Re-Imagining public safety for large crowd events

Austin, TX
University of Texas, Austin



AI/ML, AV/AR, Data Analytics, Data Collection, Data Privacy, Public Safety, User Research

Procurement Method:

University Partner:

University of Texas, Austin

Partnership Description

The City of Austin will be working with S.Craig Watkins (Professor and Director of the Institute for Media Innovation), Sherri R. Greenberg (Professor of Practice), Deepak Chetty (Assistant Professor of Practice), and William Spelman (Professor) from the University of Texas, Austin. The team has proposed a feasibility study of new crowd-data protocols and scenarios to mitigate the impact of bias in crowd management and of VR training modules that support public safety staff in reducing implicit bias in operations. The research will explore the implications of implicit bias in the collection, management and analysis of crowd data and the design of training modules. This partnership is part of the research team’s larger work in the interdisciplinary UT Good Systems initiative, read more about it here:


The City of Austin is seeking a safe, low risk and cost effective solution to increase the safety of large crowd events for our residents, public safety, other city staff, and visitors.


Pre-COVID pandemic, The City of Austin has been home to large-scale events such as South by Southwest, Austin City Limits Music Festival, etc. Public safety and government staff have found organizing and managing events of large sizes to be confusing and chaotic. 

Recent public demonstrations around the country and in Austin about policing have further magnified the need to help our public safety organizations improve engagement with the public during large public events. The City of Austin has embarked on a 're-imagining public safety' initiative.  As part of that effort, we would like to explore how to better train public safety to respond safely and effectively with our residents and guests. 

There are three types of large events to consider: (1) Structured (2) Unstructured (3) Hybrid. Each of those events requires different considerations and plans from city staff. 

The issues that large events can create:

  • Confusion due to the multitude of city stakeholders including these core members: Police Department, Fire Department, EMS, Transit Department, Economic Development Department. Larger events entail even more additional staff from Austin Code, Public Health, Resource Recovery, and Development Services Department
  • An unbalanced ratio between city employees and attendees
  • Misunderstanding amongst city staff on the public safety department approaches given limited context

The City of Austin seeks to better understand:

  • Crowd science and management
  • The existing collected crowd data and how it can help mitigate high-risk events while maintaining safety for all
  • Biases in the data
  • How to model different scenarios where bias exists so we can incorporate it into our training
  • How to recreate high-risk situations for training staff to appropriately respond 

The academic team would have access to Austin's: 

  • Public safety organizations, first responder teams, policymakers, event staff members, City Attorney's office, 
  • Existing technology, data analysts, and technology team
  • Previous EMS AR/VR training modules that have been created
  • Existing crowd data 


Requirements & Outcomes

The city of Austin would like to achieve the following outcomes: 

In the short term:

  • identify/co-create descriptions of crowd scenarios
  • identify experimentally valid numbers of study participants
  • design/choose hypotheses to test
  • choose success metrics
  • implement initial training sessions
  • public safety is enthusiastic about this training
  • short term training results
  • positive user experience during training

In the long term:

  • transform how public safety can work
  • become a leader in reimagining public safety
  • increase morale for the public safety department
  • improved relationships between public safety officers, residents, and city staff
  • quantifiable metrics on the impact of our efforts

STIR Labs Research Challenges Published

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