In recent years, pedestrian simulation has been a valuable tool for the quantitative assessment of egress performance in various environments during emergency evacuation. For a high level of realism, an evacuation simulation requires a behavioral model that takes into account behavioral aspects of real pedestrians. In many studies, however, it is assumed that simulated pedestrians have a global knowledge of the infrastructure and choose either a predefined or the shortest route. It is questionable whether this simplification provides realistic results. This study addresses the problem of human-like route-choice behavior for microscopic pedestrian simulations. A route-choice model is presented that considers two concepts: first, the modeling of infrastructure knowledge to represent the variations in the decision-making processes of pedestrians with different degrees of familiarity with the infrastructure (e.g., regular commuters versus first-time visitors). Second, for each pedestrian the internal preference for selecting a certain path can be calibrated to allow the choice for the fastest routes or the ones that are most convenient for the agent (e.g., by avoiding stairs). The approach here uses a hybrid route-choice behavior model composed of a graph-based macrolevel representation of the environment, which is augmented with local information to avoid obstacles and dense crowds in the vicinity. This method was applied with different parameter sets in an evacuation study of a multilevel subway station. The results show the impact of these parameters on evacuation times, use of infrastructure elements, and crowd density at specific locations.
Optimal Evacuation Strategy for Parking Lots Considering the Dynamic Background Traffic Flows