Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/120411
Title: Study of different seat allocation strategies to reduce the risk of contagion among passengers in a public road transport system
Authors: Cristóbal, Teresa
Quesada-Arencibia, Alexis 
De Blasio, Gabriele Salvatore 
Padrón, Gabino 
Alayón, Francisco 
García, Carmelo R. 
UNESCO Clasification: 120304 Inteligencia artificial
3327 Tecnología de los sistemas de transporte
Keywords: Close contact
Covid-19
Data mining
Intelligent transport systems
Issue Date: 2023
Publisher: Springer 
Project: COVID19-03
Journal: Lecture Notes in Networks and Systems 
Conference: 14th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2022)
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.
URI: http://hdl.handle.net/10553/120411
ISBN: 978-3-031-21332-8
ISSN: 2367-3370
DOI: 10.1007/978-3-031-21333-5_21
Source: Lecture Notes in Networks and Systems [ISSN 2367-3370], v. 594 LNNS, p. 209-220, (Enero 2023)
Appears in Collections:Actas de congresos
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