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libPEDSIM Documentation
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PedSim Behavior and Background

Here follows a small introduction to the technique used in the code of PEDSIM.

The simulation core takes care of the physical aspects of the system, such as interaction of the agents with the environment or with each other. Typical simulation techniques for such problems are:

For PEDSIM, we need to maintain individual particles, since they need to be able to make individual decisions, such as route choices, throughout the simulation. This immediately rules out field-based methods. We also need a realistic representation of inter-pedestrian interactions, which rules out both the queue models and the SPH models.

For microscopic simulations, there are essentially two techniques: methods based on coupled differential equations, and cellular automata (CA) models. In our situation, it is important that agents can move in arbitrary directions without artifacts caused by the modeling technique, which essentially rules out CA techniques. A generic coupled differential equation model for pedestrian movement is the social force model by Helbing et al., see e.g. this paper.

\[ m_i \, \frac{d\mathbf{v}_i}{dt} = m_i \, \frac{\mathbf{v}_i^0 - \mathbf{v}_i}{\tau_i} + \sum_{j\ne i}{\mathbf{f}_{ij}} + \sum_{W}{\mathbf{f}_{iW}} \]

where m is the mass of the pedestrian and v its velocity. v0 is its desired velocity; in consequence, the first term on the RHS models exponential approach to that desired velocity, with a time constant tau. The second term on the RHS models pedestrian interaction, and the third models interaction of the pedestrian with the environment. The social force model should be considered as an example on how to model the pedestrian interaction. It is easy to understand and simple to implement. However, a future implementation of PEDSIM might use a different model.

Pedestrians interact with each other, which includes avoiding collisions (short range interaction), and attraction to enemies (long range, which represents the "will" of the agents. This attraction to enemies is just an example and should be replaced by some more complicated and meaningful functions). Also avoidance of objects like trees is implemented.

This simulation also works close to obstacles, as are found e.g. close to buildings. Also the simulation of the inside of buildings is possible, which allows the usage of the same framework for e.g. evacuation simulation.

Any mobility simulation system does not just consist of the mobility simulation itself (which controls the physical constraints of the agents in a virtual world), but also of modules that compute higher level strategies of the agents. In fact, it makes sense to consider the physical and the mental world completely separately.

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⁠(c) Christian Gloor [ c|h|g|l|o|o|r|@|s|i|l|m|a|r|i|l|.|o|r|g| ]