Abstract
Agent-based simulation has become an important modeling approach in activity-travel analysis. Social activities account for a large amount of travel and have an important effect on activity-travel scheduling. Participants in joint activities usually have various options regarding location, participants, and timing and take different approaches to make their decisions. In this context, joint activity participation requires negotiation among agents involved, so that conflicts among the agents can be addressed. Existing mechanisms do not fully provide a solution when utility functions of agents are nonlinear and non-monotonic. Considering activity-travel scheduling in time and space as an application, we propose a novel negotiation approach, which takes into account these properties, such as continuous and discrete issues, and nonlinear and non-monotonic utility functions, by defining a concession strategy and a search mechanism. The results of experiments show that agents having these properties can negotiate efficiently. Furthermore, the negotiation procedure affects individuals’ choices of location, timing, duration, and participants.
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References
Andreu M-C, Whinston MD, Green JR (1995) Microeconomic theory. Oxford University Press, Oxford
Avineri E (2006) Measuring and simulating altruistic behaviour in group travel choice decisions. In: Proceedings of the 11th international conference on travel behaviour research
Becker GS (1965) A theory of the allocation of time. Econ J 75(299):493–517
Ettema DF, Arentze TA, Timmermans HJP (2007) Social influences on household location, mobility and activity choice in integrated micro-simulation models. In: Proceedings of the workshop on frontiers in transportation, Amsterdam
Fang Z, Tu W, Li Q, Li Q (2011) A multi-objective approach to scheduling joint participation with variable space and time preferences and opportunities. J Transp Geogr 19(4):623–634
Fatima SS, Wooldridge M, Jennings NR (2004) An agenda-based framework for multi-issue negotiation. Artif Intell 152(1):1–45
Gliebe JP, Koppelman FS (2002) A model of joint activity participation between household members. Transportation 29(1):49–72
Grigolon AB, Kemperman ADAM, Timmermans HJP (2012) The influence of low-fare airlines on vacation choices of students: results of a stated portfolio choice experiment. Tour Manag 33(5):1174–1184
He M, Leung H, Jennings NR (2003) A fuzzy logic based bidding strategy for autonomous agents in continuous double auctions. IEEE Trans Knowl Data Eng 15(6):1345–1363
Hensher DA, Rose JM, Greene WH (2005) Applied choice analysis: a primer. Cambridge University Press, Cambridge
Hollander Y (2006) The applicability of non-cooperative game theory in transport system analysis. In: Proceedings of the transportation research board 85th annual meeting
Ito T, Hattori H, Klein M (2007) Multi-issue negotiation protocol for agents: exploring nonlinear utility spaces. In: Proceedings of international joint conference on artificial intelligence pp 1347–1352
Jennings NR, Faratin P, Lomuscio AR, Parsons S, Wooldridge MJ, Sierra C (2001) Automated negotiation: prospects, methods and challenges. Group Decis Negotiat 10(2):199–215
Kuwano M, Zhang J, Fujiwara A (2011) A dynamic discrete choice model with multidimensional social interactions. In: Proceedings of the transportation research board 90th annual meeting
Ma H, Ronald N, Wu M, Arentze T, Timmermans H (2010) Multi-player Multi-issue negotiation with incomplete information in agent-based activity-travel scheduling. In: Proceedings of the 10th international conference on design and decision support systems in architecture and urban planning
Ma H, Ronald N, Arentze T, Timmermans H (2011a) A New Credit Mechanism for Semicooperative Agent-Mediated Joint Activity-Travel Scheduling: negotiating with Incomplete Information. Transp Res Rec Issue 2230:104–110
Ma H, Ronald N, Arentze T, Timmermans H (2011b) Incorporate power into joint activity-travel scheduling. In: Proceedings of 16th HKSTS conference, Hong Kong
Ma H, Arentze T, Timmermans H (2012) An agent based model of dynamic activity-travel scheduling and implementation. In: Proceedings of the transportation research board 91st annual meeting
Neutens T, Schwanen T, Miller HJ (2010) Dealing with timing and synchronization in opportunities for joint activity participation. Geogr Anal 42(3):245–266
Nijland L, Arentze T, Borgers A, Timmermans H (2011) Modelling complex activity-travel scheduling decisions: procedure for the simultaneous estimation of activity generation and duration functions. Transp Rev 31(3):399–418
Nisan N, Roughgarden T, Tardos E, Vazirani VV (2007) Algorithmic game theory. Cambridge University Press, Cambridge
Osborne M, Rubinstein A (1994) A course in game theory. The MIT Press, Cambridge
Ren F, Zhang M, Fulcher J (2010) Bilateral single-issue negotiation model considering nonlinear utility and time constraint. In: Proceedings of the third international workshop on agent-based complex automated negotiations
Rindt CR, Marca JE, McNally MG (2003) An agent-based activity microsimulation kernel using a negotiation metaphor. In: Proceedings of the transportation research board 82nd annual meeting
Ronald N, Arentze TA, Timmermans HJP (2009) Modelling social interactions between individuals for joint activity-travel scheduling. In: Proceedings of the 11th international conference on travel behaviour research
Rubinstein A (1985) A bargaining model with incomplete information about time preferences. Econometrica 53(5):1151–1172
Sandholm T, Vulkan NY (1999) Bargaining with deadlines. In: Proceedings of association for the advancement of artificial intelligence
Scott DM, Kanaroglou PS (2002) An activity-episode generation model that captures interactions between household heads: development and empirical analysis. Transp Res B 36(10):875–896
van den Berg PEW, Arentze TA, Timmermans HJP (2012) A multilevel path analysis of contact frequency between social network. J Geogr Syst 14(2):125–141
Wainer Jacques, Roberto Paulo, Constantino EvertonRufino (2007) Scheduling meetings through multi-agent negotiations. Decis Support Syst 44(1):285–297
Wu M, de Weerdt M, La Poutre H (2009) Efficient methods for multi-agent multi-issue negotiation: allocating resources. In: Proceedings of principles of practice in multi-agent systems pp 97–112
Zeleny M (1982) Multiple criteria decision making. McGraw-Hill Book Company, NY
Zhang J, Timmermans HJP, Borgers A (2005) A model of household task allocation and time use. Transp Res B 39(1):81–95
Acknowledgments
The research leading to these results has received funding from the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007–2013)/ERC grant agreement no. 230517 (U4IA project). The views and opinions expressed in this publication represent those of the authors only. The ERC and European Community are not liable for any use that may be made of the information in this publication.
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Ma, H., Ronald, N., Arentze, T.A. et al. Negotiating on location, timing, duration, and participant in agent-mediated joint activity-travel scheduling. J Geogr Syst 15, 427–451 (2013). https://doi.org/10.1007/s10109-012-0173-0
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DOI: https://doi.org/10.1007/s10109-012-0173-0