Simulating Dynamical Features of Escape Panic

One of the most disastrous forms of collective human behaviour is the kind of crowd stampede induced by panic, often leading to fatalities as people are crushed or trampled. Sometimes this behaviour is triggered in life-threatening situations such as fires in crowded buildings; at other times, stampedes can arise from the rush for seats or seemingly without causes. Tragic examples within recent months include the panics in Harare, Zimbabwe, and at the Roskilde rock concert in Denmark. Although engineers are finding ways to alleviate the scale of such disasters, their frequency seems to be increasing with the number and size of mass events. Yet, systematic studies of panic behaviour, and quantitative theories capable of predicting such crowd dynamics, are rare. Here we show that simulations based on a model of pedestrian behaviour can provide valuable insights into the mechanisms of and preconditions for panic and jamming by incoordination. Our results suggest practical ways of minimising the harmful consequences of such events and the existence of an optimal escape strategy, corresponding to a suitable mixture of individualistic and collective behaviour.Comment: For related information see http://angel.elte.hu/~panic, http://www.helbing.org, http://angel.elte.hu/~fij, and http://angel.elte.hu/~vicse

The automatic generation of an efficient floor field for CA simulations

The Hermes project [1] demonstrated the usefulness of on site predictive simulations of probable evacuation scenarios for security personnel. However, the hardware needed was prohibitively expensive [2]. For use in crowd management, the software has to run on available computers. The CA methods, which are fast enough, have well known problems with treating corners and turns. The present paper shows how a standard CA method can be modified to produce a realistic movement of people around bends and obstacles by changing the standard floor field. This can be done adaptively allowing for the momentary situation using simple predictions for the immediate future. The approach has one or two tuning parameter that have an obvious meaning and can therefore be set correctly by people not familiar with the inner process of a CA simulation. With this, a high end laptop can simulate more than 100 000 persons faster than real time, which should be enough for most occasions. It is intended to integrate the method into the tool JuPedSim [23]