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Rani Muhdi. Evacuation Modeling: An Overview Presentation. Outline:. Introduction The development of evacuation models Evacuation models Simulation examples and Life Safety Code ® Research opportunities. Introduction. Crowd Dynamics Pedestrians Traffic control Cities evacuation
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Rani Muhdi Evacuation Modeling:An Overview Presentation
Outline: • Introduction • The development of evacuation models • Evacuation models • Simulation examples and Life Safety Code® • Research opportunities
Introduction • Crowd Dynamics • Pedestrians • Traffic control • Cities evacuation • Natural disasters • Egress • Fire evacuation • Workplace violence
Introduction • How important to us?
Introduction • Buenos Aires Club • Some 4,000 fans at a night concert fought to reach the exits as burning debris fell on them. • The concert crowd was nearly three times the venue's capacity. • “Someone from the crowd tossed a flare and there were immediately flames.”
Buenos Aires Club • “Once the fire erupted, everyone ran for the doors, but there was only one, very narrow one open at the exit closest to us. Another wider door next to it was locked.” • Emergency exits at a nightclub packed with teenagers were padlocked or wired shut when a flare ignited the foam ceiling, sparking a blaze that killed 175 people and injured more than 700, at least 102 were in critical condition.
Stampede at Hindu Procession • About 200,000 Hindu worshippers gathered Tuesday afternoon along the narrow road leading up to the Mandhara Devi shrine. • A fire caused by a short circuit in a makeshift shop near the temple created panic among the pilgrims. • The stampede has killed at least 300people in western India, according to officials. Most of those who died were women and small children.
The development of evacuation models • Sources of data • Videotaped observations • How long it takes to evacuate • Record movement • Characteristics of individuals • Lab experiments • Restrictions and ethical issues • NIOSH • Reality!
The development of evacuation models • Post-incident survey and interviews • Survivors of actual fire • Recollections and descriptions are subjective • Elapsed times are not recorded objectively • Details can be lost overtime • Evacuation models • Key tools for the evaluation of engineered designs. • Features and capabilities • Modeling methods and structures • Limitations
Evacuation models • Features and capabilities: • Purpose • Availability for public use • Occupant movement • Occupant behavior • Use of fire data • Output • Use of CAD drawings • Special features
Evacuation models • Modeling methods and structures: • Flow-based modeling • Cellular automata • Agent-based modeling
Cellular automata • Discrete space into cells • Individuals on a grid • Series of time-steps • Tracking the movement of individuals • Lack of social behavior
Agent-based modeling • Attributes assigned to each individual • Rational agent to assess optimal escape route • Social force model • Exodus-FSEG
The social force model • Newton’s Second Law • Repulsive interaction force • Interactions with the walls
Simulation Models • No Panic • Normal desired velocity vo = 1 m/s. • Regular and efficient outflow related to good coordination in leaving. 200 people leaving without panic • Panic • High desired velocity vo = 5 m/s. • Irregular and inefficient outflow due to arching and clogging at the bottleneck (door). 1000 people leaving with panic
Dwindling Available Space due to a Fire Front Pedestrians have a normal desired velocity v0 = 1 m/s, but flee the fire which propagates linearly from left to right with velocity V (red). The fire front is modeled analogously to the psychological repulsion effect by walls, but its strength A and interaction range B are assumed 10 times stronger. When the fire comes closer, people press stronger towards the exit. As a consequence, the outflow becomes disorientated and irregular. 200 people try to escape a fire Simulation Models
Simulation Models • INDIVIDUALISM vs. HERDING • People are searching for an exit they can't see because of smoke (gray), the desired velocity of individuals is v0 = 5 m/s. • Pedestrians recognize the door and the walls from a distance of 2m, while the range of pedestrian-pedestrian interactions is assumed to be 5m. • For pure individualism (unrealistic), people find an exit only by chance. • For strong herding, people follow the mass which may move into the wrong direction. • Most efficient is a mixture of individualism and herding, for which small groups are formed: Successful strategies that individuals found by chance are imitated by a reasonable number of people. • 1 : Individualism (p=0.01) • No interaction except for collision avoidance. • 2 : Mixture of individualism and herding (p=0.4) • Small groups follow "leaders" who found the exit by chance. • 3 : Herding (p=0.8) • Everyone follows the mass, even if it makes fatal mistakes.
Research opportunities • Movement capabilities of a wide cross-section of society • Evacuation of disabled people • Delay times before beginning evacuation • Behaviors • who decides to stay and who decides to go? • what is the basis for exit choice? • how can we predict stopping and turning back behaviors? • who queues and who doesn't? • Perception of risk • what factors impact perception of risk? • how does risk perception impact judgment? • Interaction between people • how do the presence of social groups impact evacuation delays and movement?
Research Opportunities: • Number and location of exits • External disasters and facility layout design • Layout design of machines, transportation and material handling, raw material, and work in process have direct effect on human behavior in evacuation • Workplace violence and layout design • Reliability of exit systems in a facility • Average weight up 25 # since 1960s • Implementation of the Life Safety Code®
