Daniel H. Biedermann

Using Raspberry Pi for scientific video observation of pedestrians during a music festival.

The document serves as a reference for researchers trying to capture a large portion of a mass event on video for several hours, while using a very limited budget.

A Unified Pedestrian Routing Model Combining Multiple Graph-Based Navigation Methods

The navigation behaviour of pedestrians in street networks can be forecast by computer simulations based on routing models. These models characterise pedestrians’ route choices regarding a variety of factors. However, the spatial cognition aspects are often omitted in routing models; thus, the diversity of predictable routes is limited. Here, we present a unified routing model that describes route choices of pedestrians by integrating the spatial cognitive aspects of allocentric-based and egocentric-based navigation. We achieved this by combining graph-based routing methods, each formalising a single spatial cognitive aspect. In addition, we present a generic calibration method for our model. For validation, we show that our model is able to correctly predict the routing behaviour of pedestrians in a case study.

CarPed - A Hybrid and Macroscopic Traffic and Pedestrian Simulator.

Dense human flow has been a concern for the safety of public events for a long time. Macroscopic pedestrian models, which are mainly based on fluid dynamics, are often used to simulate huge crowds due to their low computational costs (Columbo & Rosini 2005). Similar approaches are used in the field of traffic simulations (Lighthill & Whitham 1955). A combined macroscopic simulation of vehicles and pedestrians is extremely helpful for all-encompassing traffic control. Therefore, we developed a hybrid model that contains networks for vehicular traffic and human flow. This comprehensive model supports concurrent multi-modal simulations of traffic and pedestrians.

A unified pedestrian routing model for graph-based wayfinding built on cognitive principles

ABSTRACT The wayfinding behavior of pedestrians in street and building networks can be predicted by computer simulations based on routing models. To model realistic routing behavior, it is necessary to integrate spatial- and social-cognitive aspects into the wayfinding models. However, a model that incorporates diverse influencing factors on pedestrian route planning has yet not been developed for microscopic simulations. We present a unified routing model that describes pedestrian route choices in street and building environments by integrating spatial- and social-cognitive aspects. We achieve an integration of both domains by combining different graph-based routing methods, each formalizing a cognitive theory. In addition, we present a calibration method for the spatial-cognitive aspects. For validation purposes, we use the model to simulate how the visitors of a music festival navigate to the event and how people navigate in a city district. Our methodology is highly flexible and can be extended to include other aspects of wayfinding behavior.

Oppilatio+ - A data and cognitive science based approach to analyze pedestrian flows in networks

Public transport services are a widespread and environmentally friendly option for mobility. In the majority of cases, passengers of public transport services will have to walk from a subway, train, or bus station to their desired travel destination. In an urban environment with a network of narrow streets, this can lead to crowd congestions during rush hour, due to the fact that passengers tend to arrive in waves. In order to monitor and analyze such crowding behavior, city planners, crowd managers, and organizers of public events must ascertain which routes these pedestrians will take from the respective station to their destination. The Oppilatio+ approach is suitable for solving this problem. It is an easy-to-apply approach to predict way-finding behavior with a minimal set of information. The necessary data includes the schedule of incoming transport vehicles at the stations and the time-stamped count of pedestrians at the respective destinations. Under these conditions, the Oppilatio+ approach is suitable for estimating the distribution of pedestrians on all possible walkways between stations and destinations. This information helps crowd control experts to recognize weak spots in the infrastructure and help event organizers to ensure an undisturbed arrival at their event. We validated our approach using two field experiments. The first one was a field study on a public event, and the second one was a case study for a large Swiss train station.

Oppilatio: The Forecast of Crowd Congestions on Street Networks During Public Events

At many events, the arrival of visitors depends mainly on public transport services. On such occasions, people walk from the station or bus stop to the event site. This can lead to crowd congestions since the visitors arrive in large numbers according to the schedules of the public transport services. Unfortunately, organisers of such events have very limited information about the arrival behaviour of their visitors. Normally, they only know the number of incoming visitors on the event site and the timetable of the public transport service. It is difficult to perform crowd management successfully with so little data. Oppilatio uses this limited data to determine the most likely routing paths of incoming visitors. This allows an early recognition of potential crowd congestions on the access routes and therefore the initiation of countermeasures.