The Journal of
the Korean Institute of Interior Design

The Journal of
the Korean Institute of Interior Design

Bimonthly
  • ISSN : 1229-7992(Print)
  • ISSN : 2733-6832(Online)
  • KCI Accredited Journal

Editorial Office

  • Tel. +82-2-564-2598
  • Fax. +82-2-564-2599
  • E-mail. kiid@kiid.or.kr
Title Enhancing the QoE of Guided Heritage Tour by Route Scheduling Optimization
Authors 김성준(Kim, Seongjun) ; 장선영(Jang, Sun-Young) ; 김성아(Kim, Sung-Ah)
DOI http://dx.doi.org/10.14774/JKIID.2018.27.6.144
Page pp.144-155
ISSN 12297992
Keywords QoE ; Indoor Spatial Information ; Route ; Tour Guide System ; Scheduling
Abstract Thanks to the advances in artificial intelligence and media technology, heritage experience can be digitally augmented and highly personalized for each single user. It is essential to customize visitor's experience by interweaving the sequence of spatial experience to ensure the successful guided heritage tour. Intelligent routing algorithm is used for this purpose. However, the quality of experience(QoE) deteriorates due to the inefficient route and the congestion in the exhibition space. The purpose of this study is to optimize the visitor's QoE according to the route for architectural heritage generated from the tour guide system. For this purpose, a model optimizing the visitor's QoE was developed. In the QoE optimization model, 1) a schedule indicating the visitor's location of every moment according to the generated route is created, 2) QoE factors such as the congestion degree, the total length of the route, and the number using stairs are measured through the indoor spatial information described in IndoorGML and 3) the QoE optimization is performed using the genetic algorithm. In order to verify the QoE optimization model, it was applied to the case of Seokjojeon, a cultural heritage of modern architecture. The result of QoE optimization for 10 visitors shows that the congestion degree is enhanced by 50%, the total length of the route and the number using stairs are improved by 12% and 13% compared to the state before the optimization.