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Crowd Modelling & Dynamics

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One of the major goals in crowd simulation is to steer crowds realistically and recreate human dynamic behaviours.  iTransport Planning undertake Crown Dynamics studies using state of the art simulation environment designed to model any kind of pedestrian infrastructure or environment in which the performance, safety, and security can be analysed and optimized.


Crowds cannot be analysed as easily as others. The psychology of a crowd impacts how it will be broken up and studied. Crowds can be casual, such as a group of pedestrian walking down the road or active or passive as an audience. While the main crowd is the subject of the bulk of the analysis, anomalies are taken into account, like someone opposing the flow of traffic or a biker travelling through a group of walkers.  Hence, the purpose of a group of individuals determines the interpretation of the data obtained.

There exists several overarching approaches to crowd simulation and AI, each one providing advantages and disadvantages based on crowd size and time scale.

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Crowd Artificial Intelligence

Otherwise referred to as swarm intelligence, our analysis and application of crowd movement can contribute to the modeling of group behaviour based on biological and artificial models. Social instinct behaviour is applied to complex systems that model multiple agents and their interactions. Population-based methods are used to represent local interactions of agents with their surroundings.

Need Help in any of the above?

We have vast experience on preparing Crowd Modelling Reports covering many different types of proposed developments for our clients, we would welcome the opportunity to provide you with the UK’s most competitive fee proposal.

Please get in touch with us if we can assist you further.





Our systems can generate a realistic crowd simulation with given inputs and simulate how the simulated moving objects, or agents, will interact with each other and with the environment. The goal is to replicate a crowd's movement patterns given a large number of agents in a given space. Algorithms based on crowd analysis attempt to manage the movement of the crowd. The more efficient and realistic a simulation becomes, the more complex the algorithm must become. The software is able to manipulate the trajectory of individual agents based on variables such as the agents' goals, stress forces, obstacles, and levels of arousal.

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