Simulating People Flow at an Airport : Case study: Arlanda Airport

Abstract: Companies that manage large numbers of people in public spaces, such as airports, would benefit from having the ability to accurately predict people-flow in their facilities. However, creating high-performance crowd-simulations in a context with continually changing time-tables and gate locations is a complex problem. In this thesis we propose a simulation system that handles a large number of simulated agents whose behavior is based on scheduled flight data. The system allows for the visualization of people flows and congestion, as well as the export of statistics to benchmark against a real machine operated counting system. Our solution combines modern game development technologies for controlling ambient characters and visualizing the environment, with traditional agent-based modeling methods. The simulation spawns human-like agents in the environment based on real (live) flight schedules and normally distributed behaviors. The system was applied at Arlanda Airport, the largest airport in Sweden, which is owned and operated by Swedavia AB. Swedavia has provided us with knowledge about their processes as well as given access to data sources with live information about flight departures from the airport. The result indicates that modern game engines, such as Unreal Engine, have the potential of being a convenient development environment for scalable crowd simulation systems. The prototype developed for this project is able to simulate all departing travelers at Arlanda Terminal 5 during a given day. The data set used for this project is based on historic flights from April to May 2019. With many optimizations left outside of scope for this project, the system has a capacity of speeding up the simulation run-time by a maximum factor of 20. The historical flight data used for evaluating the model lacks information which causes the prototype to consistently over estimate the number of agents to simulate. Yet, the prototype has an average accuracy of 79.4% when it comes to predicting the flow of people passing through security at Terminal 5.The conclusion from this project is that, it is possible to develop simulation tools using modern game development technologies that are useful for stakeholders managing travelers at airports. With that said, several optimizations have been identified that would potentially improve the prediction accuracy, the stability, and the usability of the software. These optimizations should be considered before deploying and relying upon this kind of system in an airport for real.