A Data Analytics Framework for the Application of Pedestrian Dynamics to Public Health
Abstract
The central hypothesis of this NIH funded project is that combining location-based service (LBS) data with pedestrian dynamics modeling can uncover movement patterns of people in complex situations with many public health applications. In Aim 1, we will develop an application-agnostic pedestrian dynamics modeling framework that assimilates LBS data. We will compare our approach to methods that do not utilize LBS in order to evaluate accuracy of human movement across multiple scenarios. In Aim 2, we will apply the pedestrian movement and interaction information to a variety of public health domains. These include: viral infection spread at local and global scales, enhancing walkability for active aging, and safe evacuation of the elderly. Finally, in Aim 3, we will translate our pedestrian dynamics modeling framework into public health practice. We will provide our platform to different stakeholders and obtain feedback on user satisfaction to improve the system design.
