Shortest Path Distance in Manhattan Poisson Line Cox Process

Vishnu Vardan Chetlur*, Harpreet S. Dhillon, Carl P Dettmann

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

10 Citations (Scopus)
45 Downloads (Pure)


While the Euclidean distance characteristics of the Poisson line Cox process (PLCP) have been investigated in the literature, the analytical characterization of the path distances is still an open problem. In this paper, we solve this problem for the stationary Manhattan Poisson line Cox process (MPLCP), which is a variant of the PLCP. Specifically, we derive the exact cumulative distribution function (CDF) for the length of the shortest path to the nearest point of the MPLCP in the sense of path distance measured from two reference points:
(i) the typical intersection of the Manhattan Poisson line process (MPLP), and
(ii) the typical point of the MPLCP. We also discuss the application of these results in infrastructure planning, wireless communication, and transportation networks.
Original languageEnglish
Pages (from-to)2109-2130
Number of pages22
JournalJournal of Statistical Physics
Publication statusPublished - 21 Oct 2020


  • Stochastic geometry
  • Manhattan
  • Poisson line process
  • Manhattan Poisson line Cox Process
  • Path distance
  • Shortest path


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