How engineers are reimagining street safety for communities of all kinds

Traffic crashes remain one of the leading causes of injury and death across communities of all sizes—from dense urban corridors to rural and tribal lands. A central challenge in transportation safety is the inability to anticipate risks before they become deadly. Strengthening that foresight would allow communities to intervene earlier—preventing crashes rather than reacting to them.

Researchers at the University of Utah are tackling this challenge head-on with two newly awarded federal grants that bring advanced artificial intelligence (AI), vehicle-to-everything (V2X) technology, and real-time sensing into transportation safety planning and decision-making.

Just before Christmas Eve, the U.S. Department of Transportation (USDOT) announced nearly $1 billion in funding through its highly competitive Safe Streets and Roads for All (SS4A) grant program. The University of Utah will lead one major award, as well as play a large role in a second project, awarded to support safer streets across Utah and California.

To achieve their goals, both projects will focus specifically on multimodal safety data collection, predictive sensing demonstrations, and real-time alert systems designed to inform future safety investments and policy decisions.

Designing Streets That Think Ahead: AI and V2X Powering Predictive Technologies

The University of Utah-led project, “Integrated AI Computing and V2X Infrastructure Systems for Safer Streets,” was awarded $3.99 million in federal funding.

The project’s inventiveness stems from the perceptive shift from reactive safety measures to focusing instead on how we can create predictive technologies that could provide real-time warnings. By integrating AI-powered computing with connected infrastructure, these tools will support the development and update of Comprehensive Safety Action Plans—a cornerstone of the SS4A program—while also demonstrating how emerging technologies can improve outcomes for drivers, pedestrians, cyclists, and transit users.

The University of Utah-led SS4A project is being steered by Cathy Liu (PI) and Chenxi Liu (Co-PI)—both professors in the Department of Civil & Environmental Engineering (CvEEN)—in partnership with public agencies including Utah Department of Transportation, Wasatch Front Regional Council, Salt Lake City, Garfield County, the Trinidad Rancheria, industry partner AIWaysion, and university collaborators at Johns Hopkins University and Rensselaer Polytechnic Institute.

Work will span diverse communities and various transportation settings, including:

• Salt Lake City
• Garfield County
• The Trinidad Rancheria

Collaborating on Safety Solutions for Tribal Lands: Protecting Lives Through Real-Time Hazard Detection

The University of Utah is also a key partner on a second SS4A award led by the Cher-Ae Heights Indian Community of the Trinidad Rancheria in California. That project, “Advancing Vision Zero on Tribal Lands through Real-Time Hazard Detection and Behavior Monitoring,” received $1.6 million in funding.

This three-year effort focuses on improving safety along Scenic Drive and five locations on U.S. Highway 101, where landslide risk and traffic behavior pose serious safety challenges. The project will deploy:

• AI-enabled geotechnical sensors
• AI-driven landslide alerts
• Multimodal traffic sensing
• Intelligent roadside warning systems

Data from these systems will feed into a centralized platform to evaluate outcomes and guide updates to the Tribe’s Comprehensive Safety Action Plan. Represented by CvEEN faculty members Chenxi Liu (PI), Tong Qiu (Co-PI), and Cathy Liu (Co-PI), the University of Utah will serve as a subcontractor on this project.

Research Leadership and Collaboration

Together, these projects highlight how interdisciplinary research, strong community partnerships, and emerging technologies can reshape how transportation agencies understand risk—and take action—to save lives.

Solving the Transportation Problem for Microreactors with Digital Twin

Microreactors are quickly shifting from sci-fi-adjacent prototypes to one of the most promising tools in America’s clean-energy toolkit. Roughly the size of a shipping container, these next-gen nuclear systems can deliver reliable power to remote communities, military bases, disaster zones, or industrial sites—without the massive infrastructure required by traditional nuclear plants.

But there’s one major challenge that needs to be solved before the full extent of this potential can be unlocked: how do you safely move a nuclear reactor down a highway?

It’s a question the nation is racing to answer. Microreactors offer extraordinary opportunities—portable clean energy, rapid deployment, and resilient power—but they also demand new approaches to safety, monitoring, and transportation. Ensuring the public, regulators, and industry have full confidence in this technology means rethinking how engineers track and validate reactor conditions in real time.

That’s where researchers at the University of Utah are making headway.

U Team Wins ARPA-I Ideas and Innovation Challenge Stage 1

A University of Utah Civil & Environmental Engineering team has been named among the Stage 1 winners of the ARPA-I Ideas and Innovation Challenge, a national competition pushing forward transformative transportation technologies.

The U’s winning project, “Energy in Motion: Atoms on Wheels – Safe, Monitored Transportation of Microreactors,” unites an interdisciplinary group led by Assistant Professor Vince Wang, Professor Cathy Liu, and Dr. Ted Goodell, Director of the University of Utah Nuclear Reactor Facility.

This project represents a rare fusion of transportation engineering expertise and nuclear engineering innovation—showcasing the full strength of the CvEEN Department, which includes the Utah Nuclear Engineering Program (UNEP).

A First-of-Its-Kind Digital Twin for Nuclear Transport

As microreactors move from design labs to highways, the team is developing the nation’s first digital twin built specifically for transporting microreactors by semi-truck. This virtual model integrates real-time sensing, predictive analytics, and secure data flows to enhance public safety and bolster regulatory trust.

The digital twin will track:

• Radiation and shielding performance
• Vessel and environmental temperature
• Weather and road conditions
• Potential hazardous conditions

Instead of relying on static, offline estimates, trainees and operators will be able to access live, interactive, and actionable data during training or transport—while sensitive commercial information remains protected through NDAs and University legal mechanisms.

Over the course of a two-phase development plan—which in sum will occur over 3 years and $320K in funding—key metrics will include reduced operator training time, improved safety insights for drivers, and enhanced monitoring capacity during transport.

By enabling safe, monitored, and scalable microreactor transportation, the project supports U.S. clean-energy goals and advances an emerging nuclear-technology sector.

The team will travel to Washington, D.C. this week to deliver their Phase 2 pitch.

The One-U Responsible Artificial Intelligence Initiative (One-U RAI) at the Scientific Computing and Imaging Institute (SCI) has selected a new cohort of faculty fellows — including Dr. Ryan Johnson from our Department of Civil & Environmental Engineering (CvEEN).

Johnson was recognized under the “Environment” thematic area for his work combining AI, hydroinformatics, and sustainability in water resources engineering.

Johnson’s research leverages AI to address water-resource challenges in the arid West, focusing on snow mapping, streamflow monitoring, and predictive modeling to inform water management.

“My work is driven by the need to integrate technical innovation with long-term sustainability,” he said. He uses AI-optimized monitoring stations, machine learning, and edge computing—processing data at potentially remote stations, as opposed to the cloud, to allow for real-time analysis.

Holding a PhD in civil and environmental engineering from the University of Utah, Johnson recently developed a hydroinformatics course that covers data science and computing while grounding students in responsible AI. As a fellow, he will develop auditable AI models for the Upper Colorado Basin—incorporating demographic data to prevent social or economic biases in decision-making—and create open-source tools to push his field forward.

With this fellowship, our department reinforces its commitment to sustainable infrastructure and water-resource resilience. We’re proud to support Ryan as he leverages cutting-edge AI to help secure water for communities across the West.

As part of the 2025 cohort of 12 One-U RAI Fellows selected campus-wide, five were from the Price College of Engineering — underscoring the College’s leadership in responsible AI research and application.

Learn more about the full cohort of One-U RAI Fellows across campus.

Ryan Johnson in the Hyrdaulics Lab.