Article
Version 2
This version is not peer-reviewed
Tourist Route Optimization in the Context of COVID-19 Pandemic
Version 1
: Received: 15 April 2021 / Approved: 19 April 2021 / Online: 19 April 2021 (12:26:19 CEST)
Version 2 : Received: 5 May 2021 / Approved: 7 May 2021 / Online: 7 May 2021 (09:00:17 CEST)
Version 2 : Received: 5 May 2021 / Approved: 7 May 2021 / Online: 7 May 2021 (09:00:17 CEST)
A peer-reviewed article of this Preprint also exists.
Păcurar, C.M.; Albu, R.-G.; Păcurar, V.D. Tourist Route Optimization in the Context of Covid-19 Pandemic. Sustainability 2021, 13, 5492. Păcurar, C.M.; Albu, R.-G.; Păcurar, V.D. Tourist Route Optimization in the Context of Covid-19 Pandemic. Sustainability 2021, 13, 5492.
Abstract
he paper presents an innovative method for tourist route planning inside a destination. The necessity of reorganizing the tourist routes within a destination comes as an immediate response to the Covid-19 crisis. The implementation of the method inside tourist destinations can bring an important advantage in transforming a destination into a safer destination in times of Covid-19 and post-Covid-19. The existing trend of shortening the tourist stay length has been accelerated while the epidemic became a pandemic. Moreover, the wariness for future pandemics has brought into spotlight the issue of overcrowded attractions inside a destination at certain moments. The method presented in this paper proposes a backtracking algorithm, more precisely an adaptation of the travelling salesman problem. The method presented is aimed to facilitate the navigation inside a destination and to revive certain less-visited sightseeing spots inside a destination while facilitating conformation with the social distancing measures imposed for Covid-19 control.
Keywords
Covid-19; social distancing; route planning inside a destination; urban tourism; backtracking algorithm; Brașov, sustainable development; tourist route optimization
Subject
Social Sciences, Tourism, Leisure, Sport and Hospitality
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Commenter: Cristina Maria Pacurar
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