Realistic Stride Length Adaptation in the Optimal Steps Model

Sivers, Isabella von; Köster, Gerta

doi:10.1007/978-3-319-10629-8_20

Cited by 9 publications

(11 citation statements)

References 11 publications

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“…This implies that flow was modelled correctly. Also, the simulation results for important benchmark scenarios, such as navigation around a column in a narrow corridor [90] and a crowd in front of a bottleneck, matched reality much more closely than the former state-of-the-art. We believe that this increases the predictive power of pedestrian traffic models.…”

Section: Discussion and Outlookmentioning

confidence: 63%

“…The Nelder-Mead method modifies and improves the simplex in every loop using four operations: reflection, expansion, contraction, and shrinkage. Mathematical details for our problem can be found in [89,90].…”

Section: Mathematical Formulation and Numerical Solutionmentioning

confidence: 99%

“…Hence, within our VADERE simulation platform, every model we investigate is checked against the tests listed in the RiMEA validation guideline [66] with the exception of those test where we do not claim to model the situation, e.g., movement on several levels of a building. Not only the OSM is tested in this way but also the implementations of a social force model and a cellular automaton model that we use for comparison in [70,12,90,14].…”

Section: Calibration and Validationmentioning

confidence: 99%

“…On the other extreme end, large steps when walking into a crowd or approaching a tight gap make navigational fine tuning difficult, even when the speed is low. Agents may get stuck where real pedestrians still move [90] and the flow may be reduced to zero where it should not be. Hence we need a modelling concept that allows to immediately and simultaneously adapt speed and stride length.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic stride length adaptation according to utility and personal space

Sivers

Köster

2015

Transportation Research Part B: Methodological

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a b s t r a c tPedestrians adjust both speed and stride length when they navigate difficult situations such as tight corners or dense crowds. They try to avoid collisions and to preserve their personal space. State-of-the-art pedestrian motion models automatically reduce speed in dense crowds simply because there is no space where the pedestrians could go. The stride length and its correct adaptation, however, are rarely considered. This leads to artefacts that impact macroscopic observation parameters such as densities in front of bottlenecks and, through this, flow. Hence modelling stride adaptation is important to increase the predictive power of pedestrian models. To achieve this we reformulate the problem as an optimisation problem on a disk around the pedestrian. Each pedestrian seeks the position that is most attractive in a sense of balanced goals between the search for targets, the need for individual space and the need to keep a distance from obstacles. The need for space is modelled according to findings from psychology defining zones around a person that, when invaded, cause unease. The result is a fully automatic adjustment that allows calibration through meaningful social parameters and that gives visually natural results with an excellent fit to measured experimental data.

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Section: Discussion and Outlookmentioning

confidence: 63%

Section: Mathematical Formulation and Numerical Solutionmentioning

confidence: 99%

Section: Calibration and Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic stride length adaptation according to utility and personal space

Sivers

Köster

2015

Transportation Research Part B: Methodological

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“…Since then, first proofs of concept of specific situations have emerged, where empirical findings from social psychology, not analogy from physics, inspire the model. See [ 37 , 38 ]. Yet, to our knowledge, nobody has operationalized psychological findings into computer models of crowds where an agents’ ability to pass through a dense crowd emerges as an effect.…”

Section: Introductionmentioning

confidence: 99%

Agent-based simulation of collective cooperation: from experiment to model

Kleinmeier

Köster

Drury

2020

J. R. Soc. Interface.

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Simulation models of pedestrian dynamics have become an invaluable tool for evacuation planning. Typically, crowds are assumed to stream unidirectionally towards a safe area. Simulated agents avoid collisions through mechanisms that belong to each individual, such as being repelled from each other by imaginary forces. But classic locomotion models fail when collective cooperation is called for, notably when an agent, say a first-aid attendant, needs to forge a path through a densely packed group. We present a controlled experiment to observe what happens when humans pass through a dense static crowd. We formulate and test hypotheses on salient phenomena. We discuss our observations in a psychological framework. We derive a model that incorporates: agents’ perception and cognitive processing of a situation that needs cooperation; selection from a portfolio of behaviours, such as being cooperative; and a suitable action, such as swapping places. Agents’ ability to successfully get through a dense crowd emerges as an effect of the psychological model.

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