Agent Based Modeling and Simulation of Pedestrian Crowds In Panic Situations

  • Mohammed Alrashed

Student thesis: Master's Thesis


The increasing occurrence of panic stampedes during mass events has motivated studying the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. The lack of understanding of panic stampedes still causes hundreds of fatalities each year, not to mention the scarce methodical studies of panic behavior capable of envisaging such crowd dynamics. Under those circumstances, there are thousands of fatalities and twice that many of injuries every year caused be crowd stampede worldwide, despite the tremendous efforts of crowd control and massive numbers of safekeeping forces. Pedestrian crowd dynamics are generally predictable in high-density crowds where pedestrians cannot move freely and thus gives rise to self-propelling interactions between pedestrians. Although every pedestrian has personal preferences, the motion dynamics can be modeled as a social force in such crowds. These forces are representations of internal preferences and objectives to perform certain actions or movements. The corresponding forces can be controlled for each individual to represent a different variety of behaviors that can be associated with panic situations such as escaping danger, clustering, and pushing. In this thesis, we use an agent-based model of pedestrian behavior in panic situations to predict the collective human behavior in such crowd dynamics. The proposed simulations suggests a practical way to alleviate fatalities and minimize the evacuation time in panic situations. Moreover, we introduce contagious panic and pushing behavior, resulting in a more realistic crowd dynamics model. The proposed methodology describes the intensity and spread of panic for each individual as a function of distances between pedestrians.
Date of AwardNov 2016
Original languageEnglish (US)
Awarding Institution
  • Computer, Electrical and Mathematical Sciences and Engineering
SupervisorJeff Shamma (Supervisor)

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