Based on suitable video recordings of interactive pedestrian motion and improved tracking software, we apply an evolutionary optimization algorithm to determine optimal parameter specifications for the social force model. The calibrated model is then used for large-scale pedestrian simulations of evacuation scenarios, pilgrimage, and urban environments.

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Oscillatory flow patterns have been observed in many different driven many-particle systems. The conventional assumption is that the reason for emergent oscillations in opposing flows is an increased efficiency (throughput). In this contribution, however, we will study intersecting pedestrian and vehicle flows as an example for inefficient emergent oscillations. In the coupled vehicle-pedestrian delay problem, oscillating pedestrian and vehicle flows form when pedestrians cross the street with a small time gap to approaching cars, while both pedestrians and vehicles benefit, when they keep some overcritical time gap. That is, when the safety time gap of pedestrians is increased, the average delay time of pedestrians decreases and the vehicle flow goes up. This may be interpreted as a sloweris-faster effect. The underlying mechanism of this effect is explained in detail.

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Abstract. For the past decade, many evolutionary multi-objective optimization (EMO) methodologies have been developed and applied to find multiple Pareto-optimal solutions in a single simulation run. In this paper, we discuss three different classical generating methods, some of which were suggested even before the inception of EMO methodologies. These methods specialize in finding multiple Pareto-optimal solutions in a single simulation run. On visual comparisons of the efficient frontiers obtained for a number of two and three-objective test problems, these algorithms are evaluated with an EMO methodology. The results bring out interesting insights about the strengths and weaknesses of these approaches. Further investigations of such classical generating methodologies and their evaluation should enable researchers to design a hybrid multi-objective optimization algorithm which may be better than each individual method.