The viral transmission in public places and transportations can be minimized by following the world health organization (WHO) guideline. However, the uncertainty in a dynamic system complicates the social engagement to the physical distancing regulation. This study aims to overcome this obstacle in MRT stations and train by developing an adaptive queue line system. The system was developed using low-cost hardware and open-source software to guide passengers using visual information. The system works by capturing seat images and identify the presence of humans using a cloud machine learning service. The physical representation of MRT was translated to data representation using the internet of things (IoT). The data then streamed using an asynchronous API with a representative endpoint. The endpoint is then accessed by a display computer in the destination station platform to provide visual information. The visual information was ergonomically designed with visual display principles, including the minimum content load, layout, color combination, and dimension of contents. The design of the system was evaluated by Markov simulation of virus transmission in train and usability testing of the visual design. The implementation of the system has balanced the queue line capacity in station and crowd spots distribution in MRT. The system was effective due to the visual cortex manipulation by visual information. Consequently, the aerosol and falling droplets' viral transmission radius can be reduced. Accordingly, the chance for airborne transmission can be lowered. Therefore, the adaptive queue line system is a non-pharmaceutical intervention of viral transmission diseases in public transportation
Implementation of non-pharmaceutical intervention of COVID-19 in MRT through engineering controlled queue line using participatory ergonomics approach