Tóth's buses and the "detachment process''

TL;DR

This work extends Tóth's lonely-passengers problem by introducing the detachment process, a coupled, time-inhomogeneous Markov framework that treats the number of buses as the time parameter. It develops precise asymptotics for detachment times, provides a quadratic-scale scaling limit for detachment events, and analyzes concentration, clumping, and Poissonization effects, including a novel binomial comparison tool. Key contributions include explicit formulas for detachment probabilities, a fidis convergence theory with cadlag limits, and sharp phase transitions in the lonely-passenger counts across multiple time scales. Collectively, the results illuminate the dynamic structure of occupancy problems under time-varying conditions and offer tools and open questions for related stochastic-process models in combinatorial packing and transport contexts.

Abstract

This paper introduces the \textbf{detachment process}, a novel, time-inhomogeneous Markov process inspired by I. P. Tóth's problem \cite{Toth} concerning the number of ``lonely passengers'' (those without companions) when $n$ passengers are seated independently and uniformly in $k$ initially empty buses. Tóth showed that this number is stochastically non-decreasing in $k$ for fixed $n$ (see also Haslegrave's work \cite{Haslegrave}). We extend Tóth's model by treating the number of buses $k$ as a time parameter. Specifically, for a fixed number of passengers $n$, the state of our Markov process at time $k \ge 1$ is exactly Tóth's configuration $(n, k)$. (We formally extend the process definition for all $t \in [1, \infty)$.) These processes can be coupled for all $n \ge 1$, and this larger coupled process is what we dub the \textbf{detachment process}. Our investigation focuses on properties related to detachment, clumping, the number of lonely passengers and of non-empty buses. The central notion is \textbf{detachment}, which occurs at time $k$ if every passenger occupies a distinct bus; we say the process is \textbf{in a state of detachment} at $k$. A \textbf{detachment time} $k$ is when the process transitions from a non-detached state at $k-1$ to a detached state at $k$. \textbf{Four critical time scales} are idetified -- linear, quadratic, and log-corrected linear or quadratic in the number of passengers, $n$ -- that govern the process's properties. We investigate (relative) clumping. We also explore why modeling the number of passengers with a Poisson distribution simplifies the analysis of Tóth's original model. To aid this derivation, we introduce a comparison theorem for binomial distributions, originally obtained by J. Najnudel \cite{Najnudel}, along with a novel proof.

Figures (2)

• Figure 1: The density of the limiting IE(1) law
• Figure 2: De-clumping dynamics for $n=100$ balls; large drop already in linear time!

Theorems & Definitions (57)

• Definition 1.1: The $n$-detachment process
• Remark 1.2
• Definition 1.3
• Definition 1.4: Time of first and of permanent detachment
• Definition 1.5: State of detachment vs. detachment time
• Theorem 2.1: Tóth's bus theorem
• proof
• Theorem 2.2: Tóth
• proof
• Corollary 2.3: Zero percent detachment until $o(n^2)$