Collective Travel Planning in Spatial Networks

Shuo Shang; Lisi Chen; Zhewei Wei; Christian S. Jensen; Ji-Rong Wen; Panos Kalnis

doi:10.1109/ICDE.2017.36

Collective Travel Planning in Spatial Networks

Shuo Shang, Lisi Chen, Zhewei Wei, Christian S. Jensen, Ji-Rong Wen, Panos Kalnis

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Scopus citations

Abstract

We propose and investigate a novel query, the Collective Travel Planning (CTP) query, that finds the lowest-cost route connecting multiple query sources and a destination via at most k meeting points. This type of query is useful in organizing large events, and it can bring significant benefits to society and the environment: it can help optimize the allocation of transportation resources, reduce resource consumption, and enable smarter and greener transportation; and it can help reduce greenhouse-gas emissions and traffic congestion.

Original language	English (US)
Title of host publication	2017 IEEE 33rd International Conference on Data Engineering (ICDE)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	59-60
Number of pages	2
ISBN (Print)	9781509065431
DOIs	https://doi.org/10.1109/ICDE.2017.36
State	Published - May 18 2017

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work is partly supported by the National Natural Science Foundation of China (NSFC.61402532), and Beijing Nova Program.

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ICDE.2017.36

https://repository.kaust.edu.sa/bitstream/10754/624983/1/ID_693_ICDE_TKDE_Poster.pdf

Cite this

@inproceedings{ae56ce389a0a428ca84d48d7a2758a14,

title = "Collective Travel Planning in Spatial Networks",

abstract = "We propose and investigate a novel query, the Collective Travel Planning (CTP) query, that finds the lowest-cost route connecting multiple query sources and a destination via at most k meeting points. This type of query is useful in organizing large events, and it can bring significant benefits to society and the environment: it can help optimize the allocation of transportation resources, reduce resource consumption, and enable smarter and greener transportation; and it can help reduce greenhouse-gas emissions and traffic congestion.",

author = "Shuo Shang and Lisi Chen and Zhewei Wei and Jensen, {Christian S.} and Ji-Rong Wen and Panos Kalnis",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: This work is partly supported by the National Natural Science Foundation of China (NSFC.61402532), and Beijing Nova Program.",

year = "2017",

month = may,

day = "18",

doi = "10.1109/ICDE.2017.36",

language = "English (US)",

isbn = "9781509065431",

pages = "59--60",

booktitle = "2017 IEEE 33rd International Conference on Data Engineering (ICDE)",

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T1 - Collective Travel Planning in Spatial Networks

AU - Shang, Shuo

AU - Chen, Lisi

AU - Wei, Zhewei

AU - Jensen, Christian S.

AU - Wen, Ji-Rong

AU - Kalnis, Panos

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: This work is partly supported by the National Natural Science Foundation of China (NSFC.61402532), and Beijing Nova Program.

PY - 2017/5/18

Y1 - 2017/5/18

N2 - We propose and investigate a novel query, the Collective Travel Planning (CTP) query, that finds the lowest-cost route connecting multiple query sources and a destination via at most k meeting points. This type of query is useful in organizing large events, and it can bring significant benefits to society and the environment: it can help optimize the allocation of transportation resources, reduce resource consumption, and enable smarter and greener transportation; and it can help reduce greenhouse-gas emissions and traffic congestion.

AB - We propose and investigate a novel query, the Collective Travel Planning (CTP) query, that finds the lowest-cost route connecting multiple query sources and a destination via at most k meeting points. This type of query is useful in organizing large events, and it can bring significant benefits to society and the environment: it can help optimize the allocation of transportation resources, reduce resource consumption, and enable smarter and greener transportation; and it can help reduce greenhouse-gas emissions and traffic congestion.

UR - http://hdl.handle.net/10754/624983

UR - http://ieeexplore.ieee.org/document/7929932/

UR - http://www.scopus.com/inward/record.url?scp=85021196794&partnerID=8YFLogxK

U2 - 10.1109/ICDE.2017.36

DO - 10.1109/ICDE.2017.36

M3 - Conference contribution

SN - 9781509065431

SP - 59

EP - 60

BT - 2017 IEEE 33rd International Conference on Data Engineering (ICDE)

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

Collective Travel Planning in Spatial Networks

Abstract

Bibliographical note

UN SDGs

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