# A data analytics framework for the application of pedestrian dynamics to public health

> **NIH NIH R15** · EMBRY-RIDDLE AERONAUTICAL UNIVERSITY · 2021 · $394,525

## Abstract

Project Summary: The 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. However, conventional
models either ignore human movement or rely on coarse-scale features of human movement, which limits the
types of public health interventions that can be attempted. 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.
 Particle dynamics provides a modeling methodology to study movement and interactions from the cellular to
the population level. Our long-term goal is to create a data-driven computational infrastructure for particle
dynamics, targeting public health applications. The objective of this proposal is to develop and evaluate an
application-agnostic pedestrian dynamics model, which considers each human to be a particle. Here, we will
simulate the fine-scaled movement of people in several critical public health contexts. In preliminary work, we
have shown that such models can accurately simulate fine-scale movement patterns on a small-scale, yielding
effective policies to reduce infection spread while boarding planes and moving in queues. However, directly
scaling this to more complex situations, such as an entire theme park, is difficult due to inherent variability in
human movement patterns. In this proposal, we will apply novel data sources, such as location-based services
(from cell phone apps) to augment such models.
 Our central hypothesis 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 i...

## Key facts

- **NIH application ID:** 10114050
- **Project number:** 1R15LM013382-01A1
- **Recipient organization:** EMBRY-RIDDLE AERONAUTICAL UNIVERSITY
- **Principal Investigator:** Sirish Namilae
- **Activity code:** R15 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $394,525
- **Award type:** 1
- **Project period:** 2021-08-05 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10114050

## Citation

> US National Institutes of Health, RePORTER application 10114050, A data analytics framework for the application of pedestrian dynamics to public health (1R15LM013382-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10114050. Licensed CC0.

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