Advanced Information Feedback Coupled with an Evolutionary Game in Intelligent Transportation Systems

Chapter
Part of the Springer Series in Reliability Engineering book series (RELIABILITY)

Abstract

It has been explored for decades how to alleviate traffic congestions and improve traffic fluxes by optimizing routing strategies in intelligent transportation systems (ITSs). It, however, has still remained as an unresolved issue and an active research topic due to the complexity of real traffic systems. In this study, we propose two concise and efficient feedback strategies, namely mean velocity difference feedback strategy and congestion coefficient difference feedback strategy. Both newly proposed strategies are based upon the time-varying trend in feedback information, which can achieve higher route flux with better stability compared to previous strategies proposed in the literature. In addition to improving feedback strategies, we also investigate information feedback coupled with an evolutionary game in a 1-2-1-lane ITS with dynamic periodic boundary conditions to better mimic the driver behavior at the 2-to-1 lane junction, where the evolutionary snowdrift game is adopted. We propose an improved self-questioning Fermi (SQF) updating mechanism by taking into account the self-play payoff, which shows several advantages compared to the classical Fermi mechanism. Interestingly, our model calculations show that the SQF mechanism can prevent the system from being enmeshed in a globally defective trap, in good agreement with the analytic solutions derived from the mean-field approximation.

Keywords

Advanced information feedback Two-route guidance strategy Cellular automaton model Evolutionary game theory Snowdrift game Stochastic Fermi rule Self-questioning updating rule Self-play payoff Intelligent transportation system Mean-field approximation 

Notes

Acknowledgments

C.F. Dong appreciates many fruitful discussions with Prof. Bing-Hong Wang at the University of Science and Technology of China, and Dr. Nan Liu at the University of Chicago. The authors would like to thank the editors and the anonymous referees’ helpful comments and suggestions.

References

  1. 1.
    Adler JL, Blue VJ (1998) Toward the design of intelligent traveler information systems. Transp Res Part C 6:157–172CrossRefGoogle Scholar
  2. 2.
    Axelrod R (1984) The evolution of cooperation. Basic books, New YorkGoogle Scholar
  3. 3.
    Axelrod R, Hamilton WD (1981) The evolution of cooperation. Science 211:1390–1396CrossRefMATHMathSciNetGoogle Scholar
  4. 4.
    Barato AC, Hinrichsen H (2008) Boundary-induced nonequilibrium phase transition into an absorbing state. Phys Rev Lett 100:165701CrossRefGoogle Scholar
  5. 5.
    Barlovic R, Santen L, Schadschneider A, Schreckenberg M (1998) Metastable states in cellular automata for traffic flow. Eur Phys J B 5:793–800CrossRefGoogle Scholar
  6. 6.
    Bellouquid A, Delitala M (2011) Asymptotic limits of a discrete kinetic theory model of vehicular traffic. Appl Math Lett 24:149–155CrossRefMathSciNetGoogle Scholar
  7. 7.
    Bier VM, Hausken K (2013) Defending and attacking a network of two arcs subject to traffic congestion. Reliab Eng Syst Saf 112:214–224CrossRefGoogle Scholar
  8. 8.
    Biham O, Alan Middleton A, Levine D (1992) Self-organization and a dynamical transition in traffic-flow models. Phys Rev A 46:R6124CrossRefGoogle Scholar
  9. 9.
    Chen BK, Sun XY, Wei H, Dong CF, Wang BH (2011) Piecewise function feedback strategy in intelligent traffic systems with a speed limit bottleneck. Int J Mod Phys C 22:849–860CrossRefMATHGoogle Scholar
  10. 10.
    Chen BK, Sun XY, Wei H, Dong CF, Wang BH (2012) A comprehensive study of advanced information feedbacks in real-time intelligent transportation systems. Phys A 391:2730–2739CrossRefGoogle Scholar
  11. 11.
    Chen BK, Dong CF, Liu YK, Tong W, Zhang WY, Liu J, Wang BH (2012) Real-time information feedback based on a sharp decay weighted function. Comput Phys Commun 183:2081–2088CrossRefMathSciNetGoogle Scholar
  12. 12.
    Chowdhury D, Santen L, Schadschneider A (2000) Statistical physics of vehicular traffic and some related systems. Phys Rep 329:199–329CrossRefMathSciNetGoogle Scholar
  13. 13.
    Colman AM (1995) Game theory and its applications in the social and biological sciences. Butterworth-Heinemann, OxfordGoogle Scholar
  14. 14.
    Dong CF (2009) News story: intelligent traffic system predicts future traffic flow on multiple roads. PHYSorg.com. 12 Oct 2009
  15. 15.
    Dong CF, Ma X, Wang GW, Sun XY, Wang BH (2009) Prediction feedback in intelligent transportation systems. Phys A 388:4651–4657CrossRefGoogle Scholar
  16. 16.
    Dong CF, Ma X (2010) Corresponding angle feedback in an innovative weighted transportation system. Phys Lett A 374:2417–2423CrossRefMATHGoogle Scholar
  17. 17.
    Dong CF, Ma X, Wang BH (2010) Weighted congestion coefficient feedback in intelligent transportation systems. Phys Lett A 374:1326–1331CrossRefMATHGoogle Scholar
  18. 18.
    Dong CF, Ma X, Wang BH (2010) Effects of vehicle number feedback in multi-route intelligent traffic systems. Int J Mod Phys C 21:1081–1093CrossRefMATHGoogle Scholar
  19. 19.
    Dong CF, Ma X, Wang BH, Sun XY (2010) Effects of prediction feedback in multi-route intelligent transportation systems. Phys A 389:3274–3281CrossRefGoogle Scholar
  20. 20.
    Dong CF, Paty CS (2011) Application of adaptive weights to intelligent information systems: an intelligent transportation system as a case study. Inf Sci 181:5042–5052CrossRefGoogle Scholar
  21. 21.
    Dong CF, Wang BH (2011) Applications of cellular automaton model to advanced information feedback in intelligent traffic systems. In: Salcido A (ed) Cellular automata—simplicity behind complexity, pp 237–258. ISBN 978-953-307-579-2Google Scholar
  22. 22.
    Dong CF, Ma X (2012) Dynamic weight in intelligent transportation systems: a comparison based on two exit scenarios. Phys A 391:2712–2719CrossRefGoogle Scholar
  23. 23.
    Fukui M, Nishinari K, Yokoya Y, Ishibashi Y (2009) Effect of real-time information upon traffic flows on crossing roads. Phys A 388:1207–1212CrossRefGoogle Scholar
  24. 24.
    Gao K, Wang WX, Wang BH (2007) Self-questioning games and ping-pong effect in the BA network. Phys A 380:528–538CrossRefGoogle Scholar
  25. 25.
    Gazis DC, Herman R, Rothery RW (1961) Nonlinear follow-the-leader models of traffic flow. Oper Res 9:545–567CrossRefMATHMathSciNetGoogle Scholar
  26. 26.
    Hao QY, Jiang R, Hu MB, Jia B, Wu QS (2011) Pedestrian flow dynamics in a lattice gas model coupled with an evolutionary game. Phys Rev E 84:036107CrossRefGoogle Scholar
  27. 27.
    He ZB, Chen BK, Jia N, Guan W, Lin BC, Wang BH (2014) Route guidance strategies revisited: comparison and evaluation in an asymmetric two-route traffic network. Int J Mod Phys C 25:1450005CrossRefGoogle Scholar
  28. 28.
    Helbing D (2001) Traffic and related self-driven many-particle systems. Rev Mod Phys 73:1067–1141CrossRefGoogle Scholar
  29. 29.
    Helbing D, Treiber M (1998) Gas-kinetic-based traffic model explaining observed hysteretic phase transition. Phys Rev Lett 81:3042–3045CrossRefGoogle Scholar
  30. 30.
    Hino Y, Nagatani T (2014) Effect of bottleneck on route choice in two-route traffic system with real-time information. Phys A 395:425–433CrossRefMathSciNetGoogle Scholar
  31. 31.
    Hofbauer J, Sigmund K (1998) Evolutionary games and population dynamics. Cambridge University Press, CambridgeCrossRefMATHGoogle Scholar
  32. 32.
    Kerner BS, Konhäuser P (1994) Structure and parameters of clusters in traffic flow. Phys Rev E 50:54–83CrossRefGoogle Scholar
  33. 33.
    Kerner BS (2011) Optimum principle for a vehicular traffic network: minimum probability of congestion. J. Phys. A 44:092001CrossRefMathSciNetGoogle Scholar
  34. 34.
    Laval JA, Leclercq L (2010) Mechanism to describe stop-and-go waves: a mechanism to describe the formation and propagation of stop-and-go waves in congested freeway traffic. Phil Trans R Soc A 368:4519CrossRefMATHMathSciNetGoogle Scholar
  35. 35.
    Lee K, Hui PM, Wang BH, Johnson NF (2001) Effects of announcing global information in a two-route traffic flow model. J Phys Soc Jpn 70:3507–3510CrossRefGoogle Scholar
  36. 36.
    Li XB, Wu QS, Jiang R (2001) Cellular automaton model considering the velocity effect of a car on the successive car. Phys Rev E 64:066128CrossRefGoogle Scholar
  37. 37.
    Li RH, Yu JX, Lin J (2013) Evolution of cooperation in spatial Traveler’s Dilemma game. PLoS ONE 8:e58597CrossRefGoogle Scholar
  38. 38.
    Nagatani T (2002) The physics of traffic jams. Rep Prog Phys 65:1331–1386CrossRefGoogle Scholar
  39. 39.
    Nagel K, Schreckenberg M (1992) A cellular automaton model for freeway traffic. J Phys I 2:2221–2229Google Scholar
  40. 40.
    Nakata M, Yamauchi A, Tanimoto J, Hagishima A (2010) Dilemma game structure hidden in traffic flow at a bottleneck due to a 2 into 1 lane junction. Phys A 389:5353–5361CrossRefGoogle Scholar
  41. 41.
    Nowak M, May RM (1992) Evolutionary games and spatial chaos. Nature 359:826CrossRefGoogle Scholar
  42. 42.
    Orosz G, Wilson RE, Stépán G (2010) Traffic jams: dynamics and control. Phil Trans R Soc A 368:4455–4479CrossRefMATHGoogle Scholar
  43. 43.
    Perc M (2007) Premature seizure of traffic flow due to the introduction of evolutionary games. New J Phys 9:3CrossRefGoogle Scholar
  44. 44.
    Roughgarden T (2003) The price of anarchy is independent of the network topology. J Comput Syst Sci 67:341–364CrossRefMATHMathSciNetGoogle Scholar
  45. 45.
    Sugden R (1986) The economics of rights, cooperation and welfare. Blackwell, OxfordGoogle Scholar
  46. 46.
    Sun XY, Jiang R, Hao QY, Wang BH (2010) Phase transition in random walks coupled with evolutionary game. Europhys Lett 92:18003CrossRefGoogle Scholar
  47. 47.
    Szabó G, Töke C (1998) Evolutionary prisoner’s dilemma game on a square lattice. Phys Rev E 58:69–73CrossRefGoogle Scholar
  48. 48.
    Szilagyi MN (2006) Agent-based simulation of the n-person chicken game. In: Jorgensen S, Quincampoix M, Vincent TL (eds) Advances in dynamical games, vol 9. Annals of the International Society of Dynamic Games, Birkhäuser, Boston, pp 695–703Google Scholar
  49. 49.
    Tang TQ, Li CY, Huang HJ (2010) A new car-following model with the consideration of the driver’s forecast effect. Phys Lett A 374:3951–3956CrossRefMATHGoogle Scholar
  50. 50.
    Tanimoto J, Hagishima A, Tanaka Y (2010) Study of bottleneck effect at an emergency evacuation exit using cellular automata model, mean field approximation analysis, and game theory. Phys A 389:5611CrossRefGoogle Scholar
  51. 51.
    von Neumann J, Morgenstern O (1944) Theory of games and economic behaviour. Princeton University Press, PrincetonGoogle Scholar
  52. 52.
    Wahle J, Bazzan ALC, Klügl F, Schreckenberg M (2000) Decision dynamics in a traffic scenario. Phys A 287:669–681CrossRefGoogle Scholar
  53. 53.
    Wahle J, Bazzan ALC, Klügl F, Schreckenberg M (2002) The impact of real-time information in a two-route scenario using agent-based simulation. Transp Res Part C 10:399–417CrossRefGoogle Scholar
  54. 54.
    Wang WX, Wang BH, Zheng WC, Yin CY, Zhou T (2005) Advanced information feedback in intelligent transportation systems. Phys Rev E 72:066702CrossRefGoogle Scholar
  55. 55.
    Wang WX, Ren J, Chen GR, Wang BH (2006) Memory-based snowdrift game on networks. Phys Rev E 74:056113CrossRefGoogle Scholar
  56. 56.
    Wang XF, Zhuang J (2011) Balancing congestion and security in the presence of strategic applicants with private information. Eur J Oper Res 212:100–111CrossRefMATHMathSciNetGoogle Scholar
  57. 57.
    Xiang Z-T, Li Y-J, Chen Y-F, Xiong L (2013) Simulating synchronized traffic flow and wide moving jam based on the brake light rule. Phys A 392:5399–5413CrossRefGoogle Scholar
  58. 58.
    Yamauchi A, Tanimoto J, Hagishima A, Sagara H (2009) Dilemma game structure observed in traffic flow at a 2-to-1 lane junction. Phys Rev E 79:036104CrossRefGoogle Scholar
  59. 59.
    Zhao X-M, Xie D-F, Gao Z-Y, Gao L (2013) Equilibrium of a two-route system with delayed information feedback strategies. Phys Lett A 377:3161–3169CrossRefGoogle Scholar
  60. 60.
    Zheng XP, Cheng Y (2011) Conflict game in evacuation process: a study combining cellular automata model. Phys A 390:1042CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.AOSS DepartmentCollege of Engineering, University of MichiganAnn ArborUSA
  2. 2.Department of PhysicsSyracuse UniversitySyracuseUSA
  3. 3.Department of Electronic and Information EngineeringHong Kong Polytechnic UniversityHung Hom, KowloonHong Kong
  4. 4.Division of Logistics and Transportation, Graduate School at ShenzhenTsinghua UniversityShenzhenPeople’s Republic of China

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