Modelling the spread of infectious diseases in public transport systems under varying demand patterns and capacity constraints
Year: 2025
Type: article
Abstract: Understanding the dynamics of passenger interactions and their epidemiological impact across public transportation systems is crucial for both service efficiency and public health. High passenger density and close physical proximity have been shown to accelerate the spread of infectious diseases. During the COVID-19 pandemic, many public transportation companies took measures to slow down and minimize the spread of the disease. One of these measures was introducing spacing and capacity constraints on public transit vehicles. Our objective is to explore the effects of changes in demand and transportation measures from an epidemiological point of view, offering alternative measures to public transportation companies to keep the system operational while minimizing the epidemiological risk as much as possible. Our findings show that restricting vehicle capacity can significantly reduce the spread of infections, while demand-related measures have an even stronger effect. Combining these approaches offers the best solutions for balancing public health and operability.
Source: Scientific Reports
Authors: László Hajdu, Jovan Pavlović, Miklós Krész, András Bóta
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Awards: Empowering climate-smart, circular, and zero-waste use of underutilized wood from the forest and building stock in the construction sector to support the New European Bauhaus, G3920267840, Reverse supply chain of residual wood biomass, G6310114082, Renewable materials and healthy environments research and innovation centre of excellence, G8886470378
OpenAlex ID: W4413257829