This project provides tools for interdisciplinary analysis of fine-scaled pedestrian movement, especially for science-based decision-making. It is motivated by the fact that local movement and interaction patterns of individuals congregated in public locations, such as entertainment venues and transportation hubs, impacts public health in myriad ways. For instance, infectious disease transmission in crowded areas, such as the 2016 measles outbreak in Disney world that resulted in 125 cases, is affected by the evolution of the pedestrian contact network.
In such contexts, mathematical models can be used to explore different “what if” scenarios for planning public health interventions. For example, pedestrian mobility models could help in the design of public spaces and policies that reduce contacts to mitigate disease spread or encourage walking to improve health outcomes. Understanding the fine-scale movement and interaction patterns of people can help design effective policies and spatial layouts to better engineer suitable movement and interaction patterns for improved public health outcomes in several domains.
Pedestrian dynamics enables such analysis by simulating the trajectories of individual pedestrians in a crowd. Such movement is impacted by behavioral characteristics of humans, policy choices of decisions makers, and design decisions regarding the built environment. Furthermore, the impact of pedestrian dynamics is governed by application-domain models, such as infection spread models. Thus, input from a variety of science domains is required to produce comprehensive understanding for science-based decision-making.
This project includes a modular pedestrian dynamics code to which input from different domains could be easily included. In addition, it includes tools to democratize use by users with limited computing background by including a domain specific language to specify human behavior and policy. We also separately provide a recommender system, as a complementary project, to suggested suitable models to use.
Please look at our github pages for setup/running instructions and detailed documentation