A Study on the Translation of Exterior Design for Tourist Trains Based on Affective Engineering and AHP
Authors
Xinmiao Wang1, Qi Zhang1, *, Haoran Liang2
1Dalian Jiaotong University, Dalian, Liaoning, 116028, China
2Qingdao University, Qingdao, Shandong, 266071, China
*Corresponding author.
Email: 15882868@qq.com
Corresponding Author
Qi Zhang
Available Online 18 August 2026.
- DOI
- 10.2991/978-94-6239-737-8_58How to use a DOI?
- Keywords
- Kansei Image; Analytic Hierarchy Process (AHP); Shape Grammar; Tourism Sightseeing Train; Tianshui Culture
- Abstract
Purpose To promote standardized design for the exteriors of sightseeing trains. Methods This study uses Kansei image analysis and the Analytic Hierarchy Process (AHP) to establish a cultural feature database, and applies shape grammar to transform cultural features into design language. Results A design framework of “Kansei image extraction–cultural feature weighting–shape grammar translation–scheme evaluation” is proposed, providing a systematic approach for regional culture-oriented tourism sightseeing train exterior design.
- Copyright
- © 2026 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Xinmiao Wang AU - Qi Zhang AU - Haoran Liang PY - 2026 DA - 2026/08/18 TI - A Study on the Translation of Exterior Design for Tourist Trains Based on Affective Engineering and AHP BT - Proceedings of the 2026 5th International Conference on Art Design and Digital Technology (ADDT 2026) PB - Atlantis Press SP - 475 EP - 484 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6239-737-8_58 DO - 10.2991/978-94-6239-737-8_58 ID - Wang2026 ER -