A cellular automata traffic flow modeling of desired speed variability

doi:10.1088/1674-1056/22/1/018902

Abstract The satisfaction rate of desired velocity in the case of mixture of fast and slow vehicles is studied by using cellular automaton method. It is found that at low density the satisfaction rate depends on the maximal velocity. However, the behavior of the satisfaction rate as a function of the coefficient of variance is independent of the maximal velocity. This is in good agreement with empirical results obtained by Lipshtat [Phys. Rev. E 79 066110 (2009)]. Furthermore, our numerical result demonstrates that at low density the satisfaction rate takes its higher values, whereas the coefficient of variance is close to zero. The coefficient of variance increases with increasing density, while the satisfaction rate decreases to zero. Moreover, we have also shown that, at low density the coefficient variance depends strongly on the probability of overtaking.

Keywords: traffic celluar automata satisfaction coefficient desired speed

Received: 11 April 2012
Revised: 12 July 2012
Accepted manuscript online:

PACS:	89.40.Bb	(Land transportation)
	02.70.Ac
	07.05.Tp	(Computer modeling and simulation)

Corresponding Authors: H. Ez-Zahraouy
E-mail: ezahamid@fsr.ac.ma

Cite this article:

K. Bentaleb, K. Jetto, H. Ez-Zahraouy, A. Benyoussef A cellular automata traffic flow modeling of desired speed variability 2013 Chin. Phys. B 22 018902

[1]	Chowdhury D and Nishinari K 2011 Stochastic Transport in Complex Systems from Molecules to Vehicles (Elsevier)
[2]	Klar A and Wegener R 2000 Transport Theory and Statistical Physics 29 479
[3]	Blue V, Bonetto F and Embrechts M 1996 Transportation Research Board Annual Meeting (Washington, DC)
[4]	Nagel K 1996 Phys. Rev. E 3 4655
[5]	Schadschneider A and Schreckenberg M 1993 J. Phys. A 26 679
[6]	Villar L and de Souza A 1994 Physica A 211 84
[7]	Ding J X, Huang H J and Tian Q 2011 Chin. Phys. B 20
[8]	Qian Y S, Shi P J, Zeng Q, Ma C X, Lin F, Sun P and Yin X T 2009 Chin. Phys. B 18 4037
[9]	Qian Y S, Shi P J, Zeng Q, Ma C X, Lin F, Sun P and Wang H L 2010 Chin. Phys. B 19 048201
[10]	Lu W Z, He H D and Dong L Y 2011 Chin. Phys. B 20 040514
[11]	Rawat K 2012 International Journal of Vehicular Technology 2012 1
[12]	Nagel K and Schreckenberg M 1992 J. Phys. I 2
[13]	Ez-Zahraouy H, Jetto K and Benyoussef A 2004 Eur. Phys. J. B 40 111
[14]	Ez-Zahraouy H, Jetto K and Benyoussef A 2006 Chin. J. Phys. 44 486
[15]	Lipshtat A 2009 Phys. Rev. E 79 066110
[16]	Jetto K, Ez-Zahraouy H and Benyoussef A 2010 Int. J. Mod. Phys. C 21 1311
[17]	Ben-Naim E and Krapivsky P L 1997 Phys. Rev. E 56 6680

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A cellular automata traffic flow modeling of desired speed variability

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