Open Access
June 2011 Geodesics and flows in a Poissonian city
Wilfrid S. Kendall
Ann. Appl. Probab. 21(3): 801-842 (June 2011). DOI: 10.1214/10-AAP724

Abstract

The stationary isotropic Poisson line process was used to derive upper bounds on mean excess network geodesic length in Aldous and Kendall [Adv. in Appl. Probab. 40 (2008) 1–21]. The current paper presents a study of the geometry and fluctuations of near-geodesics in the network generated by the line process. The notion of a “Poissonian city” is introduced, in which connections between pairs of nodes are made using simple “no-overshoot” paths based on the Poisson line process. Asymptotics for geometric features and random variation in length are computed for such near-geodesic paths; it is shown that they traverse the network with an order of efficiency comparable to that of true network geodesics. Mean characteristics and limiting behavior at the center are computed for a natural network flow. Comparisons are drawn with similar network flows in a city based on a comparable rectilinear grid. A concluding section discusses several open problems.

Citation

Download Citation

Wilfrid S. Kendall. "Geodesics and flows in a Poissonian city." Ann. Appl. Probab. 21 (3) 801 - 842, June 2011. https://doi.org/10.1214/10-AAP724

Information

Published: June 2011
First available in Project Euclid: 2 June 2011

zbMATH: 1226.60014
MathSciNet: MR2830605
Digital Object Identifier: 10.1214/10-AAP724

Subjects:
Primary: 60D05 , 90B15

Keywords: Dufresne integral , frustrated optimization , geometric spanner network , Growth process , improper anisotropic Poisson line process , Lamperti transformation , Laplace exponent , Lévy process , logarithmic excess , Manhattan city network , mark distribution , martingale central limit theorem , Mills ratio , network geodesic , Palm distribution , perpetuity , Poisson line process , Poissonian city network , Slivynak theorem , spanner , spatial network , subordinator , traffic flow , uniform integrability

Rights: Copyright © 2011 Institute of Mathematical Statistics

Vol.21 • No. 3 • June 2011
Back to Top