ABSTRACT
As a result of the COVID-19 pandemic, public transport systems suffered a significant reduction in passengers due to the suppression of services and reduced vehicle capacity. This reduction jeopardized their role as facilitators of sustainable mobility, causing large economic losses to public transport operators. Therefore, an intelligent management aimed at reducing the risk of contagion among its users is an aspect of interest for public transport operators and a challenge from a scientific point of view. This paper presents the results of a study aimed at analyzing the effect of different seat allocation strategies on the risk of contagion among passengers. Starting from a formalization of the problem based on epidemiological and public transport entities, the methodology employed, based on Data Mining, makes use of simulation processes to analyze the effect of these strategies. The paper presents the results obtained by analyzing a route of a public road passenger transport operator. The results allow us to evaluate the risk of contagion of different seat allocation strategies and to evaluate how this risk varies according to the number of passengers who have traveled on a vehicle journey. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The COVID-19 pandemic has had very negative effects on public transport systems. These effects have compromised the role they should play as enablers of social equity and environmentally sustainable mobility and have caused serious economic losses for public transport operators. For this reason, in the context of pandemics, meaningful epidemiological information gathered in the specific framework of these systems is of great interest. This article presents the findings of an investigation into the risk of transmission of a respiratory infectious disease in an intercity road transport system that carries millions of passengers annually. To achieve this objective, a data mining methodology was used to generate the data required to ascertain the level of risk. Using this methodology, the occupancy of vehicle seats by passengers was simulated using two different strategies. The first is an empirical approach to the behaviour of passengers when occupying a free seat and the second attempts to minimise the risk of contagion. For each of these strategies, the interactions with risk of infection between passengers were estimated, the patterns of these interactions on the different routes of the transport system were obtained using k-means clustering technique, and the impact of the strategies was analysed.