Advantages and disadvantages.
(updated 31st August 2007)
Keywords: simulating people, simulating crowds, simulating crowd dynamics, workshops
Uses of Crowd Dynamics around the world
UK Cabinet Office Emergency Planning College - Workshops
London New Year Fireworks (Real-Time Decision Support)
Wide Area Evacuation (Real-Time Decision Support and information system)
DWELL Time models - train loading/unloading under high density conditions
Simulation advantages and disadvantages.
A complex problem can be analysed and solved using a computer simulation. We need to be clear about definitions of a model, simulation and animation when dealing with crowd dynamics.
"Truth is ever to be found in simplicity, and not in the multiplicity and confusion of things. As the world, which to the naked eye exhibits the greatest variety of objects, appears very simple in its internal constitution when surveyed by a philosophical understanding, and so much the simpler by how much the better it is understood." Isaac Newton
Why Build a model?
We build models to understand a particular phenomena or characteristic of a system. We build models to understand the relationship between cause and effect. We build models to improve our understanding because - with a good model comes discovery, with discovery comes understanding and with understanding comes control. DISCOVERY - UNDERSTANDING - CONTROL.
What is a model?
Models are mathematical representations of a process (for example activity cycle diagrams, flow charts, algorithms etc. are all examples of models). A model contains the key elements of a system under study. Most good models begin with the modeller breaking down a complex process into a series of parts. There are many good fire and egress models in the market today. All mathematical models are "toy" systems in that they represent a part of a reality. The development of effective pedestrian simulation systems is important in understanding risk and crowd safety in places of public assembly. Building a crowd simulation is a complex exercise in applied modelling and it is important that the modeller understands the differences between a simulation, an animation and a model.
Simulations can take many forms from spreadsheets to three dimensional representations of things moving in space. A simulation can be stochastic or deterministic - it is important that the developer understands the difference between these two types of simulation. Stochastic models some probabilistic element (uncertainty) in a system or process. Typical outputs are boundary conditions, upper and lower limits and degree of certainty. If a model is stochastic it needs to output confidence limits so the end user understands that the process under scrutiny has elements of randomness. A determinist model, as students of chaos theory will know, can also have uncertain outcomes. Be cautious of simulation outputs that do not state their assumptions. The original pedestrian simulation system was designed to assess the safety limits for places of public assembly.
Understanding the limits to simulations
In order to understand some aspect of a system it helps to simplify it as much as possible, and to include only those properties and characteristics that are essential to understanding. If you want to determine how an object drops, you don't concern yourself with whether it is new or old, is red of green, has odour or not. You eliminate those things and do not needlessly complicate matters.
The simplification you call a model or a simulation and you can present it as an actual representation on a computer screen, or as a mathematical relationship.
Now as you wish to know more and more about any phenomenon, or as the phenomenon becomes more complex, you need more and more elaborate equations, more and more detailed programming, and you end with a computerized simulation that is harder and harder to grasp.
In the principle of LPS (least possible simulation) a simulation gains in complexity faster than the object being simulated does and, eventually, the simulation catches up with the phenomenon. Thus, it was established years ago (Information Theory and the "Shannon Entropy" concept) that you simply cannot produce something that has less information than the thing you are claiming to simulate.
Or to put it another way - a system, in it's full complexity, cannot be represented by any simulation smaller than itself.
Thus - no crowd modelling system could claim to model the whole range of human behaviour - without FIRST understanding the WHOLE range of human behaviour. The Shannon principle is simple to express in that - what is "knowable" about a system can be modelled using simulations.
One important addition to the Shannon Entropy principle relates to complexity and emergence. Namely that a system of "simple" things can exhibit the apparent complexity of something that has "more information" however this needs further clarification.
From my PhD thesis (Chapter 5)
It is important to define the principles of emergence and how they relate to a simulation. By definition the simulation has to have as few rules as possible to be successful. Those rules also have to represent the characteristics of individuals as they progress through complex geometries, react to differing densities, and act appropriately in a variety of conditions. The conditions can be interpreted by the entities as they navigate the simulation. To prove the concepts and set a goal or objective for the definition of a simulation, we need to outline the principles we wish to demonstrate.
These can be summarised as:
To build an entity level model of the hypothetical mechanisms of a behaviour we must:
Show that, at a fundamental level, the essential elements actually exist.
Show, by simulation, that the model is capable of generating the observed behaviour (as an emergent phenomenon).
This leads us to the logical conclusion that if the simulation satisfies the process of the behaviour from the proven underlying mechanisms, we have demonstrated the existence of the phenomenon as a well-constructed scientific object.
Note that this does NOT contradict the principles of Shannon Entropy in that I only claim to model an ELEMENT of crowd behaviour (ie: simulated annealing as a good proxy for evaluating crowd dynamics with respect to risk). I strongly disagree with any system that claims to model "the whole range of human behaviour" to any given situation and would ask for good third party and independent validation before I would entertain any such concept. No one has risen to that challenge yet...
Simulations and Animations
Animations are relevant to the process of understanding the dynamics of a complex system. Typically taking the form of computer simulations using graphical representations of a process. They may be real-time animations or replayed animations from a file. It is important to understand the process behind an animation, how it was constructed and what elements it displays.
We can make the following general observations about the simulation approach to decision making.
Simulation is most appropriate when the problem is too complex or difficult to solve using another method.
A model must be developed to represent the various relationships existing in the problem situation.
A process such as random-number procedures must be employed to generate values for the probabilistic components of the model.
A bookkeeping procedure must be developed to keep track of what is happening in the simulation process.
The simulation process must be conducted for many periods in order to establish the long-run averages for the decision alternatives or other changes in the system. Ergodic analysis (long term averages) should be the purpose of the simulation system.
Local transient effects can skew simulation results - as can bad model building - it is essential that simulation builders be scrutinised in the same way one would scrutinise the simulation system.
A decision support simulation needs to be validated and open to scrutiny. Good third party validation is essential to be confident of any simulation system.
Caveat emptor - buyer beware !
How to build a good simulation
The aims of the simulation/model builder should be to construct a model which is easy to understand, easy to detect errors in the process of building a model and easy to compute a solution. To do this one has to spend more time in the analysis of the clients requirement than the process of building a model. It is not necessary to build a complex model of an environment in all of its configurations with all of the individuals in all of their possible states. Indeed such a model is impossible to build (computational and analytically intractable) and, more importantly, impossible to understand.
You need to break the process into logical, easy to build, easy to test, easy to understand sections. Often it is the process of the analysis of the problem that leads to a simpler model building process and easier to understand results.
"The pedestrian planning process follows the classic sequence of problem definition, identification of restraints, determination of program objectives, establishment of study scope and procedures, collection and analysis of data, development of alternative solutions. A planning program for pedestrians may involve one building, a small group of buildings, a downtown core network, or even larger systems of interlocking networks. Project scope may range from a basic low budget improvement program, gradually implemented over a long time period, to a large capital project with accelerated priorities.
"The primary goals and objectives of an improvement program for pedestrians are: safety, security, convenience, continuity, comfort, system coherence and attractiveness. All goals are interrelated and overlapping." Pedestrian Planning and Design by Dr. John J. Fruin.
In general GOOD simulation systems follow four simple principles. Simulations should be:
Simple to build
Simple to modify
Simple to understand
Simple to communicate its output
Simulation should not necessarily be thought of as another technique for finding optimal solutions to problems. However, once a simulation model has been developed, a quantitative analyst may vary certain key design parameters and observe the effect on the output of the computer runs. Through a series of experiments with the simulation model good values may be selected for the key design parameters of the system.
Visualising Crowd Flow and High Density
Level of Service A. Flow rate less than 23 people per metre per minute. Virtually unrestricted choice of speed; minimum manoeuvring to pass; crossing & reverse movements are unrestricted.
Level of Service B. Flow rate 23 to 33 people per metre per minute. Normal walking speeds only occasionally restricted; some occasional interference in passing; crossing & reverse movements are possible with occasional conflict.
Level of Service C. Flow rate 33 to 49 people per metre per minute. Walking speeds are partially restricted; passing is restricted but possible with manoeuvring; crossing and reverse movements are restricted and require significant manoeuvring to avoid conflict, flow is reasonably fluid.
Level of Service D. Flow rate 49 to 66 people per metre per minute. Walking speeds are restricted and reduced, passing is rarely possible without conflict; crossing and reverse movements are severely restricted with multiple conflicts; some probability of momentary flow stoppages when critical densities might be intermittently reached.
Level of Service E. Flow rate 66 to 82 people per metre per minute. Walking speeds are restricted and occasionally reduced to shuffling; frequent adjustment of gait is required and passing is impossible without conflict; crossing and reverse movements are severely restricted with unavoidable conflicts; flow achieves maximum capacity under pressure, but with frequent stoppages and interruptions of flow.
Level of Service F. Flow rate variable. Walking speed is reduced to shuffling; passing is impossible, crossing and reverse movements are impossible; physical contact is frequent and unavoidable; flow is sporadic and on the verge of complete breakdown and stoppage.
Simulation model of the Elliptical Jamarah Pillars - Saudi Arabia
To allow the user to visualise the above we colour the agents with their appropriate level of service indicator.
