Passenger Spatiotemporal Distribution Prediction in Airport Terminals Based on Physics-Guided Spatio-Temporal Graph Convolutional Network and Its Effect on Indoor Environment Prediction

The airport as an important transportation hub plays a leading role in promoting sustainable cities and new-type urbanization. To boost safe, environmental-friendly and technologically advanced airports, the passenger travel behavior as a core that decides the resource allocation, system tuning and capacity dispatching, must be grasped. Previous research in passenger distribution prediction focused on physics-based methods or only mining temporal dynamics. In this work, a refined passenger distribution prediction was modeled based on a learning-based method embedding physical prior knowledge, and then its effects on indoor environment prediction were analyzed. Among them, based on insect intelligent building architecture, a virtual spatial graph was defined in Guangzhou Baiyun International Airport Terminal 2, then a Wi-Fi positioning system was constructed; Next, a physics-guided spatio-temporal graph convolutional network, considering both the spatial dependencies and the passenger arrival pattern extracted from cost-free flight schedules, was developed for domestic and international passenger distribution predictions with R2 over 0.87 and 0.76 respectively; Lastly, the contributions of predicted occupant densities to the indoor environment prediction were evaluated with results showing that the average R2 for indoor temperature, relative humidity and CO2 concentration prediction was enhanced by 0.4%∼91.5%, 0.2%∼29.7% and 0.4%∼45.4% respectively as the prediction horizon broadening.