摘要We investigate the traffic flow volume data on the time dependent activity of Beijing's urban road network. The couplings between the average flux and the fluctuations on individual links are shown to follow certain scaling laws and yield a wide variety of scaling exponents between 1/2 and 1. To quantitatively explain this interesting phenomenon, a non-stationary Poisson arriving model is proposed. The scaling property is interpreted as the result of the time-variation of the arriving rate of flux over the network, which nicely explicates the effect of aggregation windows, and provides a concise model for the dependence of scaling exponent on the external/internal force ratio.
Abstract：We investigate the traffic flow volume data on the time dependent activity of Beijing's urban road network. The couplings between the average flux and the fluctuations on individual links are shown to follow certain scaling laws and yield a wide variety of scaling exponents between 1/2 and 1. To quantitatively explain this interesting phenomenon, a non-stationary Poisson arriving model is proposed. The scaling property is interpreted as the result of the time-variation of the arriving rate of flux over the network, which nicely explicates the effect of aggregation windows, and provides a concise model for the dependence of scaling exponent on the external/internal force ratio.
(Fluctuation phenomena, random processes, noise, and Brownian motion)
引用本文:
CHEN Yu-Dong;LI Li;ZHANG Yi;JIN Xue-Xiang. A Non-Stationary Poisson Model for the Scaling of Urban Traffic Fluctuations[J]. 中国物理快报, 2008, 25(5): 1912-1915.
CHEN Yu-Dong, LI Li, ZHANG Yi, JIN Xue-Xiang. A Non-Stationary Poisson Model for the Scaling of Urban Traffic Fluctuations. Chin. Phys. Lett., 2008, 25(5): 1912-1915.
[1] Disbro J E and Frame M 1989 Transport. Res. Rec. 1225 109 [2] Vlahogianni E I, Karlaftis M G, and Golias J C 2006 Transport. Res. C: Emer. 14 351 [3] Chen Y D, Li L, Zhang Y and Hu J M 2008 Mod. Phys.Lett. B 22 101 [4] de Menezes M A and Barabasi A L 2004 Phys. Rev.Lett. 92 028701 [5] de Menezes M A and Barabasi A L 2004 Phys. Rev.Lett. 93 068701 [6] Valverde S 2007 Europhys. Lett. 77 20002 [7] Nacher J C, Ochiai T and Akutsu T 2005 Mod. Phys.Lett. B 19 1169 [8] Eisler Z, Kertesz J, Yook S H and Barabasi A L 2005 Europhys. Lett. 69 664 [9] Duch J and Arenas A 2006 Phys. Rev. Lett. 96218702 [10] Duch J and Arenas A 2007 Eur. Phys. J. (SpecialTopics) 143 253 [11] Eisler Z and Kertesz J 2005 Phys. Rev. E 71057104 [12] Eisler Z and Kertesz J 2006 Phys. Rev. E 73046109 [13] Kertesz J and Eisler Z. arXiv:physics/0503139 [14] Kujawski B, Tadic B and Rodgers G J 2007 New J.Phys. 9 154 [15] Yook S H and de Menezes M A 2005 Europhys. Lett. 72 541 [16] Fischer W and Meier-Hellstern K 1992 PerformanceEvaluation 18 149 [17] Rossiter M 1987 Australian TelecommunicationResearch 21 53 [18] Bolch G, Greiner S, de Meer H and Trivedi K S 2006 Queueing Networks and Markov Chains: Modeling and PerformanceEvaluation with Computer Science Applications 2nd edn (New York:Wiley)
2008, Vol. 25 
