Carlos Rallo iv crowd dynamics

1
Carlos Rallo de la Cruz
M. Arch. UPM
M. Eng. in Fire safety. UC3M
Dipl. Project Management. PUC
PhD Candidate CERTEC. UPC
Contact
carlosrallo@gmail.com
+34 647865702
Madrid, Spain
www.rallodelacruz.com
www.arquitecturayfuego.es
www.linkedin.com/in/carlosrallo
Fire Safety impact in Building Design
01. Prescriptive Design 05/05/2017
02. Performance Based Design 05/05/2017
03. NFPA 101 Live Safety Code 12/05/2017
04. Fire Safety in complex Architecture designs 12/05/2017
05. CFD / Fire Dynamics Simulator 26/05/2017
06. Crowd Dynamics & Pedestrian Modeling 02/06/2017
Máster en Ingeniería de
Seguridad contra Incendios

2
Fire safety in the Built Enviroment - Máster en Ingeniería de Seguridad contra Incendios - Universidad de Alcalá de Henares - 05.2017
Prof. Carlos Rallo de la Cruz carlosrallo@gmail.com https://www.linkedin.com/in/carlosrallo/ www.rallodelacruz.com www.arquitecturayfuego.es
06. Crowd Dynamics & Pedestrian Modeling

3
Crowd Dynamics & Pedestrian Modeling
• About
• History
• Complexity
• Models
• Limitations
Practice
• FDS+Evac
• Pathfinder
• MassMotion
Schedule

4
Interiors
Bus, Train, Metro Facilities
Exteriors Public Buildings
Stadiums Airports
About Crowd Dynamics & Pedestrian Modeling
Objectives
1. Avoid dangerous situations
2. Ensure evacuation times and safety
3. Optimize normal operation and evacuation

5
Major crowd catastrophes
Love Parade in Duisburg, Germany
Photographer: Uwe Weber
Reference: Crowd Modeling and Simulation on High Performance
Architectures. Table 2.4.1 Major crowd catastrophes.
Albert Gutiérrez Millá – UAB - 2016
“In the last years has been a growing effort in solving the evacuation
safety problem. The research áreas interested on the problema vary from
engineering, computer science, psychology, architectrue or sociology,..”

6
Evacuation calculations => Part of performance-based analyses => Life safety provided in buildings.
‒ Hand calculations
Usually follow the equations given in the Emergency Movement Chapter of the Society of Fire Protection Engineers
(SFPE) Handbook to calculate mass flow evacuation from any height of building.
http://ws680.nist.gov/publication/get_pdf.cfm?pub_id=906951
Reference: A review of Building Evacuation Models 2º Edition. NIST Technical Note 1680
The calculation focuses mainly on
points of constriction throughout the
building (commonly the door to the
outside) and calculates the time for
the occupants to flow past these
points and to the outside.

7
Reference: A review of Building Evacuation Models 2º Edition. NIST Technical Note 1680
Evacuation calculations => Part of performance-based analyses => Life safety provided in buildings.
‒ Computer models
To achieve a more realistic evacuation calculation, engineers have been looking to evacuation computer models to
assess a building’s life safety.
‘Grid’ model analysis ‘Continuous’ model analysis

8
Evacuation modelling first two assumptions:
• Human behaviour during evacuation is rational
• Human behaviour during evacuation can be predicted
70s Crowd behave similar to fluids
80s Fist computer models for evacuation simulation
90s Equation-based models, Agent-Based Models
History of Crowd Dynamics & Pedestrian Modeling
http://www.springer.com/gp/book/9783319207070
Arturo Cuesta, Orlando Abreu, Daniel Alvear, Springer 2016,
Evacuation Modeling Trends

9
Pedestrians have been empirically studied for more than four decades.
The evaluation methods initially applied were based on direct
observation, photographs, and time-lapse films.
History of Crowd Dynamics & Pedestrian Modeling
Fruin Walkways
Fruin platform (Queuing) IATA Wait/Circulate
Fruin, J. J. (1971) Pedestrian planning and design. Level of Service (LOS).
https://www.researchgate.net/publication/226065087_Pedestrian_Crowd_and_Evacuation_Dynamics
Pedestrian, Crowd and Evacuation Dynamics. 2010
Dirk Helbing - ETH Zurich, Andrés Johansson – University of Bristol.
For a long time, the main goal of these studies was:
‒ To develop a level-of-service concept,
‒ Design elements of pedestrian facilities,
‒ Design planning guidelines.
Simulation Models
‒ e. g. queuing models,
‒ transition matrix models,
‒ stochastic models,
‒ Route choice behavior of pedestrians.
Crowds behave similar to gases or fluids.
Agent-based models of pedestrian crowds.
The “social force model”.
Cellular automata of pedestrian dynamics.
AI-based models.
Cooperation patterns
‒ Lane Formation
‒ Oscillatory Flows at Bottlenecks
‒ Stripe Formation in Intersecting Flows

10
Evacuation Dynamics
Crowd dynamics at high densities and under psychological stress.
Evacuation and Panic Research
Panic => Short-term personal interests uncontrolled by social and cultural constraints.
 reduced attention in situations of fear,
 options like side exits are mostly ignored.
 Social contagion,
 Transition from individual to mass psychology, in which individuals transfer control over their actions to others, leading to conformity.
 This “herding behavior” => irrational => dangerous overcrowding and slower escape.
Complexity. Crowd Dynamics & Pedestrian Modeling

11
http://vision.cse.psu.edu/courses/Tracking/vlpr12/HelbingSocialForceModel95.pdf
Helbing, D., Molnar, P., Social Force Model for Pedestrian Dynamics, Physical Review
E, Volume 51, Issue 5, pp4281-4286, 1995

12
Mass evacuation - human behavior and crowd dynamics - What do we know? - Markus Friberg, Michael Hjelm. Department of
Fire Safety Engineering Lund University, Sweden. 2014
http://lup.lub.lu.se/luur/download?func=downloadFile&recordOId=7766859&fileOId=7766990
Human behavior in fires & evacuations
3.1 Individual behavior
3.1.1 Uncertainty
3.1.2 Problem solving & decision making
3.1.3 Gender
3.1.4 Cultural
3.1.5 Age
3.1.6 Stress
3.1.7 Panic
3.1.8 Individual roles
3.2 Group behavior
3.2.1 Social influence
3.2.2 Social identity
3.3 Pre-movement
3.3.1 The evaluation process
3.3.2 Knowledge & Understanding
3.3.3 Fire alarms & designs
3.3.4 Denying the danger
3.4 Movement
3.4.1 Speed
3.4.2 Structural impacts

13
A review of Building Evacuation Models 2º Edition. NIST Technical Note 1680
Models for Crowd Dynamics & Pedestrian Modeling

14
Modeling Method:
(M): Movement model
(M-O): Movement/optimization models
(PB): Partial Behavioral model
(B): Behavioral model
(B-RA): Behavioral model with risk assessment capabilities
Purpose:
(1) Models that can simulate any type of building
(2) Models that specialize in residences
(3) Models that specialize in public transport stations
(4) Models that are capable of simulating low-rise buildings (under 15 stories)
(5) Models that only simulate 1-route/exit of the building.
Grid/Structure:
(C): Coarse network
(F): Fine network
(Co): Continuous Perspective of the model/occupant:
(G): Global perspective
(I): Individual perspective
Behavior:
(N): No behavior
(I): Implicit
(C): Conditional or rule-based
(AI): Artificial intelligence
(P): Probabilistic
Movement:
(D): Density
(UC): User’s choice
(ID): Inter-person distance
(P): Potential
(E): Emptiness of next grid cell
(C): Conditional
(Ac_K): Acquired knowledge
(Un_F): Unimpeded flow
(CA): Cellular automata
Fire Data:
(N): The model cannot incorporate fire data
(Y1): The model can import fire data from another model
(Y2): The model allows the user to input specific fire data at certain times
throughout the evacuation
(Y3): The model has its own simultaneous fire model
CAD:
(N): The model does not allow for importation of CAD drawings
(Y): The model does allow for importation of CAD drawings
Visual:
(N): The model does not have visualization capabilities
(2-D): 2-dimension visualization available
(3-D): 3-dimension visualization available
Validation:
(C): Validation against codes
(FD): Validation against fire drills or other people movement experiments/trials
(PE): Validation against literature on past experiments (flow rates, etc.)
(OM): Validation against other models
(3P): Third party validation
(N): No validation work could be found regarding the model

15
Trends (1º Edition 2005/2º Edition 2010)
Complexity of the evacuation models
More of the models are including behaviors and decision-making capabilities for the simulated occupants.
Complexity of the model grids
In the previous review, very few models incorporated a continuous grid network. In this review, the majority of the available models
simulate movement on a continuous grid.
Complexity of the modeling input
Incorporate fire effects into the simulation.
CAD
Complexity of the models’ output capabilities
3-D visualization
Notes:
‒ Users should be careful to ensure that the behavioral aspects of the model are supported by data and/or theory of human behavior during fires.
‒ The attributes and decisions of the occupants are often defined in a probabilistic fashion which requires multiple iterations of each simulation to
determine the range of expected occupant evacuation times and movement speeds.
‒ it is imperative to understand how the model has been validated and to find out if the capabilities are grounded in any evacuation or pedestrian
data sources and to understand the validity of that data to the desired scenario.

16
J. Averill, Five grand challenges in pedestrian and evacuation dynamics, in: Proceedings of Pedestrian and Evacuation Dynamics, Gaithersburg, MD, 2010
Limitations in Crowd Dynamics & Pedestrian Modeling
NIST 2010. Five grand challenges in pedestrian and evacuation dynamics.
‒ Grand Challenge #1: Develop and validate a comprehensive theory which predicts human behavior
during pedestrian or evacuation movement.
‒ Grand Challenge #2: Create a comprehensive database of actual emergency data
‒ Grand Challenge #3: Embrace variance (stochastic)
‒ Grand Challenge #4: Integrate results of evacuation models with fire models to enable accurate and
reliable performance-based design
‒ Grand Challenge #5: Embrace technology

17
https://www.youtube.com/watch?v=OMov1aMWscw&t=36s
Golaem software. Physics (for visualization only)
https://www.youtube.com/watch?v=daysCqmqd2Y
Madrid Arena 2012
https://www.youtube.com/watch?v=8SeYkvLTgKo
Love Parade 2010
https://www.youtube.com/watch?v=8y73-7lFBNE
Physics of real extrem crowds

18
Physics of real crowds

19
Delhi Metro peak hour
https://www.youtube.com/watch?v=_BtBaM-izwo
Physics of real crowds

20
11-M. Atocha 2004
https://www.youtube.com/watch?v=izZXTwsTbLU
Real people behave during evacuation

21
Santiago de Chile, Estación Pudahuel. 2012
https://www.youtube.com/watch?v=s6dqnv48JzI
Real people behave during evacuation (without panic)

22
https://www.youtube.com/watch?v=v6iTSAwGo1Y&oref=https%3A%2F%2Fwww.yo
utube.com%2Fwatch%3Fv%3Dv6iTSAwGo1Y&has_verified=1
Real people behave during evacuation

23
https://www.youtube.com/watch?v=BgpdmAtbhbE
Crowds waves

24
So, how we design with these limitations?
With prevention strategies.
Design the building/space for a Level of service C (for example).

25
Practice
FDS+Evac Pathfinder MassMotion

26
Practice – FDS + EVAC
http://virtual.vtt.fi/virtual/proj6/fdsevac/examples_fds6.html
Examples
http://virtual.vtt.fi/virtual/proj6/fdsevac/documents/FDS+Evac_textbased_homepage.txt
Software web
Author
http://www.vttresearch.com/
http://virtual.vtt.fi/virtual/proj6/fdsevac/documents/FDS+EVAC_Guide.pdf
Software User Manual
Download FDS + EVAC is fully embedded in Fire Dynamics Simulator (FDS)
https://pages.nist.gov/fds-smv/

27
Practice – FDS + EVAC – Key points
‒ Simulate human egress using the Fire Dynamics Simulator (FDS)
‒ Allows simultaneous (or not) simulation of fire and evacuation processes.
‒ Each evacuee is a separate entity, or an ’agent’, which has its own personal properties
and escape strategies.
‒ The movement of the agents is simulated using two-dimensional planes representing
the floors of buildings. (a continuous two dimensional space )
‒ The forces acting on the agents consist of both physical forces, such as contact forces
and psychological forces exerted by the environment and other agents.
‒ The model behind the movement algorithm is the social force model introduced by
Helbing’s group [17, 18, 19, 20]. A modification of the model to describe better the
shape of the human body was introduced by Langston et al. [21].
[17] Helbing, D., and Molnár, P., “Social force model for pedestrian dynamics”, Physical Review E 51:
4282–4286 (1995).
[18] Helbing, D., Farkas, I., and Vicsek, T., “Simulating dynamical features of escape panic”, Nature 407:
487–490 (2000).
[19] Helbing, D., Farkas, I., Molnár, P., and Vicsek,T., “Simulating of Pedestrian Crowds in Normal and
Evacuation Situations”, Pedestrian and Evacuation Dynamics, Schreckenberg, M. and Sharma, S.D.
(eds.), Springer, Berlin, 2002, pp. 21–58.
[20] Werner, T., and Helbing, D., “The social force pedestrian model applied to real life scenarios”,
Pedestrian and Evacuation Dynamics – Proceedings of the Seconnd International Conference,
University of Greenwich, London, 2003, pp. 17–26.
[21] Langston, P.A., Masling, R., and Asmar, B.N., “Crowd dynamics discrete element multi-circle
model”, Safety Science 44: 395–417 (2006).

28
‒ The evacuation geometry is described using two-dimensional planes that cut the fire
geometry at the heights representing best the floor geometries.
‒ Evacuation meshes have their own rectilinear meshes that need not coinside with the
fire meshes.
‒ Usually mesh cell sizes 0.25 m or larger
‒ Spaces, where agents are allowed to move, should be at least about 0.7 m wide.
‒ The evacuation geometry does not support time dependent geometries. But the user
can give time dependent information on the usability of the doors/exits.
‒ Limit: 10 000 agents are tried to place on the same evacuation mesh. The total number
of agents is not restricted by the programme.
‒ The initial density of agents cannot be much larger than 4 persons per square metre.
Because the initial positions of agents are generated randomly. If higher densities are
needed, then the option for ordered placements of the initial agents should be used.
‒ The gas phase concentrations of O2, CO2, and CO are used by default to calculate
Purser’s Fractional Effective Dose (FED) index, indicating the human incapacitation.
‒ The effects of some other gases, (NO, NO2, CN, HCl, HBr, HF, SO2, C3H4O, CH2O) are
also considered, if the user gives corresponding inputs.
‒ Smoke density is used both to slow down the walking speeds of the agents and to
‒ affect the exit selection algorithm of the agents.
‒ The effects of radiation and gas temperature on agents are not yet implemented in the
programme, so agents do not try to avoid a fire if the user does not explicitly define the
evacuation geometry to take this in to account.

29
‒ The evacuation part of the FDS+Evac is stochastic, i.e., it uses random numbers to
generate the initial positions and properties of the agents. For this reason, one should
always do a dozen or so egress simulations to see the variation of the results.
‒ Several egress calculation can be done per one fire simulation and the calculation of the
guiding door flow fields for evacuation movement need to be calculated only once for
each given geometry. Monte Carlo mode EVACUATION_MC_MODE.
‒ The present version of FDS+Evac does not fully support parallel CPU calculations for the
evacuation part.
‒ FDS+Evac is primarily a research tool for studying evacuation processes in buildings.
While it seems to produce similar egress flows as found in the literature (both
experimental and other simulation tools) it is not yet fully validated. Thus, its use as an
engineering tool needs further considerations. It is suggested that FDS+Evac is used
together with some other (well) validated egress programme/method to study
evacuation.

30
Practice – FDS + EVAC – Limitations
‒ Geometry Rectilinear numerical mesh
‒ The default model for stairs does not include the option for agents to turn back when
the smoke concentration becomes too high.
‒ Exit Route Selection: The exit door selection algorithm is still a relatively simple one.
‒ New social type behaviours are not yet validated.
‒ There is no feasible model for elevators in the current version of the FDS+Evac. There is
a really simple elevator model implemented in the programme, but it is mostly there for
illustrative purposes only.
‒ More than a couple of thousand agents in an evacuation mesh then the calculation will
take long time.
‒ The possible blocking effect of other agents is not considered in the current version of
the programme.

31
Practice – FDS + EVAC – Brief Theoretical Basis for the Evacuation Model
Where:
is the position of agent at time
is the force exerted on agent by the surroundings
is the mass
is a small random fluctuation force
The velocity of agent i is given by

32
‒ Force on the agent

33
‒ Exit Selection
Many prescriptive fire codes implicitly assume that the total exit width of buildings is used
in egress. Herding behavior, as well as people’s tendency to favor the familiar routes, may
easily lead to outcomes that contradict with these assumptions.
Three new agent types were introduced in FDS+Evac so now there are four
types: conservative type, active type, follower type, and herding type.
The agents observe the actions of the others and select the target exit through which the
evacuation is estimated to be the fastest. The evacuation time of each agent to each exit is
calculated from the distances to the exits and the congestion in front of the exits. The
estimated evacuation time is not the only criterion considered in the model; also the
visibility of the exits and the fire related conditions at the exits affect the decision, as well
as the familiarity with the different exits, which can be defined for each agent by the user.

34
‒ Model Validation

35
‒ Model Validation

36
The user needs just to define:
• The main evacuation mesh (usually one per building floor)
• Final exits (EXIT namelists)
• Internal door connections (DOOR and ENTR namelists) that move agents from one
mesh (floor) to some other mesh (floor)
• Inclines, stairs, staircases, etc (EVSS and STRS namelists)
• Agent properties and initial postions (PERS and EVAC namelists)
Let’s see some examples
01. Door Flow 02. Wide Stairs 03. Real project

37
Practice Example 01 : Door Flow, fds file
Let’s see fds input file & User Guide, Chapter 8. Setting up the Input File for FDS+Evac
Practice – FDS + EVAC – EXAMPLES - Input File

38
&MESH IJK=150,150,1, XB=0.0,15.0, 0.0,15.0, 1.45,1.55, EVACUATION=.TRUE.,
EVAC_HUMANS=.TRUE., EVAC_Z_OFFSET=1.5, ID= 'FF1stFloor’ /
&EXIT ID='TopExit', IOR= +2,
FYI= 'Comment line',
COUNT_ONLY=.FALSE. ,
XYZ= 7.5, 14.5, 1.50,
XB= 5.0,10.0, 15.0,15.0, 1.45,1.55 /

39
&PERS ID='Adult',
FYI='Male+Female diameter and velocity',
DEFAULT_PROPERTIES='Adult',
PRE_EVAC_DIST=0,PRE_MEAN=1.0,
DET_EVAC_DIST=0,DET_MEAN=0.0,
DENS_INIT=40.0,
HUMAN_SMOKE_HEIGHT=1.60,
OUTPUT_SPEED=.TRUE.,
OUTPUT_DENSITY=.TRUE.,
OUTPUT_TOTAL_FORCE=.TRUE.,
COLOR_METHOD=0, I_HERDING_TYPE=2, /
&EVAC ID='EvacAdult',
NUMBER_INITIAL_PERSONS= 100,
XB= 5.0, 10.0, 4.8, 9.8, 1.5,1.5,
AVATAR_COLOR= 'BLUE',
ANGLE= 90, AGENT_TYPE=2,
KNOWN_DOOR_NAMES='TopExit',
KNOWN_DOOR_PROBS=1.0,
PERS_ID='Adult' /

40
Next line could be used to plot the evacuation flow fields:
&SLCF PBZ=1.5, QUANTITY='VELOCITY', VECTOR=.TRUE., EVACUATION=.TRUE. /

41
Practice Example 02: Wide Stairs Example, fds file, version 2
The wide stairs (x=40m - 50m), stairs (EVSS) at the 1st floor mesh, door connection is
at x=40m
The stairs belong to the 1st floor, so we put an OBST where the is not enough space
in the z-direction.
&OBST XB=40.0,45.0, 10.0,15.0, 0.9,1.1, EVACUATION=.TRUE., OUTLINE=.TRUE. /
&DOOR ID='GF_2_Stairs', IOR=+1,
KEEP_XY=.TRUE.,
COLOR='PINK', EXIT_SIGN=.TRUE.,
TO_NODE= 'Stairs_2_Up',
XYZ=39.0, 12.5, 1.0,
XB= 40.0,40.0, 10.0,15.0, 0.9,1.1, /
&ENTR ID='Stairs_2_Up', IOR=+1,
COLOR='CYAN',
XB= 40.0,40.0, 10.0,15.0, 4.9,5.1, /
&EXIT ID='GroundExit', IOR=+2,
COLOR='YELLOW',
XYZ=11.0, 24.0, 1.0,
XB= 10.0,12.0, 25.0,25.0, 0.9,1.1, /
&EXIT ID='1stFloorExit', IOR=+2,
COLOR='GREEN',
XYZ=79.0, 24.0, 5.0,
XB= 78.0,80.0, 25.0,25.0, 4.9,5.1, /

42
Practice Example 02: Wide Stairs Example, fds file, version 2
Next was the "MonStairs.fds", just one stair flight.
EVSS XB=40.0,50.0, 10.0,15.0, 4.9,5.1, IOR=-1, ID='WideStairs',
FAC_V0_UP=0.4, FAC_V0_DOWN=0.7, FAC_V0_HORI=1.0,
HEIGHT=0.0, HEIGHT0=-4.0, MESH_ID='FirstFloor' /
Below the same, but an intermediate landing is put in place.
Still the stairs are "straight".
&EVSS XB=40.0,44.0, 10.0,15.0, 4.9,5.1, IOR=-1, ID='WideStairs1',
HEIGHT=-2.0, HEIGHT0=-4.0, MESH_ID='FirstFloor’ /
Next is a landing ==> normal velocities are used
HEIGHT=-2.0, HEIGHT0=-2.0, MESH_ID='FirstFloor’ /
HEIGHT=0.0, HEIGHT0=-2.0, MESH_ID='FirstFloor' /

43
Practice Example 03: Real Project (with Report)

44
Practice – Pathfinder
http://www.thunderheadeng.com/pathfinder/resources/
Examples & Tutorials
http://www.thunderheadeng.com/downloads/pathfinder/users_guide.pdf
Download Pathfinder
http://www.thunderheadeng.com/pathfinder/
http://www.thunderheadeng.com/pathfinder/tutorials/
Licence Prices
http://www.thunderheadeng.com/downloads/pathfinder/tech_ref.pdf
Software Technical Reference
https://www.thunderheadeng.com/wp-
content/uploads/dlm_uploads/2012/05/verification_validation_2017_1.pdf
Software Verification & Validation
https://www.youtube.com/watch?v=c6unBZ
oY9Ag

45
Simulation Modes
Pathfinder supports two movement simulation modes. In "Steering" mode, occupants use a
steering system to move and interact with others. This mode tries to emulate human behavior
and movement as much as possible. SFPE mode uses a set of assumptions and hand-calculations
as defined in the Engineering Guide to Human Behavior in Fire (SFPE, 2003). In SFPE mode,
occupants make no attempt to avoid one another and are allowed to interpenetrate, but doors
impose a flow limit and velocity is controlled by density.
Limitations and Known Issues
Pathfinder does not presently integrate results from a fire model or provide support for complex
behaviors (e.g. family grouping).
Dynamic geometry is only partially supported (e.g. elevators, virtual escalators, and door
opening/closing are supported, but trains and other moving surfaces are not).
Elevators are supported in evacuation-only circumstances. They do not model a general-purpose
elevator system.
Practice – Pathfinder - Technical Reference

46
‒ Pathfinder uses a 3D geometry model. Within this geometric model is a navigation mesh
defined as a continuous 2D triangulated surface referred to as a "navigation mesh.“ Occupant
motion takes place on this navigation mesh.
‒ Pathfinder supports drawing or automatic generation of a navigation mesh from imported
geometry – including FDS files,PyroSim files, DXF and DWG files. Also background images
BMP, GIF, JPG, PNG, and TGA .
‒ The navigation geometry is organized into rooms of irregular shape. Each room has a
boundary that cannot be crossed by an occupant. Travel between two adjacent rooms is
through doors. A door that does not connect two rooms and is defined on the exterior
boundary of a room is an Exit door.
‒ Any location on the navigation mesh can be categorized as one of four terrain types:
‒ Open space (rooms & ramps)
‒ Doors
‒ Stairs
‒ Exit
‒ Behaviors and Goals
‒ Seek Goals occupant uses path planning, path generation, and path following.
‒ Idle Goals. Occupants wait until an event occurs.
‒ Goals
‒ Assist Occupants
‒ Detach from Assistants
‒ Fill Room
‒ Goto Elevator
‒ Go to Exit
‒ Go to room
‒ Goto Waypoint
‒ Wait
‒ Wait for Assistance

47
‒ Door Distance Map
‒ For each vertex of the sub-divided triangle, a distance value is generated that is the minimum
distance to a set of doors.
‒ The set of doors used to generate the distance map varies per-occupant based on whether the
occupant can move in the room. The set only includes doors that are active.
‒ Ideal Seek Direction
‒ Once the occupant has obtained the door distance map, the occupant determines an ideal seek
direction. To do this, the occupant creates sample directions that are 30° apart from each other
covering a full 360°.
‒ Then the occupant checks to see if they will collide with other occupants in that direction and
how far it is to the collision. The occupant then limits the distance that can be travelled in that
direction to the minimum of the distance to an occupant collision and distance to the
maximum door distance.
‒ The occupant chooses the sample direction that will give the farthest distance from a door
according to the door distance map.
‒ Decide Whether to Move

48
Paths
‒ Path Planning (Locally Quickest)
‒ Locally quickest is the path planning approach used in Pathfinder .
‒ It plans the route hierarchically, using local information about the occupant’s current room and
global knowledge of the building. It is assumed that an occupant knows about all doors in their
current room as well as queues at those doors. It is also assumed that the occupant knows how far
it is from one of those doors to the current destination (seek goal). Locally quickest then uses this
information to choose a door in the current room based on a calculated cost of that door. A path is
then generated to the door, which the occupant can follow.
‒ Door Choice
‒ The cost for each target is based on multiple criteria and the occupant’s preferences.
Current room travel time, current room queue time, global travel time, distance travelled in
room, Current Room Travel Time Cost Factor, Current Room Queue Time Cost Factor, Global
Travel Time Cost Factor, Current Door Preference, Current Room Distance Penalty.
‒ Backtrack Prevention
‒ In Pathfinder, once an occupant manages to exit a room using a particular exit door, they are
committed to that routing decision using the following rules:
‒ 1. The next local door the occupant selects may not lead back into any previous rooms. If this
rule eliminates all options.
‒ 2. Backtrack prevention is disabled, the occupant can choose from any valid local door.
‒ Path Generation

49
‒ Path Generation
‒ Once a local target has been chosen through path planning, a path is needed to reach the target.
‒ Pathfinder uses the A* search algorithm [Hart et al., 1968] and the triangulated navigation mesh.
‒ The resulting path is represented as a series of points on edges of mesh triangles. These points
from A* create a jagged path to the occupant’s goal.
‒ To smooth out this jagged path, Pathfinder then uses a variation on a technique known as string
pulling [Johnson, 2006]. This re-aligns the points so the resulting path only bends at the corner of
obstructions but remains at least the occupant’s radius away from those obstructions. Examples of
these final points, called waypoints, are shown in .

50
Calculation of Measurement Region Quantities
The calculation of density and velocity in measurement regions uses an implementation of
Steffen and Seyfried's Voronoi diagram-based method [Steffen and Seyfried, 2010]. In this
method a Voronoi diagram is created to divide space among occupants. Each occupant's density
is calculated based on the size of their cell in the Voronoi diagram. These densities are then
combined using a weighted average, where the weights are the portion of the measurement
area that intersects the Voronoi cell.
Occupants whose location is up to 1.41 meters outside the measurement region will contribute
to the measurement, but more distant agents will be ignored.
‒ The 1.41 meter range corresponds to a 4 m2 square maximum area of influence for each
occupant.

51
Practice – Pathfinder - Graphical User Interface

52
Importing FDS Output Data
Pathfinder can use the PLOT3D data output from FDS to create time history data for each occupant as
they move throughout the simulation. In cases where FDS PLOT3D output data is available for CO
Volume Fraction, CO2 Volume Fraction, and O2 Volume Fraction; Pathfinder will also output FED for
each occupant specified.
FDS data integration is a measurement only and does not alter the movements or decision making
within the Pathfinder simulation.

53
Vehicle Shapes

54

55
Practice – MassMotion
http://www.oasys-software.com/massmotion-tutorials.html
https://www.youtube.com/channel/UCCSaCU47M1miJaf7l357VTw
Tutorials
http://www.oasys-software.com/media/Manuals/Latest_Manuals/MassMotion.pdf
Download MassMotion
http://www.oasys-software.com/customer-service/request-trial.html?product=MassMotion
https://www.youtube.com/watch?v=dR5G5SNI5T4
First Version - April 2011
Current Version 9.0.3.2 - 12th April 2017
Verification testing of the MassMotion model has been performed in accordance with:
• International Maritime Organisation (IMO) 1238
• National Institute of Standards (NIST) [Ronchi, E., Kuligowski, E.D., Reneke, P.A.,
Peacock, R.D., Nilsson, D., The Process of Verification and Validation of Building Fire
Evacuation Models, NIST Technical Note 1822, 2013.]
“The World's Most Advanced Crowd
Simulation Software”

56
Working with Geometry
• Importing Geometry (.3ds, .dae, .dxf, .fbx, .ifc, .obj)
• Creating Geometry
• Editing Geometry
BIM model
• Revit
IFC
MassMotion
Scene
‒ Floor
‒ Link
‒ Stair
‒ Ramp
‒ Escalator
‒ Path
‒ Portal
‒ Barrier

57
“Each agent has the ability to monitor and react to its environment according to a unique set of characteristics and goals”
Agents
‒ Profile characteristics
‒ Scheduling (events, journey, etc)
‒ Behaviour
‒ Agent Tasks ("things to do")
‒ Agent Navigation (best path to a given destination) Costing Routes
‒ Agent Movement (Social Forces)
Physical properties
‒ Body Radius
‒ Speed Distribution
‒ Direction Bias
‒ Shuffle Factor
‒ Max Acceleration
‒ Max Turn Rate
Agents, Profile Properties
Personality
‒ Horizontal
‒ Distance Cost
‒ Vertical Distance
‒ Cost
‒ Queue Cost
‒ Processing Cost
+ Tokens!

58
Agents, Behaviour, Agent Tasks
Types
‒ Moving to a portal destination
‒ Moving to and entering a process chain
‒ Evacuating a zone
‒ Waiting in an area for some duration
‒ Executing a sequence of sub tasks (in order)
‒ Exiting the simulation

59
Agents, Behaviour, Agent Navigation
Automatically creating path networks
Costing Routes
‒ Downstream Horizontal Distance (target – goal)
‒ Downstream Vertical Displacement
‒ Near Horizontal Distance (agent – target)
‒ Queue Time
‒ Opposing Flow
‒ Closed Penalty
‒ Backtrack Penalty
Stochastic Elements => randomness => agent personality and choice variability

60
Agents, Behaviour, Agent Movement
Agent Movement
‒ Finding the Target
‒ Neighbours
‒ Social Forces
Component Forces
‒ Goal
‒ Neighbour
‒ Cohesion
‒ Collision
‒ Drift
‒ Orderly Queuing
‒ Corner
Agent Speed
‒ Profile
+
‒ Density
‒ Object Speed/Type

61
Connection Objects
Properties
‒ Direction
‒ Gates (open by an event)
‒ Flow Limits
‒ Priority Flow
‒ Delay on Enter and Exit
‒ Banks and Perimeters
Connection Objects
‒ Escalators
‒ Links
‒ Paths
‒ Ramps
‒ Stairs

62
Events
‒ Time Event For creating time reference points
‒ Action Event For how to apply an action to all agents in the simulation
‒ Open Gate Event for how to control gated actors
‒ Evacuate Event For how to trigger a basic evacuation

63
Reporting
Graph and Table Data
‒ (text CSV file)
‒ Graph Images (Maps)
‒ Scene Images and Videos
‒ Alembic (export to 3d Max)
FlowCounts
Number of agents who crossed the given connection in the given
direction during the given interval.
Journey Times
(total, by floor, by token, etc)
Where and when they entered the simulation, where and when they
exited the simulation, their normal speed, total distance traveled,
how long the spent 'congested‘, and how long they spent
experiencing various levels of service
Link Queue average
Average number of agents queuing
Agent Count/path
Displays paths of agents across selected objects, where the colour
represents the number of agents who have ever occupied that space.
Agent Time To Exit
Displays paths of agents across selected objects, where the colour
represents the maximum time it took an agent to exit the simulation
from that point.
Average/max. Density
Colours objects based on the average agent density at each point.
Time Above Density
Colours objects based on how long each point has had an agent
density above a given threshold.
Time Occupied
Colours objects based on the total amount of time each point was
occupied by any agent.

64
Simulation time & LOS

65
Timetables
http://www.oasys-software.com/blog/2015/04/using-python-scripts-with-massmotion-%E2%80%93-creating-a-timetable-schedule-from-an-od-matrix/
Origin/Destination Matrices with Python

66
Vision Time

67
Alembic

68

69

70
Carlos Rallo de la Cruz
M. Arch. UPM
M. Eng. in Fire safety. UC3M
Dipl. Project Management. PUC
PhD Candidate CERTEC. UPC
Contact
carlosrallo@gmail.com
+34 647865702
Madrid, Spain
www.rallodelacruz.com
www.arquitecturayfuego.es
www.linkedin.com/in/carlosrallo
Fire Safety impact in Building Design
01. Prescriptive Design 05/05/2017
02. Performance Based Design 05/05/2017
03. NFPA 101 Live Safety Code 12/05/2017
04. Fire Safety in complex Architecture designs 12/05/2017
05. CFD / Fire Dynamics Simulator 26/05/2017
06. Crowd Dynamics & Pedestrian Modeling 02/06/2017
Máster en Ingeniería de
Seguridad contra Incendios
Thank you for your attention!

Carlos Rallo iv crowd dynamics

Recommended

Recommended

More Related Content

Similar to Carlos Rallo iv crowd dynamics

Similar to Carlos Rallo iv crowd dynamics (20)

Recently uploaded

Recently uploaded (20)

Carlos Rallo iv crowd dynamics