A first step is presented toward a simulation tool for shared space zones, including all three prevailing individual modes of transport: cars, bicycles, and walking. Unlike on conventional roads, the behavior in shared spaces cannot be modeled by following a predefined path and strictly obeying traffic rules because the architectural design allows for many more degrees of freedom. Therefore, the research focused on two main aspects: finding a path for each individual and handling potential conflicts with other individuals. A simulation tool is needed because many urban planners see shared space as a modern design concept for busy urban roads. A growing number of cities are interested in experimenting with shared space zones but are uncertain about safety issues and the effectiveness of the design. Although mature simulation tools exist for conventional road designs, no such tool is available for shared space designs because of the added degrees of freedom in movement and more-complex social interactions. To tackle these problems, an infrastructure model was created to help all agents find a path to their destinations. A separate system for handling conflict detects when two agents, following their individual paths, might collide. Game theory is used to resolve these conflicts by maximizing a utility function for different strategies. First results give a preliminary assessment of the functionality of the proposed simulation model for shared space zones and its calibration that uses real trajectories from an existing shared space.
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.
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