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Social Diffusive Impact Analysis Based on Evolutionary Computations for a Novel Car Navigation System Sharing Individual Information in Urban Traffic Systems

Published online by Cambridge University Press:  12 September 2011

Jun Tanimoto  and
Hiroki Sagara
Jun Tanimoto*
Affiliation:
(IGSES, Kyushu University, Japan)
Hiroki Sagara*
Affiliation:
(IGSES, Kyushu University, Japan)
*
(Email: [email protected])
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Abstract

In this study, an experiment to establish a model for human-environment social systems, a multi-agent simulation model to deal with urban traffic congestion problems involving automobiles embedded with several strategies of car navigation systems (CNS), is presented. A shortest time route with route information sharing strategy (ST-RIS) is believed to be one of the solutions for a novel CNS based on bilateral information shared among automobile agents. We assume several strategies including ST-RIS for agents, which are defined differently in terms of their information-handling process. The question of which strategy is most appropriate for solving urban traffic congestion can be seen as a social dilemma, because social holistic utility may conflict with an agent's individual utility. The presented model shows that this social dilemma can be observed as a typical chicken-type dilemma, or as a typical minority game, where an agent who has adopted a minority strategy can earn more utility compared to when other strategies are used. Consequently, the model has illustrated that shortest time route with partial route information sharing strategy (ST-pRIS), which is an advanced strategic form of ST-RIS in which only partial information is shared among agents, has moderate potential to be diffused in a society from the viewpoint of the evolutionary game theory.

Keywords

Car Navigation SystemIntelligent Transport SystemInformation Sharing Strategy
Type
Research Article
Information
The Journal of Navigation , Volume 64 , Issue 4 , October 2011 , pp. 711 - 725
Copyright
Copyright © The Royal Institute of Navigation 2011

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References

REFERENCES

[1]Yamashita, T., Izumi, K., Kurumatani, K., and Nakashima, H., “Smooth traffic flow with a cooperative car navigation system,” in Proc. 4th international joint conference on Autonomous agents and multiagent system, pp. 478–485, 2005.CrossRefGoogle Scholar
[2]Mahmassani, H. S. and Jayakrishnan, R., “System Performance and User Response under Real-Time Information in a Congested Traffic Corridor,” Transportation Research, vol. 25A(5), pp. 293–307, 1991.CrossRefGoogle Scholar
[3]Yoshii, T., Akahane, H., and Kuwahara, M., “Impacts of the Accuracy of Traffic Information in Dynamic Route Guidance Systems,” The 3rd Annual World Congress on Intelligent Transport Systems, Orlando, CD-ROM, 1996.Google Scholar
[4]Kawamura, H., Kurumatani, K., and Ohuchi, A., “Modeling of Theme Park Problem with Multiagent for Mass User Support,” in Working Note of The International Joint Conference of Artificial Intelligence 2003, Workshop on Multiagent for Mass User Support I–7, 2003.CrossRefGoogle Scholar
[5]Arthur, W. B., “Inductive reasoning and bounded rationality,” in Proc. American Economic Association Papers, vol. 84, no. 2, pp. 406–411, 1994.Google Scholar
[6]Challet, D. and Zhang, Y. C., “Emergence of Cooperation and Organization in an Evolutionary Game,” Physica A, vol. 246, pp. 407–418, 1997.CrossRefGoogle Scholar
[7]Tanimoto, J. and Sagara, H., “Relationship between dilemma occurrence and the existence of a weakly dominant strategy in a two-player symmetric game,” BioSystems, 90(1), 105–114, 2007.CrossRefGoogle Scholar
[8]Tanimoto, J. and Sagara, H., “A study on emergence of Coordinated Alternating Reciprocity in a 2×2 game with 2-memory length strategy,” BioSystems, 90(3), 728–737, 2007.CrossRefGoogle Scholar
[9]Savit, R., Manuca, R., and Riolo, R., “Adaptive Competition, Market Efficiency, and Phase Transitions,” Physical Review Letter, vol. 82, no. 10, pp. 2203–2206, 1999.CrossRefGoogle Scholar
[10]Akaishi, J. and Arita, T., “Misperception, Communication and Diversity,” in Proc. Artificial Life VIII, pp. 350–357, 2002.Google Scholar
[11]Kerner, B.S., Konhäuser, P.; Structure and parameters of clusters in traffic flow, Physical Review E 50, 54–83, 1994.CrossRefGoogle ScholarPubMed
[12]Nagel, K., Schreckenberg, M.A.; A cellular automaton model for freeway traffic, Journal de Physique I 2, 2221–2229, 1992.CrossRefGoogle Scholar
[13]Lighthill, M.L., Whitham, G.B.; On kinematic waves. II. A theory of traffic flow on long crowded roads, Proceedings of the Royal Society of London Series A 229, 317–345, 1955.Google Scholar
[14]Helbing, D.; Improved fluid-dynamic model for vehicular traffic, Physical Review E 51, 3164–3169, 1995.CrossRefGoogle ScholarPubMed
[15]Pipes, L.A.; An operational analysis of traffic dynamics, Journal of Applied Physics 24, 274–281, 1953.CrossRefGoogle Scholar
[16]Gazis, C., Herman, R., Rothery, R.W.; Nonlinear follow-the-leader models of traffic flow, Operations Research 9, 545–567, 1961.CrossRefGoogle Scholar
[17]Bogers, E., Bierlaire, M., Hoogendoorn, S.; Modeling Learning in Route Choice, Transportation Research Record 2014, 1–8, 2007.CrossRefGoogle Scholar
[18]Ben-Akiva, M., Lerman, S.; Discrete choice analysis, The MIT Press, Cambridge Massachusetts, 1985.Google Scholar
[19]Ben-Akiva, M., De Palma, A., Kaysi, I.; Dynamic network models and driver information systems, Transportation Research A 25A(5), 251–266, 1991.CrossRefGoogle Scholar
[20]Vaughn, K. M., Reddy, P., Abdel-Aty, M.A., Kitamura, R., Jovanis, P.P.; Route Choice and Information Use: Initial Results from Simulation Experiments, Transportation Research Record 1516, 61–69, 1995.Google Scholar
[21]Khattak, A., Jovanis, P.; Capacity and Delay Estimation for Priority Unsignalized Intersections: Conceptual and Empirical Issues, Transportation Research Record 1287, 129–137, 1990.Google Scholar
[22]Khattak, A.; Intelligent Transportation Systems: Planning, Operations and Evaluation, CRC-Press, Boca Raton Florida, 2005.Google Scholar
[23]Bonsall, P.; Using an Interactive Route Choice Simulator to Investigate Drivers' Compliance with Route Guidance Advice. Transportation Research Record 1306, 100–110, 1991.Google Scholar
[24]Emmerink, R., Nikamp, P. (Eds.); Behavioral and Network Impacts of Driver Information Systems, Ashgate Publishing, Surrey, 1998.Google Scholar
[25]Yamauchi, A., Tanimoto, J., Hagishima, A., Sagara, H., “Dilemma Game Structure Observed in Traffic Flow at a 2-to-1 Lane Junction”, Physical Review E, 79, #036104, 2009.CrossRefGoogle Scholar
[26]Juran, I., Prashker, J.N., Bekhor, S., Ishai, I.; A dynamic traffic assignment model for the assessment of moving bottlenecks, Transportation Research Part C 17, 240–258, 2009.CrossRefGoogle Scholar
[27], Y.Sugiyama; Physics of Traffic Flow, Nagare 22, 95–108, 2003 (in Japanese).Google Scholar