FEMTC 2020

Modeling Waiting Behavior at Train Stations with Cellular Automaton

Tobias Schrödter - Forschungszentrum Jülich


In general, pedestrian models describe the movement of pedestrians with varying level of detail. Often complex processes like way-finding in buildings or navigation through a crowd are solved in a simplified way by collision avoidance algorithms or social forces. For some applications, like the evacuation of a building the moving of pedestrians towards a goal is the substantial contribution determining the dynamic of the process. This changes in more general context, like pedestrians on platforms or airport gates, where waiting people restrict the space for the movement of the others. After reaching their waiting position, pedestrians don’t have any need to keep moving unless the event they are waiting for occurs, e.g. arrival of a train or boarding of the plane starts. In this regard, as opposed to a “moving dynamics” resulting from pedestrians evacuating a specific place, we focus on a “waiting dynamics” where pedestrians visit some temporary areas and wait for a certain amount of time.

We model the process of finding the waiting positions, the pedestrians would like to retain throughout the waiting time. The decision will be based on a floor-field representing comfort values based on static and dynamic influences. We consider the geometry, the positions of the entrances/exits, and other constant parameters as static influence (Ezaki et al. (2016)). To simulate the real-time reaction of the pedestrians to the observed situations, the distribution and motion of the neighbors within a waiting area are considered dynamically. Since the pedestrians usually do not have a global overview of the region, an iterative approach is proposed. First the pedestrians only considers the parts of the geometry which are visible from the current position. If none of the comfort values in the visible area is above an individual threshold, the pedestrians moves through the geometry until such a position is found.



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