Modelling gap-size distribution of parked cars using random-matrix theory
arXiv:physics/0510136 · doi:10.1016/j.physa.2005.10.059
Abstract
We apply the random-matrix theory to the car-parking problem. For this purpose, we adopt a Coulomb gas model that associates the coordinates of the gas particles with the eigenvalues of a random matrix. The nature of interaction between the particles is consistent with the tendency of the drivers to park their cars near to each other and in the same time keep a distance sufficient for manoeuvring. We show that the recently measured gap-size distribution of parked cars in a number of roads in central London is well represented by the spacing distribution of a Gaussian unitary ensemble.
7 pages, 1 figure
References in corpus (2)
Cited by in corpus (17)
- Capture-zone scaling in island nucleation: phenomenological theory of an example of universal fluctuation behavior
- Modelling highway-traffic headway distributions using superstatistics
- Coulomb and Riesz gases: The known and the unknown
- Universality for mathematical and physical systems
- Nearest-neigbor spacing distributions of the beta-Hermite ensemble of random matrices
- A Markov Process Inspired Cellular Automata Model of Road Traffic
- Inter-vehicle gap statistics on signal-controlled crossroads
- Voronoi Cell Patterns: theoretical model and applications
- Local universality of repulsive particle systems and random matrices
- Moment analysis of highway-traffic clearance distribution
- Modeling Spacing Distribution of Queuing Vehicles in Front of a Signalized Junction Using Random-Matrix Theory
- Gaussian Determinantal Processes: a new model for directionality in data
- Territorial behaviour of buzzards versus random matrix spacing distributions
- The Dyson and Coulomb games
- Circular unitary ensemble with highly oscillatory potential
- Stochastic and Quantum Dynamics of Repulsive Particles: from Random Matrix Theory to Trapped Fermions
- Random matrix statistics and safety rest areas on interstates in the United States