914:11(2273-2282)Online publication date: 27-Oct-2021
https://dl.acm.org/doi/10.14778/3476249.3476279
Walsh JCunningham ASmith RThomas B(2018)Tangible Braille Plot: Tangibly Exploring Geo-Temporal Data in Virtual Reality2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA)10.1109/BDVA.2018.8534024(1-6)Online publication date: Oct-2018
https://doi.org/10.1109/BDVA.2018.8534024
Hu JYang BJensen CMa Y(2017)Enabling time-dependent uncertain eco-weights for road networksGeoinformatica10.1007/s10707-016-0272-z21:1(57-88)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1007/s10707-016-0272-z
Show More Cited By

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Spatial big data for eco-routing services: computational challenges and accomplishments

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Information & Contributors

Information

Published In

cover image SIGSPATIAL Special

SIGSPATIAL Special Volume 6, Issue 2

Big Spatial Data (part 1)

July 2014

39 pages

EISSN:1946-7729

DOI:10.1145/2744700

Editor:
Chi-Yin Chow
City University of Hong Kong, Hong Kong

Issue’s Table of Contents

Copyright © 2015 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 March 2015

Published in SIGSPATIAL Volume 6, Issue 2

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Cited By

Musleh MAbbar SStanojevic RMokbel M(2021)QARTAProceedings of the VLDB Endowment10.14778/3476249.347627914:11(2273-2282)Online publication date: 27-Oct-2021
https://dl.acm.org/doi/10.14778/3476249.3476279
Walsh JCunningham ASmith RThomas B(2018)Tangible Braille Plot: Tangibly Exploring Geo-Temporal Data in Virtual Reality2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA)10.1109/BDVA.2018.8534024(1-6)Online publication date: Oct-2018
https://doi.org/10.1109/BDVA.2018.8534024
Hu JYang BJensen CMa Y(2017)Enabling time-dependent uncertain eco-weights for road networksGeoinformatica10.1007/s10707-016-0272-z21:1(57-88)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1007/s10707-016-0272-z
Walsh JZucco JSmith RThomas B(2016)Temporal-Geospatial Cooperative Visual Analysis2016 Big Data Visual Analytics (BDVA)10.1109/BDVA.2016.7787050(1-8)Online publication date: Nov-2016
https://doi.org/10.1109/BDVA.2016.7787050

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