Transportation Research and Education Center

Roadside construction – be it a detour, a closed lane, or a slow weave past workers and equipment – work zones impact traffic flow and travel times on a system-wide level. The ability to predict exactly what those impacts will be, and plan for them, would be a major help to both transportation agencies and road users. Funded by the National Institute for Transportation and Communities, the latest Small Starts project led by Abbas Rashidi of the University of Utah introduces a robust, deep neural network model for analyzing the automobile traffic impacts of construction zones.

The top three causes of non-recurring traffic delays are crashes, work zones, and adverse weather conditions, with work zones accounting for 10% of all non-recurring delays. Precise work zone impact prediction could significantly alleviate fuel consumption and air pollution.

"Machine learning and deep learning are powerful tools to build different types of data and predict future situations. Using AI for analyzing data is the future of transportation engineering in general," Rashidi said.

The Utah Department of Transportation (UDOT) collects various types of data related to work zone operations. Working with these data, Rashidi and graduate research assistant Ali Hassandokht...

How can we use a variety of data-driven speed management strategies to make transportation safer and more efficient for all modes–whether you’re driving, walking or taking transit?

The project was led by Yao Jan Wu, director of the Smart Transportation Lab at the University of Arizona. Co-investigators were Xianfeng Terry Yang of the University of Utah, who researches traffic operations and modeling along with connected automated vehicles, and Sirisha Kothuri of Portland State University, whose research has focused on improving signal timing to better serve pedestrians. Join them on Sept 15, 2021 for a free webinar to learn more.

"We want to improve mobility for all users, be it pedestrians, vehicle drivers or transit riders, and there are different strategies to do this. How do we harness data to drive us to these strategies?" Kothuri said.

Funded by the National Institute for Transportation and Communities (NITC), this multi-university collaboration addressed the question from three angles:

• Wu and his students in Arizona looked at the impact of speed management strategies on conventional roadways...

Brandon Siracuse recently earned his master's degree in city and metropolitan planning from the University of Utah. While at the U of Utah he worked as a research assistant, earned a NITC scholarship, and served as an officer in the university's transportation student group, Point B. As of June 2021, he now works as a planner for the City of Council Bluffs, IA. Before enrolling in his master's program, Brandon worked as a full-time intern at the Washington University Medical Center Redevelopment Corporation in St. Louis, where he primarily did GIS work and data analysis to aid in the nonprofit organization's community planning efforts. Brandon is interested in land use, transportation, and the intersection of the two, and is particularly passionate about active transportation.

Connect with Brandon on LinkedIn

Tell us about yourself?

I am originally from Omaha, NE, and I have also had the great privilege of getting to live in St. Louis and Salt Lake City. I got my undergraduate degree in Environmental Science from Saint Louis University, and while I was there I realized I wanted to become an urban planner. I recently graduated from the Master of City and Metropolitan Planning program at the University of Utah, where I served as secretary and then treasurer...

Led by Xiaoyue Cathy Liu of the University of Utah (UU) and funded by the National Institute for Transportation and Communities, researchers have created a web-based modeling tool (see GitHub repository built for the Utah Transit Authority) that enables U.S. transit providers to explore the impacts of changing over their systems to electric buses*. The researchers ran the model for TriMet in Portland, OR as well, with TriMet results and analysis presented in the final report (PDF).

"The interactive visualization platform lets users explore various electric bus deployment budget scenarios, so that transit agencies can plan the most cost-effective way to transition their fleet from diesel to electric buses – while prioritizing disadvantaged populations," Liu said.

The research team, at University of Utah, Portland State University (PSU), and University of California, Riverside, set out to answer three questions: 

1. What costs and benefits are associated...