Departure passenger arrival probability extraction and spatiotemporal distribution prediction in airport terminals

Studying the spatiotemporal distribution characteristics of terminal passenger flow and introducing them into the regulation process of the environmental control system in real time is of great significance to improving the level of system regulation. This study takes Terminal T2 of Guangzhou Baiyun International Airport as an example. The terminal space units are divided. According to flight and security information, the domestic and international departure passenger arrival probabilities during the epidemic and normal periods are extracted, and it is found that they are well fitted with the chi-square distribution and are independent of the number of passengers on the flight. A spatiotemporal distribution prediction model for departure passenger flow is established based on Euler’s method. Compared with Wi-Fi positioning data, the domestic and international passenger densities are predicted with correlation index R2above 0.74 and 0.67, respectively.