EXAMENSARBETE INOM DATATEKNIK, GRUNDNIVÅ, 15 HP STOCKHOLM, SVERIGE 2020 Simulating People Flow at an Airport Case study: Arlanda Airport LINUS BEIN FAHLANDER MELKER MOSSBERG KTH SKOLAN FÖR ELEKTROTEKNIK OCH DATAVETENSKAP Authors Linus Bein Fahlander, Melker Mossberg University KTH Royal Institute of Technology Supervisor Anders Sjögren Examinor Fadil Galjic Bachelor Thesis Degree Project in Computer Engineering, First Cycle Faculty Electrical Engineering and Computer Science University KTH Royal Institute of Technology Partner Swedavia AB 2 Abstract Companies that manage large numbers of people in public spaces, such as air- ports, would benefit from having the ability to accurately predict people-flow in their facilities. However, creating high-performance crowd-simulations in a con- text with continually changing time-tables and gate locations is a complex prob- lem. 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 simu- late 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 histori- cal 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 stake- holders managing travelers at airports. With that said, several optimizations have been identified that would potentially improve the prediction accuracy, the stabil- ity, 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. 3 Abstract Företag som ansvarar för offentliga miljöer kan dra nytta av att förutspå flöden av människor. Dessvärre är det inte trivialt att skapa mjukvara som med hög nog- granhet simulerar stora folkmassor. I detta projekt utvecklade och utvärderade vi en lösning på problemet att simulera ett stort antal människor, vars beteende baseras på dynamisk data. Lösningen visualiserar flödet av personer i en virtuell miljö, samt sparar statistik för att kunna jämföras mot verkliga observationer. Vår lösning kombinerar spelutvecklingsverktyg med agentbaserade simulations- strategier för att visualisera personmodeller som rör sig genom miljön. Personmodellerna generas i simulationen baserat på dynamisk data från realtids- system. Lösningen tillämpades på Arlanda flygplats, vilket är den största flygplatsen i Sverige, som ägs av Swedavia AB. Swedavia har som samarbetspartner försett oss med information om deras processer samt tillgång till deras realtidssystem som ger information om avgångar från flygplatsen. Resultatet av projektet är en lösning som visar stor potential för användandet av spelmotorer som utvecklingsmiljö för denna typ av simuleringar. Den utveck- lade prototypen är byggd i spelmotorn Unreal Engine och kan simulera alla utre- sande resenärer som rör sig genom Terminal 5 på Arlanda under en given dag. Underlaget som används för att utvärdera prototypen var historisk data från april till maj 2019. Lösningen har kapacitet att simulera upp till 20 gånger snabbare än verkligheten. Den historiska flygdatan som användes för utvärderingen hade vissa brister, vilket påverkade resulatet på ett sätt som realtidsystem inte hade gjort. Detta bidrog till att lösningen konsekvent överestimerade antalet resande i mätningarna. Trots detta lyckades prototypen uppnå en genomsnittlig träffsäkerhet på 79.4% för tidpunkten då resenärerna anländande till säkerhetskontrollen på Terminal 5. Slutsatsen av detta projekt är att det är möjligt att utveckla simuleringsverk- tyg med hjälp av spelmotorer som är värdefulla vid hantering av stora flöden av människor. Dock har prototypen som utvecklades under projektet inte tillräcklig träffsäkerhet eller tillförlitlighet för att användas som beslutsunderlag utan vidare utveckling. 4 Contents 1 Introduction 7 1.1 Background . 7 1.2 Hypothesis . 8 1.3 Purpose . 8 1.4 Case study requirements . 8 1.5 Limitations . 8 1.6 Outline . 9 2 Theoretical background 11 2.1 Simulating Crowds . 11 2.2 Simulation Software . 13 2.3 Swedavia API . 14 2.4 Cloud Functions . 14 2.5 Box-Muller Transformation . 15 2.6 Related Work . 15 3 Methodology 17 3.1 Methodology Theory . 17 3.2 Methodology Implementation . 18 3.3 Methodology limitations . 19 3.4 Breaking down the research hypothesis . 19 3.5 Prototype Case Study . 20 3.6 Interview . 20 3.7 Literary Study . 21 3.8 Airport observations . 21 3.9 Method for drawing conclusions . 21 4 Simulation prototype 23 4.1 Functionality . 23 4.2 Architecture . 26 4.3 Challenges . 33 5 Result 35 5.1 Prototype Case-Study . 35 5.2 Interview . 40 5.3 Literary study . 41 6 Discussion 43 6.1 Answers to the hypothesis sub questions . 43 6.2 Evaluating the Prototype and Case Study . 45 6.3 Method . 46 6.4 Future Development & Research . 47 7 Appendix 51 5 6 1 Introduction The total number of flights per person is steadily rising in Sweden [3], which means that more and more people are traveling through the major airports. This leads to a growing demand from airport managers to be able to make accurate predictions about the "people flow" in their facilities. People-flow can be defined as a quality measure of people’s movement in different environments. "Good people flow" infers low congestion, high passability and short queue times; parameters that are all highly important for any large airport to run its operations efficiently. [7] Unfortunate scenarios such as flight delays can result in sudden high densities of people that create disturbances if operators are not well prepared. If decision-makers at airports had access to people-flow predictions they could make optimizations in everything, from staffing and flight-scheduling to planning of the actual architecture of the airport. This would result in a smoother experience for both the visitors and the airport operators. [16] There are many other industries for whom such simulations could be beneficial. What is special about pedestrians at an airport is that their behavior is largely determined by the arrivals and departures their scheduled flights. However, if a tool was developed for predicting people-flow at an airport, then a similar setup could be used for other public spaces such as train stations or offices. 1.1 Background Simulations are generally considered to be one of the best support technologies for predicting complex systems that are dynamic and stochastic in nature. [13] How- ever, accurately simulating crowd behaviour with hundreds of autonomous agents is a complex problem. Especially in a constantly changing environment, such as an airport with gate-changes and delays. What "predicting people-flow" really means in this context is to simulate crowd movement ahead of time. Modeling behaviours of "crowds" of people is a task with many challenges such as: realistically modeling interactions between pedestrians, the collective motion of large scale crowds, obstacle avoidance as well as representing virtual humans in their environment. Creating a people-flow simulation tool is a task that would be very large and complex in scope if it was built from scratch. At the same time, modern "game engines" are making it more and more conve- nient to solve these problems. Not only do game engines provide environments for writing code and algorithms, but they also provide engines for rendering high- quality 3D graphics, physics and collision detection, animation, artificial intelligence, networking, streaming, memory management, threading, localization support, and more. All of this makes the challenge of scope mentioned earlier a lot more manage- able. 7 1.2 Hypothesis To explore the use of simulation-technology as a mean of predicting people flow at an airport, we propose the following research hypothesis: It is possible to aid decision-making at an airport by simulating people flow. 1.3 Purpose The objective of this thesis is to conduct a case study to answer the research question. The aim is to develop a scalable and accurate model of real-time pedestrian activity at an airport, Arlanda Airport in Sweden