The Inflection Point of the Speed-Density Relation and the Social Force Model

It has been argued that the speed-density digram of pedestrian movement has an inflection point. This inflection point was found empirically in investigations of closed-loop single-file pedestrian movement. The reduced complexity of single-file movement does not only allow a higher precision for the evaluation of empirical data, but it occasionally also allows analytical considerations for micosimulation models. In this way it will be shown that certain (common) variants of the Social Force Model (SFM) do not produce an inflection point in the speed-density diagram if infinitely many pedestrians contribute to the force computed for one pedestrian. We propose a modified Social Force Model that produces the inflection point.Comment: accepted for presentation at conference Traffic and Granular Flow 201

Crowd Research at School: Crossing Flows

It has become widely known that when two flows of pedestrians cross stripes emerge spontaneously by which the pedestrians of the two walking directions manage to pass each other in an orderly manner. In this work, we report about the results of an experiment on crossing flows which has been carried out at a German school. These results include that previously reported high flow volumes on the crossing area can be confirmed. The empirical results are furthermore compared to the results of a simulation model which succesfully could be calibrated to catch the specific properties of the population of participants.Comment: contribution to proceedings of Traffic and Granular Flow 2013 held in J\"ulich, German

Stochastic Transition Model for Discrete Agent Movements

We propose a calibrated two-dimensional cellular automaton model to simulate pedestrian motion behavior. It is a v=4 (3) model with exclusion statistics and random shuffled dynamics. The underlying regular grid structure results in a direction-dependent behavior, which has in particular not been considered within previous approaches. We efficiently compensate these grid-caused deficiencies on model level.Comment: 8 pages, 4 figure

Calibration and Validation of A Shared space Model: A Case Study

Shared space is an innovative streetscape design that seeks minimum separation between vehicle traffic and pedestrians. Urban design is moving toward space sharing as a means of increasing the community texture of street surroundings. Its unique features aim to balance priorities and allow cars and pedestrians to coexist harmoniously without the need to dictate behavior. There is, however, a need for a simulation tool to model future shared space schemes and to help judge whether they might represent suitable alternatives to traditional street layouts. This paper builds on the authors’ previously published work in which a shared space microscopic mixed traffic model based on the social force model (SFM) was presented, calibrated, and evaluated with data from the shared space link typology of New Road in Brighton, United Kingdom. Here, the goal is to explore the transferability of the authors’ model to a similar shared space typology and investigate the effect of flow and ratio of traffic modes. Data recorded from the shared space scheme of Exhibition Road, London, were collected and analyzed. The flow and speed of cars and segregation between pedestrians and cars are greater on Exhibition Road than on New Road. The rule-based SFM for shared space modeling is calibrated and validated with the real data. On the basis of the results, it can be concluded that shared space schemes are context dependent and that factors such as the infrastructural design of the environment and the flow and speed of pedestrians and vehicles affect the willingness to share space

Social Attention: Modeling Attention in Human Crowds

Robots that navigate through human crowds need to be able to plan safe, efficient, and human predictable trajectories. This is a particularly challenging problem as it requires the robot to predict future human trajectories within a crowd where everyone implicitly cooperates with each other to avoid collisions. Previous approaches to human trajectory prediction have modeled the interactions between humans as a function of proximity. However, that is not necessarily true as some people in our immediate vicinity moving in the same direction might not be as important as other people that are further away, but that might collide with us in the future. In this work, we propose Social Attention, a novel trajectory prediction model that captures the relative importance of each person when navigating in the crowd, irrespective of their proximity. We demonstrate the performance of our method against a state-of-the-art approach on two publicly available crowd datasets and analyze the trained attention model to gain a better understanding of which surrounding agents humans attend to, when navigating in a crowd