Hostname: page-component-76fb5796d-25wd4 Total loading time: 0 Render date: 2024-04-25T09:40:53.667Z Has data issue: false hasContentIssue false

Traffic flow densities in large transport networks

Published online by Cambridge University Press:  17 November 2017

Christian Hirsch*
Affiliation:
Ludwig-Maximilians-Universität München
Benedikt Jahnel*
Affiliation:
Weierstrass Institute
Paul Keeler*
Affiliation:
Weierstrass Institute
Robert I. A. Patterson*
Affiliation:
Weierstrass Institute
*
* Postal address: Mathematisches Institut, Ludwig-Maximilians-Universität München, Theresienstraße 39, 80333 München, Germany. Email address: hirsch@math.lmu.de
** Postal address: Weierstrass Institute for Applied Analysis and Stochastics, Mohrenstr. 39, 10117 Berlin, Germany.
** Postal address: Weierstrass Institute for Applied Analysis and Stochastics, Mohrenstr. 39, 10117 Berlin, Germany.
** Postal address: Weierstrass Institute for Applied Analysis and Stochastics, Mohrenstr. 39, 10117 Berlin, Germany.

Abstract

We consider transport networks with nodes scattered at random in a large domain. At certain local rates, the nodes generate traffic flows according to some navigation scheme in a given direction. In the thermodynamic limit of a growing domain, we present an asymptotic formula expressing the local traffic flow density at any given location in the domain in terms of three fundamental characteristics of the underlying network: the spatial intensity of the nodes together with their traffic generation rates, and of the links induced by the navigation. This formula holds for a general class of navigations satisfying a link-density and a sub-ballisticity condition. As a specific example, we verify these conditions for navigations arising from a directed spanning tree on a Poisson point process with inhomogeneous intensity function.

Type
Research Article
Copyright
Copyright © Applied Probability Trust 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1] Baccelli, F. and Bordenave, C. (2007). The radial spanning tree of a Poisson point process. Ann. Appl. Prob. 17, 305359. CrossRefGoogle Scholar
[2] Baccelli, F., Tchoumatchenko, K. and Zuyev, S. (2000). Markov paths on the Poisson–Delaunay graph with applications to routing in mobile networks. Adv. Appl. Prob. 32, 118. CrossRefGoogle Scholar
[3] Bhatt, A. G. and Roy, R. (2004). On a random directed spanning tree. Adv. Appl. Prob. 36, 1942. CrossRefGoogle Scholar
[4] Bonichon, N. and Marckert, J.-F. (2011). Asymptotics of geometrical navigation on a random set of points in the plane. Adv. Appl. Prob. 43, 899942. CrossRefGoogle Scholar
[5] Bordenave, C. (2008). Navigation on a Poisson point process. Ann. Appl. Prob. 18, 708746. CrossRefGoogle Scholar
[6] Bramson, M. and Durrett, R. (eds) (1999). Perplexing Problems in Probability. Birkhäuser, Boston, MA. CrossRefGoogle Scholar
[7] Chiu, S. N., Stoyan, D., Kendall, W. S. and Mecke, J. (2013). Stochastic Geometry and Its Applications, 3rd edn. John Wiley, Chichester. CrossRefGoogle Scholar
[8] Federer, H. (1969). Geometric Measure Theory. Springer, New York. Google Scholar
[9] Ferrari, P. A., Landim, C. and Thorisson, H. (2004). Poisson trees, succession lines and coalescing random walks. Ann. Inst. H. Poincaré Prob. Statist. 40, 141152. CrossRefGoogle Scholar
[10] Howard, C. D. and Newman, C. M. (1997). Euclidean models of first-passage percolation. Prob. Theory Relat. Fields 108, 153170. CrossRefGoogle Scholar
[11] Howard, C. D. and Newman, C. M. (2001). Geodesics and spanning trees for Euclidean first-passage percolation. Ann. Prob. 29, 577623. CrossRefGoogle Scholar
[12] Keeler, H. P. and Taylor, P. G. (2010). A stochastic analysis of a greedy routing scheme in sensor networks. SIAM J. Appl. Math. 70, 22142238. CrossRefGoogle Scholar
[13] Keeler, H. P. and Taylor, P. G. (2012). Random transmission radii in greedy routing models for ad hoc sensor networks. SIAM J. Appl. Math. 72, 535557. CrossRefGoogle Scholar
[14] Licea, C. and Newman, C. M. (1996). Geodesics in two-dimensional first-passage percolation. Ann. Prob. 24, 399410. CrossRefGoogle Scholar
[15] Liggett, T. M., Schonmann, R. H. and Stacey, A. M. (1997). Domination by product measures. Ann. Prob. 25, 7195. CrossRefGoogle Scholar
[16] Mao, G. and Anderson, B. D. O. (2014). Capacity of large wireless networks with generally distributed nodes. IEEE Trans. Wireless Commun. 13, 16781691. CrossRefGoogle Scholar
[17] Newman, C. M. and Piza, M. S. T. (1995). Divergence of shape fluctuations in two dimensions. Ann. Prob. 23, 9771005. Google Scholar
[18] Penrose, M. D. (2003). Random Geometric Graphs. Oxford University Press. CrossRefGoogle Scholar
[19] Penrose, M. D. and Wade, A. R. (2006). On the total length of the random minimal directed spanning tree. Adv. Appl. Prob. 38, 336372. CrossRefGoogle Scholar
[20] Penrose, M. D. and Wade, A. R. (2010). Limit theorems for random spatial drainage networks. Adv. Appl. Prob. 42, 659688. CrossRefGoogle Scholar
[21] Penrose, M. D. and Yukich, J. E. (2002). Limit theory for random sequential packing and deposition. Ann. Appl. Prob. 12, 272301. CrossRefGoogle Scholar
[22] Penrose, M. D. and Yukich, J. E. (2003). Weak laws of large numbers in geometric probability. Ann. Appl. Prob. 13, 277303. CrossRefGoogle Scholar
[23] Rodolakis, G. (2013). Information theoretic cut-set bounds on the capacity of Poisson wireless networks. In 2013 IEEE International Symposium on Information Theory, IEEE, New York, pp. 14511455. CrossRefGoogle Scholar
[24] Schneider, R. and Weil, W. (2008). Stochastic and Integral Geometry. Springer, Berlin. CrossRefGoogle Scholar
[25] Takagi, H. and Kleinrock, L. (1984). Optimal transmission ranges for randomly distributed packet radio terminals. IEEE Trans. Commun. 32, 246257. CrossRefGoogle Scholar