Faeze Momeni Rad: Harnessing Advanced Tech for Safer Streets

A photo of UMD doctoral student Faeze Rad

A University of Maryland (UMD) researcher is exploring the use of advanced technologies, notably artificial intelligence and machine learning in combination with sensor systems, to improve the way we identify risks to pedestrians and cyclists.

Faeze Momeni Rad, whose advisor is UMD Professor and Center for Multimodal Mobility (CMMM) Director Cinzia Cirillo, has already gleaned significant findings from her hi-tech approach—findings that can help agencies such as the Maryland Department of Transportation and State Highway Administration implement measures to reduce traffic-related injuries and fatalities.

“We’re using regular and thermal cameras to observe how pedestrians cross the street, how vehicles respond, and whether there are potentially dangerous interactions between them,” Rad explained. “We then use AI-based methods to detect and analyze their movements and their behavior.”

The approach is a step forward from previous research methods that relied largely on post-crash data. Instead of retroactively identifying and analyzing the conditions that led to incidents, researchers like Rad can flag dangerous patterns ahead of time, allowing the agencies to take action before injuries or fatalities happen.

Her work, which is currently being submitted for publication, has yielded several notable insights. Her findings confirm the importance of speed management, which can be achieved through measures such as yield markings, raised crosswalks, curb extensions, speed feedback signs, and traffic-calming treatments.

She also found that driver and pedestrian behavior can vary among communities and be affected by social norms, suggesting a “one-size-fits-all” approach may not be effective.

More generally, Rad’s work demonstrates the efficacy of a framework that combines camera data and computing tools to proactively monitor safety. “The data extraction supports a combination of models, which allow us to adjust parameters in order to accommodate sufficient variation,” she said. “Together, these approaches offer transportation agencies a more comprehensive basis for understanding crossing activity and identifying behavioral patterns relevant to pedestrian safety assessments.”

A graduate of Sharif University of Technology and the University of Alberta, Rad says she was drawn to transportation engineering because of its impact on people’s daily lives. Even small improvements, she says, can make a big difference. Her broader interests in the field include intelligent transportation systems and autonomy.

Rad said she is grateful for the support for her work she has received from CMMM, the UMD Department of Civil and Environmental Engineering, and the Graduate School. That support includes a prestigious Ann G. Wylie Dissertation Fellowship, awarded to Rad this fall.

Published October 19, 2026