How Stride Adaptation in Pedestrian Models Improves Navigation

Pedestrians adjust both speed and stride length when they navigate difficult situations such as tight corners or dense crowds. They do this with foresight reacting instantly when they encounter the difficulty. This has an impact on the movement of the whole crowd especially at bottlenecks where slower movement and smaller steps can be observed. State-of-the-art pedestrian motion models automatically reduce speed in dense crowds simply because there is no space where the virtual pedestrians could advance. The stride length, however, is rarely considered, which leads to artifacts. We reformulate the problem of correct stride adaptation as an optimization problem on a disk around the pedestrian. He or she seeks the position that is most attractive in a sense of balanced goals between the search for targets, the need of space of individual pedestrians and the need to keep a distance from obstacles. The result is a fully automatic adjustment that simplifies calibration, and gives visually natural results and an excellent fit to measured experimental data.