An Agent-Based Model Predicting Group Emotion and Misbehaviours in Stranded Passengers
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Airline passengers can get stranded in an airport due to a number of reasons. As a consequence, they might get frustrated. Frustration leads to misbehaving if a given individual is frustrated enough, according to the literature. In this work, an agent-based model of stranded passengers in an airport departure area is presented. Structured simulations show how personal and environmental characteristics such as age, gender and emotional contagion, among others, influence the frustration dynamics, number and type of misbehaviours in such a scenario. We also present simulation results with two implemented support models (a chatbot and multilingual staff) aiming to reduce the overall frustration level of passengers facing this type of situation. Important findings are that: men are more likely to use force than women, the crowd composition plays an important role in terms of misbehaviours, the effect of emotional contagion leads to more misbehaviours and a chatbot might be considered as an alternative for supporting stranded passengers.
KeywordsComputational modelling Multi-agent based modelling Emotional contagion Misbehaviour prediction Crime prevention Chatbots
This research was undertaken as part of the EU HORIZON 2020 Project IMPACT (GA 653383) and Science without Borders – CNPq (scholarship reference: 235134/2014-7). We would like to thank our Consortium Partners and stakeholders for their input as well as the Brazilian Government.
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