Simulation Analysis on Flight Delay Propagation Under Different Network Configurations

Haoyu Zhang,Shengrun Zhang,Weiwei Wu,Frank Witlox

doi:10.1109/access.2020.2999098

Abstract

This paper investigates flight delay propagation in air transportation networks (ATNs) by considering both network structures and airport operation performance. An airport susceptible-infected-recovered (ASIR) model is established based on the mechanism of epidemic spreading, where the focus is on the impact of the infection rate in order to properly map and understand the probability of delay propagation. Different network configurations are abstracted under complex network theory, in which the ASIR model can be simulated upon. The simulation results show that the original airport traffic, airport connection and the level of airport turnaround services play important roles in influencing delay propagation in different airports. In addition, changes of network structure such as the emerging of secondary hubs can also influence the delay propagation.

Highlights

The global aviation industry has experienced an unprecedented growth in terms of supply and demand
As the development of air transportation networks (ATNs) needs a long time scale, airports are highly stable over time, in order to further explore the impact of different network configurations on the delay propagation, we examine the development results of three network structure under different time scale (i.e. 2007, 2012, 2017) (Fig.4)
SIMULATION RESULTS Drawing on the proposed airport susceptible-infectedrecovered (ASIR) model, this section investigates the impact of airport traffic, the level of airport turnaround services and network structures on the delay propagation

Summary

INTRODUCTION

The global aviation industry has experienced an unprecedented growth in terms of supply and demand. This paper apply the SIR model to the air network as Baspinar and Koyuncu [17] did, and redefines the probability of delay propagation from the perspective of network configuration such as airport connectivity (i.e. airport degree k) and factors that effect airport operation performance (e.g. airport annual passenger flow, airport turnaround service efficiency, etc.) to explore in what ways these factors may influence the flight delay propagation as the ATNs structure develops.

THE ASIR MODEL

SIMULATION RESULTS

CONCLUSION