Building Smart System by Applied Deep Learning and Spatial Indoor Agent Based Model for a New Adaptation University Learning Process Post Covid-19

Abstract

The impact of COVID-19 implied various restrictions on people’s mobility, especially for the higher education communities, by implementing the Learning from Home approach. This approach has altered the behavior of a human on a daily basis for a year long. Subsequently, the global vaccination program has been the advent of a “New Normal” approach as it reenables the direct human interactions by following health protocols to abide such as social distancing. This study investigated the pedestrian flow in the Department of Urban and Regional Planning (DURP) lecture building, Brawijaya University, and predicted the potential crowd spots using the Integrated Agent-Based Model (ABM), Computer Vision, and the Geographical Information System on an Indoor scale. Additionally, alternative designs of pedestrian flow were proposed to prevent crowds from occurring. The results showed the East and West entrance paths of the DURP building have high traffic, so the proper response is to organize the Southside door as an alternative entrance for pedestrian access. Moreover, the opening of the south gate could reduce the crowd spots on the 2nd Floor of the DURP lecture building.