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Exploiting graph-theoretic tools for matching in carpooling applications

  • Luk Knapen
  • Ansar YasarEmail author
  • Sungjin Cho
  • Daniel Keren
  • Abed Abu Dbai
  • Tom Bellemans
  • Davy Janssens
  • Geert Wets
  • Assaf Schuster
  • Izchak Sharfman
  • Kanishka Bhaduri
Original Research

Abstract

An automatic service to match commuting trips has been designed. Candidate carpoolers register their personal profile and a set of periodically recurring trips. The Global CarPooling Matching Service shall advise registered candidates how to combine their commuting trips by carpooling. Planned periodic trips correspond to nodes in a graph; the edges are labeled with the probability for for success while negotiating to merge two planned trips by carpooling. The probability values are calculated by a learning mechanism using on one hand the registered person and trip characteristics and on the other hand the negotiation feedback. The probability values vary over time due to repetitive execution of the learning mechanism. As a consequence, the matcher needs to cope with a dynamically changing graph both with respect to topology and edge weights. In order to evaluate the matcher performance before deployment in the real world, it will be exercised using a large scale agent based model. This paper describes both the exercising model and the matcher.

Keywords

Graph theory Binary matching Agent-based modeling Scalability Dynamic networks Learning 

List of symbols

\(\mathcal{A}\)

The set of all agreements (see Definition 2)

\(\mathcal{I}\)

The set of all individuals

\(\mathcal{P}\)

The set of all pools (see Definition 3)

range(TOD)

24*60*60 (time-of-day)

range(TOW)

7*range(TOD) (time-of-week)

\(\mathcal{T}\)

The set of all periodicTripEx’s (see Definition 1)

TOD

Time of day; if expressed in seconds, cardinal \(\in [0,range(TOD)-1]\)

TOW

Time of week; if expressed in seconds, cardinal \(\in [0,range(TOW)-1]\)

\(t_{early}, t_{late}\)

Earliest resp. latest time

\(t_{d}, t_{a}\)

Departure resp. arrival time

Notes

Acknowledgments

The research leading tot these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under Grant Agreement Nr 270833.

References

  1. Agatz N, Erera A, Savelsbergh M, Wang X (2010) Sustainable passenger transportation: dynamic ride-sharing. Research Paper ERIM Report Series Reference No. ERS-2010-010-LIS, Erasmus University of Rotterdam Erasmus Research Institute of ManagementGoogle Scholar
  2. Agatz N, Erera AL, Savelsbergh MWP, Wang X (2011) Dynamic ride-sharing: a simulation study in metro atlanta. In: Procedia—Social and Behavioral Sciences 17:532–550Google Scholar
  3. Arentze T, Pelizaro C, Timmermans H (2005) Implementation of a model of dynamic activity-travel rescheduling decisions: an agent-based micro-simulation framework. In: Proceedings of CUPUM 05, Computers in Urban Planning and Urban Management, London, JuneGoogle Scholar
  4. Chun HW, Wong RYM (2003) N*-an agent-based negotiation algorithm for dynamic scheduling and rescheduling. Advanced Engineering Informatics 17:1–22. URL:http://www.elsevier.com/locate/aei
  5. Gaud N, Galland S, Hilaire V, Koukam A (2009) Programming multi-agent systems. Springer, Berlin, pp 104–119. ISBN 978-3-642-03277-6. doi: 10.1007/978-3-642-03278-3_7
  6. Guo J, Nandam S, Adams T (2012) A data collection framework for exploring the dynamic adaptation of Activity-Tra vel decisions. Tampa, Florida. TRB (Transportation Research Board)Google Scholar
  7. Iwan LH, Safar M (2010) Pattern mining from movement of mobile users. J Ambient Intell Humaniz Comput 1(4): 295–308 ISSN 1868-5137. doi: 10.1007/s12652-010-0024-0 Google Scholar
  8. Joh C-H (2002) Modeling individuals Activity Travel rescheduling heuristics: theory and numerical experiments. Transportation Research Board of the National Academies (1807 Paper 02-2173), pp 16–25Google Scholar
  9. Joh C-H (2004) Measuring and Predicting Adaptation in Multidimensional Activity-travel Patterns. PhD thesis, TUE, EindhovenGoogle Scholar
  10. Kamar E, Horvitz E (2009) Collaboration and shared plans in the open world: studies of ridesharing. In:Proceedings of the twenty-first international joint conference on artificial intelligenceGoogle Scholar
  11. Knapen L, Keren D, Yasar A, Cho S, Bellemans T, Janssens D, Wets G (2012a) Analysis of the co-routing problem in agent-based carpooling simulation. In: Procedia Computer Science, Niagara FallsGoogle Scholar
  12. Knapen L, Muhammad U, Bellemans T, Janssens D, Wets G (2012b) Framework to evaluate rescheduling due to unexpected events in an activity-based model. In: TRB 2013 Annual Meeting, Washington, D.C.Google Scholar
  13. Knapen L, Keren D, Yasar A, Cho S, Bellemans T, Janssens D, Wets G (2013) Estimating scalability issues while finding an optimal assignment for carpooling. In: Procedia Computer Science, Halifax, Nova Scotia, Canada, June 2013. Procedia Computer Science, Elsevier, AmsterdamGoogle Scholar
  14. Luetzenberger M, Masuch N, Hirsch B, Ahrndt S, Albayrak S (2011) Strategic behaviour in dynamic cities. In: Weed D (ed) Proceedings of the 43-rd Summer Computer Simulation Conference, The Hague, The Netherlands, pp 148–155Google Scholar
  15. Manzini R, Pareschi A (2012) A decision-support system for the car pooling problem. J Transp Technol 2:85–101CrossRefGoogle Scholar
  16. McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: Homophily in social networks. Annu Rev Sociol 27(1):415–444CrossRefGoogle Scholar
  17. Nijland L, Arentze T, Borgers A, Timmermans HJP (2009) Individuals’ activity-travel rescheduling behaviour: experiment and model-based analysis. Environ Plan A 41:1511–1522CrossRefGoogle Scholar
  18. Papandrea M, Giordano S (2013) Location prediction and mobility modelling for enhanced localization solution. J Ambient Intell Humaniz Comput pp 1–17. ISSN 1868-5137. doi: 10.1007/s12652-013-0175-x
  19. Ronald N (2012) Modelling the effects of social networks on activity and travel behaviour. PhD thesis, TUE, EindhovenGoogle Scholar
  20. Trasarti R, Pinelli F, Nanni M, Giannotti F (2011) Mining mobility user profiles for car pooling. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD ’11. ACM, New York, NY, USA, pp 1190–1198. ISBN 978-1-4503-0813-7. doi: 10.1145/2020408.2020591
  21. Varrentrapp K, Maniezzo V, Sttzle T (2002) The long term car pooling problem on the soundness of the problem formulation an d proof of NP-completeness. Technical Report AIDA-02-03, Fachgebiet Intellektik, Fachbereich Informatik, TU Darmstadt, Darmstadt, GermanyGoogle Scholar
  22. Xiao X, Zheng Y, Luo Q, Xie X (2012) Inferring social ties between users with human location history. J Ambient Intell Humaniz Comput, pp 1–17. ISSN 1868-5137. doi: 10.1007/s12652-012-0117-z

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Luk Knapen
    • 1
  • Ansar Yasar
    • 1
    Email author
  • Sungjin Cho
    • 1
  • Daniel Keren
    • 2
  • Abed Abu Dbai
    • 2
  • Tom Bellemans
    • 1
  • Davy Janssens
    • 1
  • Geert Wets
    • 1
  • Assaf Schuster
    • 3
  • Izchak Sharfman
    • 3
  • Kanishka Bhaduri
    • 4
  1. 1.Transportation Research Institute IMOB, Hasselt UniversityDiepenbeekBelgium
  2. 2.Department of Computer ScienceUniversity of HaifaHaifaIsrael
  3. 3.Faculty of Computer Science, Technion Israel Institute of TechnologyHaifaIsrael
  4. 4.Netflix Inc.Los GatosUSA

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