A Computer Vision-Based Multidimensional Data Modelling Study of the Psychological Responses of Metro Car Colour Lighting Design on Passengers

• Published in: Journal of Combinatorial Mathematics and Combinatorial Computing
• Main Author: Wang Y.; Samsudin M.R.; Daud N.
• Format: Article
• Language: English
• Published: Charles Babbage Research Centre 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214685944&doi=10.61091%2fjcmcc123-39&partnerID=40&md5=b7f21d99fe5324fac47342e555e9e4a6

With the rapid urbanization and expansion of subway rail transit, the subway has become an essential mode of public transportation. This study explores the impact of subway car color design on passengers' psychological responses. Utilizing computer vision technology and a pruning algorithm, a target detection model for passenger expression recognition was developed, serving as an intuitive measure of psychological reactions. An optimized expression feature extraction network was constructed for facial expression recognition, while a multidimensional data analysis model, based on data mining, provided comprehensive insights. The study reveals that green, red, and yellow lighting evoke positive psychological responses, whereas blue and purple induce calmer or more somber reactions. These findings offer valuable guidance for urban subway carriage color lighting design, enhancing passenger experience. © 2024 The Author(s).