Beyond Classical Models: Statistical Physics Tools for the Analysis of Time Series in Modern Air Transport

TL;DR

This paper reviews how statistical physics tools can analyze air transport time series, offering insights into system microscale behaviors from macroscale data to improve efficiency and resilience.

Contribution

It introduces statistical physics concepts to air transport analysis and discusses their application, challenges, and future prospects in the field.

Findings

Statistical physics methods reveal microscale dynamics from coarse-grained data.

Application of these methods enhances understanding of air transport system behavior.

Identifies obstacles and future directions for adopting physics tools in air transport.

Abstract

Within the continuous endeavour of improving the efficiency and resilience of air transport, the trend of using concepts and metrics from statistical physics has recently gained momentum. This scientific discipline, which integrates elements from physics and statistics, aims at extracting knowledge about the microscale rules governing a (potentially complex) system when only its macroscale is observable. Translated to air transport, this entails extracting information about how individual operations are managed, by only studying coarse-grained information, e.g. average delays. We here review some fundamental concepts of statistical physics, and explore how these have been applied to the analysis of time series representing different aspects of the air transport system. In order to overcome the abstractness and complexity of some of these concepts, intuitive definitions and explanations are provided whenever possible. We further conclude by discussing the main obstacles towards a more widespread adoption of statistical physics in air transport, and sketch topics that we believe may be relevant in the future.