Development of a cognitive and decision-based model for pedestrian dynamics

Research on pedestrian dynamics is always an interplay between empirical and experimental observations and theoretical modelling and simulations. Thereby, pedestrian models are not only used for theoretically reproducing empirical data, but also to better analyse and understand the mechanisms and behavioural aspects that underlie pedestrian dynamics. The model approach that is presented in this work assumes pedestrian motion to result from cognitive and decision-based processes. The model is set in continuous space, but discrete time and therefore belongs to a model class whose potential has been rarely investigated yet. However, compared to other model classes that are widely used in pedestrian dynamics, this approach is highly advantageous considering fidelity and simplicity in its structure. A pedestrian is considered as an autonomous entity that gains information on the surrounding by visual perception and anticipation. On this basis, the agent takes a decision on its movement for the next time step. The main focus during the development of the approach was on modelling the interaction and collision avoidance with other agents. Particularly for the collision avoidance, stochastic procedures are used in order to consider uncertainties of human decisions explicitly which makes the modelling approach more realistic. As simulation results show, the new approach is able to reproduce characteristic effects of pedestrian motion very well. For typical scenarios that have been used as test cases the simulated results fit well, at least qualitatively but often even quantitatively, to experimental data. Especially, important macroscopic effects, particularly collective phenomena, are observed in the results that are reproduced by modelling individual interaction of a single pedestrian with others. During the development of the model its parameters were specifically adjusted for the single scenarios, considering the empirical data basis. In addition, several cognitive mechanisms were supplemented. By this means, it is possible to identify and understand the important intrinsic properties and motivations of a pedestrian. Furthermore, this provides the opportunity to gain insight into how cognitive and decision-based approaches can model pedestrian behaviour as realistically as possible

Concept of a Decision-Based Pedestrian Model

We develop a decision-based model for pedestrian dynamics which is an extension of the Stochastic Headway Distance Velocity (SHDV) model for single-file motion to two dimensions. The model is discrete in time, but continuous in space. It combines perception, anticipation and decision-making with the simplicity and stochasticity that are characteristic for cellular automaton models. The basic concept is discussed and preliminary results show that the model yield realistic trajectories and fundamental diagrams

Empirical study of the influence of social groups in evacuation scenarios

The effects of social groups on pedestrian dynamics, especially in evacuation scenarios, have attracted some interest recently. However, due to the lack of reliable empirical data, most of the studies focussed on modelling aspects. It was shown that social groups can have a considerable effect, e.g. on evacuation times. In order to test the model predictions we have performed laboratory experiments of evacuations with different types and sizes of the social groups. The experiments have been performed with pupils of different ages. Parameters that have been considered are (1) group size, (2) strength of intra-group interactions, and (3) composition of the groups (young adults, children, and mixtures). For all the experiments high-quality trajectories for all participants have been obtained using the PeTrack software. This allows for a detailed analysis of the group effects. One surprising observation is a decrease of the evacuation time with increasing group size.Comment: 8 pages, 4 figures, to be published in Traffic and Granular Flow '15 (Springer, 2016

A Glossary for Research on Human Crowd Dynamics. In Collective Dynamics

This article presents a glossary of terms that are frequently used in research on human crowds. This topic is inherently multidisciplinary as it includes work in and across computer science, engineering, mathematics, physics, psychology and social science, for example. We do not view the glossary presented here as a collection of finalised and formal definitions. Instead, we suggest it is a snapshot of current views and the starting point of an ongoing process that we hope will be useful in providing some guidance on the use of terminology to develop a mutual understanding across disciplines. The glossary was developed collaboratively during a multidisciplinary meeting. We deliberately allow several definitions of terms, to reflect the confluence of disciplines in the field. This also reflects the fact not all contributors necessarily agree with all definitions in this glossary