Ride sharing: A network perspective

Erez Shmueli; Itzik Mazeh; Laura Radaelli; Alex Sandy Pentland; Yaniv Altshuler

doi:10.1007/978-3-319-16268-3_55

Ride sharing: A network perspective

Erez Shmueli, Itzik Mazeh, Laura Radaelli, Alex Sandy Pentland, Yaniv Altshuler

Department of Industrial Engineering

Tel Aviv University

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

Abstract

Ride sharing’s potential to improve traffic congestion as well as assist in reducing CO2 emission and fuel consumption was recently demonstrated by works such as [1]. Furthermore, it was shown that ride sharing can be implemented within a sound economic regime, providing values for all participants (e.g., Uber). Better understanding the utilization of ride sharing can help policy makers and urban planners in modifying existing urban transportation systems to increase their “ride sharing friendliness” as well as in designing new ride sharing oriented ones. In this paper, we study systematically the relationship between properties of the dynamic transportation network (implied by the aggregated rides) and the potential benefit of ride sharing. By analyzing a dataset of over 14 Million taxi trips taken in New York City during January 2013, we predict the potential benefit of ride sharing using topological properties of the rides network only. Such prediction can ease the analysis of urban areas, with respect to the potential efficiency of ride sharing for their inhabitants, without the need to carry out expensive and time consuming surveys, data collection and analysis operations.

Original language	English
Title of host publication	Social Computing, Behavioral-Cultural Modeling, and Prediction - 8th International Conference, SBP 2015, Proceedings
Editors	Kevin Xu, Nitin Agarwal, Nathaniel Osgood
Publisher	Springer Verlag
Pages	434-439
Number of pages	6
ISBN (Electronic)	9783319162676
DOIs	https://doi.org/10.1007/978-3-319-16268-3_55
State	Published - 1 Jan 2015
Event	8th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2015 - Washington, United States Duration: 31 Mar 2015 → 3 Apr 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9021

Conference

Conference	8th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2015
Country/Territory	United States
City	Washington
Period	31/03/15 → 3/04/15

UN SDGs

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

SDG 11 Sustainable Cities and Communities
SDG 13 Climate Action

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-16268-3_55

Cite this

Shmueli, E., Mazeh, I., Radaelli, L., Pentland, A. S., & Altshuler, Y. (2015). Ride sharing: A network perspective. In K. Xu, N. Agarwal, & N. Osgood (Eds.), Social Computing, Behavioral-Cultural Modeling, and Prediction - 8th International Conference, SBP 2015, Proceedings (pp. 434-439). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9021). Springer Verlag. https://doi.org/10.1007/978-3-319-16268-3_55

Shmueli, Erez ; Mazeh, Itzik ; Radaelli, Laura et al. / Ride sharing : A network perspective. Social Computing, Behavioral-Cultural Modeling, and Prediction - 8th International Conference, SBP 2015, Proceedings. editor / Kevin Xu ; Nitin Agarwal ; Nathaniel Osgood. Springer Verlag, 2015. pp. 434-439 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{be8005a96761419a942df9764782dee8,

title = "Ride sharing: A network perspective",

abstract = "Ride sharing{\textquoteright}s potential to improve traffic congestion as well as assist in reducing CO2 emission and fuel consumption was recently demonstrated by works such as [1]. Furthermore, it was shown that ride sharing can be implemented within a sound economic regime, providing values for all participants (e.g., Uber). Better understanding the utilization of ride sharing can help policy makers and urban planners in modifying existing urban transportation systems to increase their “ride sharing friendliness” as well as in designing new ride sharing oriented ones. In this paper, we study systematically the relationship between properties of the dynamic transportation network (implied by the aggregated rides) and the potential benefit of ride sharing. By analyzing a dataset of over 14 Million taxi trips taken in New York City during January 2013, we predict the potential benefit of ride sharing using topological properties of the rides network only. Such prediction can ease the analysis of urban areas, with respect to the potential efficiency of ride sharing for their inhabitants, without the need to carry out expensive and time consuming surveys, data collection and analysis operations.",

author = "Erez Shmueli and Itzik Mazeh and Laura Radaelli and Pentland, \{Alex Sandy\} and Yaniv Altshuler",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 8th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2015 ; Conference date: 31-03-2015 Through 03-04-2015",

year = "2015",

month = jan,

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doi = "10.1007/978-3-319-16268-3\_55",

language = "الإنجليزيّة",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "434--439",

editor = "Kevin Xu and Nitin Agarwal and Nathaniel Osgood",

booktitle = "Social Computing, Behavioral-Cultural Modeling, and Prediction - 8th International Conference, SBP 2015, Proceedings",

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Shmueli, E, Mazeh, I, Radaelli, L, Pentland, AS & Altshuler, Y 2015, Ride sharing: A network perspective. in K Xu, N Agarwal & N Osgood (eds), Social Computing, Behavioral-Cultural Modeling, and Prediction - 8th International Conference, SBP 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9021, Springer Verlag, pp. 434-439, 8th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2015, Washington, United States, 31/03/15. https://doi.org/10.1007/978-3-319-16268-3_55

Ride sharing: A network perspective. / Shmueli, Erez; Mazeh, Itzik; Radaelli, Laura et al.
Social Computing, Behavioral-Cultural Modeling, and Prediction - 8th International Conference, SBP 2015, Proceedings. ed. / Kevin Xu; Nitin Agarwal; Nathaniel Osgood. Springer Verlag, 2015. p. 434-439 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9021).

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

TY - GEN

T1 - Ride sharing

T2 - 8th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2015

AU - Shmueli, Erez

AU - Mazeh, Itzik

AU - Radaelli, Laura

AU - Pentland, Alex Sandy

AU - Altshuler, Yaniv

PY - 2015/1/1

Y1 - 2015/1/1

N2 - Ride sharing’s potential to improve traffic congestion as well as assist in reducing CO2 emission and fuel consumption was recently demonstrated by works such as [1]. Furthermore, it was shown that ride sharing can be implemented within a sound economic regime, providing values for all participants (e.g., Uber). Better understanding the utilization of ride sharing can help policy makers and urban planners in modifying existing urban transportation systems to increase their “ride sharing friendliness” as well as in designing new ride sharing oriented ones. In this paper, we study systematically the relationship between properties of the dynamic transportation network (implied by the aggregated rides) and the potential benefit of ride sharing. By analyzing a dataset of over 14 Million taxi trips taken in New York City during January 2013, we predict the potential benefit of ride sharing using topological properties of the rides network only. Such prediction can ease the analysis of urban areas, with respect to the potential efficiency of ride sharing for their inhabitants, without the need to carry out expensive and time consuming surveys, data collection and analysis operations.

AB - Ride sharing’s potential to improve traffic congestion as well as assist in reducing CO2 emission and fuel consumption was recently demonstrated by works such as [1]. Furthermore, it was shown that ride sharing can be implemented within a sound economic regime, providing values for all participants (e.g., Uber). Better understanding the utilization of ride sharing can help policy makers and urban planners in modifying existing urban transportation systems to increase their “ride sharing friendliness” as well as in designing new ride sharing oriented ones. In this paper, we study systematically the relationship between properties of the dynamic transportation network (implied by the aggregated rides) and the potential benefit of ride sharing. By analyzing a dataset of over 14 Million taxi trips taken in New York City during January 2013, we predict the potential benefit of ride sharing using topological properties of the rides network only. Such prediction can ease the analysis of urban areas, with respect to the potential efficiency of ride sharing for their inhabitants, without the need to carry out expensive and time consuming surveys, data collection and analysis operations.

UR - http://www.scopus.com/inward/record.url?scp=84925310848&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-16268-3_55

DO - 10.1007/978-3-319-16268-3_55

M3 - منشور من مؤتمر

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 434

EP - 439

BT - Social Computing, Behavioral-Cultural Modeling, and Prediction - 8th International Conference, SBP 2015, Proceedings

A2 - Xu, Kevin

A2 - Agarwal, Nitin

A2 - Osgood, Nathaniel

PB - Springer Verlag

Y2 - 31 March 2015 through 3 April 2015

ER -

Shmueli E, Mazeh I, Radaelli L, Pentland AS, Altshuler Y. Ride sharing: A network perspective. In Xu K, Agarwal N, Osgood N, editors, Social Computing, Behavioral-Cultural Modeling, and Prediction - 8th International Conference, SBP 2015, Proceedings. Springer Verlag. 2015. p. 434-439. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-16268-3_55

Ride sharing: A network perspective

Abstract

Publication series

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

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