    libPEDSIM Documentation [HOME] [EXAMPLES] [ECOSYSTEM] [DOCUMENTATION] [DOWNLOAD]   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:

• In microscopic simulations each particle is represented individually.
• In macroscopic or field-based simulations, particles are aggregated into fields. The corresponding mathematical models are partial differential equations, which need to be discretized for computer implementations.
• It is possible to combine microscopic and field-based methods, which is sometimes called smooth particle hydrodynamics. In SPH, the individuality of each particle is maintained. During each time step, particles are aggregated to field quantities such as density, then velocities are computed from these densities, and then each individual particle is moved according to these macroscopic velocities.
• As a fourth method, somewhat on the side, exist the queuing simulations from operations research. Here, particles move in a networks of queues, where each queue has a service rate. Once a particle is served, it moves into the next queue.

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. 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.

• The Physical Layer (the mobility simulation) takes care of the physical aspects of the system, such as movement of the agents, interaction of the agents with the environment, or interactions between the agents.
• The Mental Layers implement the humans intelligence (well, at least a part of it), which improves the agent's behavior. Actually, if the mental layer strategy are very sophisticated, there is no need for the social force model in the physical simulation - all the forces can be set to zero. The Look Ahead mental strategy tells each agent to look for other agents in front of him, an count the ones at the left side and the ones at the right side. It then will walk into the direction where less other agents are. Collisions with walls and other pedestrians are avoided by the pedestrian itself, and not by a constraint by the underlying physical model. Another example for a mental layer module is a Route Generator. It is not enough to have agents walk around randomly; for realistic applications it is necessary to generate plausible routes for each pedestrian. Being able to compute routes, as the route generator does, only makes sense if one knows the destinations for the agents. A technique in transportation research is to generate a (say) day-long chain of activities for each agent, and each activity's specific location. There exist very sophisticated mental layer modules. There is for example a View Analyzer Module, which describes to the system what individual agents "see" as they move through the landscape. The agents field-of-view is analyzed, and events are sent to the system describing what the agent sees.   ⁠(c) Christian Gloor [ c|h|g|l|o|o|r|@|s|i|l|m|a|r|i|l|.|o|r|g| ]