Abstract
The optimal information feedback has a significant effect on many socioeconomic systems like stock market and traffic systems aiming to make full use of resources. In this paper, we study dynamics of traffic flow with real-time information. The influence of a feedback strategy named vehicle number feedback strategy (VNFS) is introduced, in which we only calculate the vehicle number of first 500 route sites from the entrance. Moreover, the two-route traffic system has only one entrance and one exit, which is different from those in the previous work. Our model incorporates the effects of adaptability into the cellular automaton models of traffic flow, and simulation results by adopting this optimal information feedback strategy have demonstrated higher efficiency in controlling spatial distribution of traffic patterns than the other three information feedback strategies, i.e., TTFS, MVFS and CCFS.
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References
Chowdhury D, Santen L, Schadschneider A. Statistical physics of vehicular traffic and some related systems. Phys Rep, 2000, 329: 199–329
Helbing D. Traffic and related self-driven many-particle systems. Rev Mod Phys, 2001, 73: 1067–1141
Nagatani T. The physics of traffic jams. Rep Prog Phys, 2002, 65: 1331–1386
Long J C, Gao Z Y, Ren H L, et al. Urban traffic congestion propagation and bottleneck identification. Sci China Ser F-Inf Sci, 2008, 51: 948–964
Rothery R W. Traffic flow theory. In: Gartner N, Messner C J, Rathi A J, eds. Transportation Research Board Special Report, Vol. 165. Washington, DC: Transportation Research Board, 1992. Chap. 4
Paveri-Fontana S L. Boltzmann-like treatments for traffic flow-critical review of basic model and an alternative proposal for dilute traffic analysis. Transp Res, 1975, 9: 225–235
Lehmann H. Distribution function properties and the fundamental diagram in kinetic traffic flow theory. Phys Rev E, 1996, 54: 6058–6064
Wagner C, Hoffmann C, Sollacher R, et al. Second-order continuum traffic flow model. Phys Rev E, 1996, 54: 5073–5085
Helbing D. Gas-kinetic derivation of Navier-Stokes-like traffic equations. Phys Rev E, 1996, 53: 2366–2381
Helbing D, Treiber M. Gas-kinetic-based traffic model explaining observed hysteretic phase transition. Phys Rev Lett, 1998, 81: 3042–3045
Dong L F, Shu Y T, Chen H M, et al. Packet delay analysis on IEEE 802.11 DCF under finite load traffic in multi-hop ad hoc networks. Sci China Ser F-Inf Sci, 2008, 51: 408–416
Nagel K, Schreckenberg M. A cellular automaton model for freeway traffic. J Phys I, 1992, 2: 2221–2229
Biham O, Middleton A A, Levine D. Self-organization and a dynamic transition in traffic-flow models. Phys Rev A, 1992, 46: R6124–R6127
Yokoya Y. Dynamics of traffic flow with real-time traffic information. Phys Rev E, 2004, 69: 016121
Friesz T L, Luque J, Tobin R L, et al. Dynamic network traffic assignment considered as a continuous-time optimalcontrol problem. Oper Res, 1989, 37: 893–901
Ben-Akiva M, De Palma A, Kaysi I. Dynamic network models and driver information-systems. Transp Res Part A, 1991, 25: 251–266
Dong C F, Ma X, Wang B H. Weighted congestion coefficient feedback in intelligent transportation systems. Phys Lett A, 2010, 374: 1326–1331
Arnott R, de Palma A, Lindsey R. Does providing information to drivers reduce traffic congestion. Transp Res Part A, 1991, 25: 309–318
Kachroo P, Özbay K. Real time dynamic traffic routing-based on fuzzy feedback control methodology. Transp Res Rec, 1996, 1556: 137–146
Wahle J, Bazzan A L C, Klügl F, et al. Decision dynamics in a traffic scenario. Phys A, 2000, 287: 669–681
Lee K, Hui P M, Wang B H, et al. Effects of announcing global information in a two-route traffic flow model. J Phys Soc Jpn, 2001, 70: 3507–3510
Wang W X, Wang B H, Zheng W C, et al. Advanced information feedback in intelligent traffic systems. Phys Rev E, 2005, 72: 066702
Wang B H, Mao D, Hui P M. The two-way decision traffic flow model: mean field theory. In: Proceedings of the Second International Symposium on Complexity Science, Shanghai, 2002. 204
Wang B H, Wang L, Xu B M, et al. The gradual accelerating traffic flow gellular automaton model in which only high speed car can be delayed. Acta Phys Sin, 2000, 49: 1926–1932
Fu C J, Wang B H, Ying C Y, et al. Intelligent decision-making in a two-route traffic flow model. Acta Phys Sin, 2006, 55: 4032–4038
Peng L J, Kang R. One-dimensional cellular automaton model of traffic flow considering drivers’ features. Acta Phys Sin, 2009, 58: 830–835
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Dong, C., Ma, X. & Wang, B. Advanced information feedback strategy in intelligent two-route traffic flow systems. Sci. China Inf. Sci. 53, 2265–2271 (2010). https://doi.org/10.1007/s11432-010-4070-1
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DOI: https://doi.org/10.1007/s11432-010-4070-1