Traffic simulation for architectural purposes
Crowd simulation help to study and predict the behaviour of people around different objects in the built environment. The ability to observe the flow will help designers to understand and also define the effectiveness of architectural elements and of course many other relevant factors.
Case study: Observing pedestrian flow on streets around the Gherkin.
Modelling individual behaviours
Helbing proposed a model based on physics using a particle system and socio-psychological forces in order to describe human crowd behaviour in a panic situation, this is now called the Helbing Model. His work is based on how the average person would react in a certain situation. Although this is a good model, there are always different types of people present in the crowd and they each have their own individual characteristics as well as how they act in a group structure. For instance, one person may not react to a panic situation, while another may stop walking and interfere in the crowd dynamics as a whole. Furthermore, depending on the group structure, the individual action can change because the agent is part of a group, for example, returning to a dangerous place in order to rescue a member of that group. Helbing’s model can be generalised incorporating individualism.
To simulate more aspects of human activities in a crowd, more is needed than path and motion planning. Complex social interactions, smart object manipulation, and hybrid models are challenges in this area. Simulated crowd behaviour is inspired by the flow of real-world crowds. Behavioural patterns, movement speeds and densities, and anomalies are analysed across many environments and building types. Individuals are tracked and their movements are documented such that algorithms can be derived and implemented into crowd simulations.
Individual entities in a crowd are also called agents. In order for a crowd to behave realistically each agent should act autonomously (be capable of acting independently of the other agents). This idea is referred to as an agent-based model. Moreover, it is usually desired that the agents act with some degree of intelligence (i.e. the agents should not perform actions that would cause them to harm themselves). For agents to make intelligent and realistic decisions, they should act in accordance with their surrounding environment, react to its changes, and react to the other agents.
Rule-based AI
In rule-based AI, virtual agents follow scripts: “if this happens, do that”. This is a good approach to take if agents with different roles are required, such as main characters and several background characters. This type of AI is usually implemented with a hierarchy, such as in Maslow’s hierarchy of needs, where the lower the need lies in the hierarchy, the stronger it is.
Learning AI
In learning AI, virtual characters behave in ways that have been tested to help them achieve their goals. Agents experiment with their environment or a sample environment which is similar to their real one.
Agents perform a variety of actions and learn from their mistakes. Each agent alters its behaviour in response to rewards and punishments it receives from the environment. Over time, each agent would develop behaviours that are consistently more likely to earn high rewards. If this approach is used, along with a large number of possible behaviours and a complex environment agents will act in a realistic and unpredictable fashion.
No.44 from Shiraz 