Predicting pedestrian flow: a methodology and a proof of concept based on real-life data

Waiting in crowded places: influence of number of pedestrians, waiting time and obstacles

At crowded places, like railway platforms at rush hour, the spatial distribution of waiting pedestrians has a significant influence on performance and level of comfort. However, the choice of waiting places and the resulting spatial distribution of the crowd have rarely been studied. This study investigates the effects of obstacles, number of passengers and waiting time on the distribution of waiting passengers. Laboratory experiments were performed using a mock-up platform with three set-ups: without obstacles, with a narrow and a wide obstacle. Density profiles determine preferred waiting places. While the space usage by waiting passengers is inhomogeneous, the distances between the individuals show surprisingly small variations, regardless of obstacles and number of passengers. This suggests a robust collective optimization of the crowd when searching for waiting positions. In doing so, and without necessity, the participants chose distances to each other extending into the personal zone specified in classical personal-space-concepts. These results indicate necessary refinements of the concept by considering context and collective behaviour. The findings are transformed into floor-fields modelling the space usage by a superposition of attractive or repulsive areas which consider optimization of distances and comfort. This model does not only reproduce the waiting places at platforms but can be adapted for other use cases.

Identification of social groups and waiting pedestrians at railway platforms using trajectory data

To investigate the impact of social groups on waiting behaviour of passengers at railway platforms a method to identify social groups through the monitoring of distances between pedestrians and the stability of those distances over time is introduced. The method allows the recognition of groups using trajectories only and thus opens up the possibility of studying crowds in public places without constrains caused by privacy protection issues. Trajectories from a railway platform in Switzerland were used to analyse the waiting behaviour of passengers in dependence of waiting time as well as the size of social groups. The analysis of the trajectories shows that the portion of passengers travelling in groups reaches up to 10% during the week and increases to 20% on the weekends. 60% of the groups were pairs, larger groups were less frequent. With increasing group size, the mean speed of the members decreases. Individuals and pairs often choose waiting spots at the sides of the stairs and in vicinity of obstacles, while larger groups wait close to the platform entries. The results indicate that passengers choose waiting places according to the following criteria and ranking: shortest ways, direction of the next intended action, undisturbed places and ensured communication. While individual passengers often wait in places where they are undisturbed and do not hinder others, the dominating comfort criterion for groups is to ensure communication. The results regarding space requirements of waiting passengers could be used for different applications. E.g. to enhance the level of service concept assessing the comfort of different types of users, to avoid temporary bottlenecks to improve the boarding and alighting process or to increase the robustness of the performance of railway platforms during peak loads by optimising the pedestrian distribution.

Walking on stairs: Experiment and model

An increasing global population forces urban planners to construct buildings and infrastructure that is extremely deep and high. Elevators and escalators serve skyscrapers and tunnels, but in an emergency people still have to walk on stairs. Computer simulations can mitigate risks of escape situations. For these situations, pedestrian locomotion models need to match reality well. Motion on stairs, however, is not nearly as well understood as motion in the plane. Publications are scarce and some are contradictory. As a result, movement on stairs is usually modeled by slowing down pedestrians by a fixed factor. But is this justified? And what happens at intermediate landings? This contribution aims to clarify inconclusive results of previous research and provide new information to directly incorporate empirical results into a parsimonious computer model. The algorithms are freely available through an open-source framework. After outlining the shortcomings of existing approaches, we present three experiments, from which we derive requirements for the computer model. Reenacting computer experiments shows the extent to which our model meets our observations. We conclude with an applied example, simulating an evacuation of Germany's famous Neuschwanstein Castle.

From Mindless Masses to Small Groups: Conceptualizing Collective Behavior in Crowd Modeling

Computer simulations are increasingly used to monitor and predict behavior at large crowd events, such as mass gatherings, festivals and evacuations. We critically examine the crowd modeling literature and call for future simulations of crowd behavior to be based more closely on findings from current social psychological research. A systematic review was conducted on the crowd modeling literature (N = 140 articles) to identify the assumptions about crowd behavior that modelers use in their simulations. Articles were coded according to the way in which crowd structure was modeled. It was found that 2 broad types are used: mass approaches and small group approaches. However, neither the mass nor the small group approaches can accurately simulate the large collective behavior that has been found in extensive empirical research on crowd events. We argue that to model crowd behavior realistically, simulations must use methods which allow crowd members to identify with each other, as suggested by self-categorization theory.