Development of a computer-aided model for reliable terminal evacuation simulation – a statistical approach to handle unpredictable passenger behavior - ICRAT Dipl.-Wirtsch.-Ing. Michael Schultz Dipl.-Ing. Susann Lehmann Prof. Dr.-Ing. habil. Hartmut Fricke Zilina,

2 A statistical approach to handle unpredictable passenger behaviour M. Schultz, S. Lehmann, Prof. H. Fricke Structure Of Presentation 1. Institute Of Aviation 2. Motivation 3. Motion Behavior Of People 4. Cellular Automaton Approach 5. Model Details 6. Conclusion And Perspective

3 A statistical approach to handle unpredictable passenger behaviour M. Schultz, S. Lehmann, Prof. H. Fricke 1. Institute Of Aviation director of the institute: Professor Hartmut Fricke young team (9 assistants) of aviation experts and engineers key aspects of research: –Air Transport Infrastructure Planning –Air Transport System Technologies –Optimizing Ground Handling and Passenger Flow Processes –Capacity Analysis: Correlating Capacity and Safety

4 A statistical approach to handle unpredictable passenger behaviour M. Schultz, S. Lehmann, Prof. H. Fricke 2. Motivation airport terminal –complex infrastructure ( passenger dispatch vs. leisure) –high passenger frequency, capacity often nearly saturated –accentuation of security conditions –highest security/safety standards in transportation  terminal as a reference for granting security in buildings unpredictable human behavior in emergency cases identification of bottlenecks and comparison of possible evacuation strategies proposed security assessment systematic to validate strategies

5 A statistical approach to handle unpredictable passenger behaviour M. Schultz, S. Lehmann, Prof. H. Fricke 3. Motion Behavior Of People - In Emergency Cases - The motion pattern of people in emergency cases differ heavily from normal, well-known motion patterns. An experiment in a Japanese supermarket shows the motion behavior under stress conditions [1] : –46,7 %uses the information of warning and information signs and follows properly staff instructions –26,3 % move away from impact zone and intend to leave the consequence area –16,7 % use the next reachable exit –3 % follow other persons –3 % avoid gathering –2,3 % prefer the "brightest" exit –1,7 % choose arbitrarily any door to escape [1] Abe "Human Science of Panic", Brain Pub. Co., Tokyo, 1986

6 A statistical approach to handle unpredictable passenger behaviour M. Schultz, S. Lehmann, Prof. H. Fricke 3. Motion Behavior Of People - In Emergency Cases - classification of escape behavior according to SCHNEIDER [2] –approx %act rational and are able to lead other persons out of the hazard area –approx. 70 %are astonished and composed, they can be led by clear instructions –approx %act unpredictable, do freeze or start to stampede [2] Schneider "Evakuierung bei Brandereignissen", lecture at Technische Akademie Esslingen, Institute for Building Materials, Building Physics, and Fire Protection, Vienna University of Technology, 2004

7 A statistical approach to handle unpredictable passenger behaviour M. Schultz, S. Lehmann, Prof. H. Fricke 4. Cellular Automaton Approach microscopic model (simulation of individuals) two dimensional spatial, time and state discrete regular lattice with Moore neighbourhood relationship one cell has two states  empty, occupied

8 A statistical approach to handle unpredictable passenger behaviour M. Schultz, S. Lehmann, Prof. H. Fricke 4. Cellular Automaton Approach - model description - one person per cell  dimension 40 cm x 40 cm person moves one cell per time step (single speed model) max. walking speed according to WEIDMANN [3] v max = 1,34 ms -1 renunciation of acceleration, persons reach v max within 0,5 s [4] time step t  0,3 s [3] Weidmann "Transporttechnik der Fußgänger", Schriftenreihe des IVT, 90, Zürich, 1992 [4] Henderson "The Statistics of Crowd Fluids", p381, Nature 229, 1971

9 A statistical approach to handle unpredictable passenger behaviour M. Schultz, S. Lehmann, Prof. H. Fricke independency of longitudinal p und transversal q motion components probability distribution by variance  , mean value  and probability explicit probability is given by 4. Cellular Automaton Approach - statistical model - M M 0 -1 M 1 -1 M 0 -1 M 0 0 M 0 +1 M 1 -1 M 0 1 M 1 1 pipi qjqj

10 A statistical approach to handle unpredictable passenger behaviour M. Schultz, S. Lehmann, Prof. H. Fricke variance  p  and mean value  p of longitudinal component p variance  q  and mean value  q of transversal component q 4. Cellular Automaton Approach - statistical model -

11 A statistical approach to handle unpredictable passenger behaviour M. Schultz, S. Lehmann, Prof. H. Fricke overlapping of –spatial discrete cellular automaton  statistical model –spatial discretized continuous model  potential theory statistical model –changing of person motion behavior parameter  velocity, purposefulness –position of obstacles –line of sight potential model –changing of person motion behavior parameter  repulsion, attraction effects due to signs, marks, walls  repulsion, attraction effects due to persons, traces 5. Model Details

12 A statistical approach to handle unpredictable passenger behaviour M. Schultz, S. Lehmann, Prof. H. Fricke using a layer structure to describe scenarios, where each layer contains specific information example for layers: building structure obstacles and barriers guidance, evacuation system person traces (active walker) 5. Model Details - layer model - 1. layer 2. layer 3. layer 4. layer n. layer

13 A statistical approach to handle unpredictable passenger behaviour M. Schultz, S. Lehmann, Prof. H. Fricke tests of different evacuation strategies identification of safety parameters in addition to walking range and evacuation time recommendations for evacuation strategies (rescue management) 6. Conclusion And Perspective

14 A statistical approach to handle unpredictable passenger behaviour M. Schultz, S. Lehmann, Prof. H. Fricke Thank you for your attention! Contact: