National Institute for Transportation and Communities

"Transformative Transportation Survey Methods: Enhancing Household Transportation Survey Methods for Hard-To-Reach Populations," is a new article published in the September 2021 issue of Transportation Research Part D. It was co-authored by Amy Lubitow, a sociology faculty member at Portland State University, Erika Carpenter, a sociology graduate student, and Julius McGee, a faculty member in urban studies and planning.

The study explores the challenges that hard-to-reach populations face in completing household activity surveys. Researchers drew on qualitative data from hard-to-reach populations regarding the limits of the Oregon Household Activity Survey and found evidence that the survey methods lack social, cultural, and linguistic applicability for Black, Indigenous and other people of color, as well as low-income populations. The authors argue that Oregon’s household travel survey prioritizes certain ways of understanding and experiencing mobility that are, by default, exclusionary. The article concludes in sharing insights regarding how transportation professionals might ...

We're proud to announce the publication of a new NITC dissertation: "Modeling Capacity: Multiple Weaving Areas," by Sheida Khademi of the University of Texas at Arlington.

"Traffic congestion on freeway systems is one significant concern in urban areas throughout the U.S.A. In this era, building new freeways to reduce congestion is less feasible due to the high capital and social costs. Thus, the effective management and operation of existing freeway facilities has become a preferred approach to reduce traffic congestion. Using the outcomes of this research, agencies can get an idea of which effective variable they should control to manage freeways' multiple weaving areas more efficiently; obtaining the highest capacity while planning for existing freeways. The results will be presented to DOTs and MPOs. The model would be highly useful and money-saving for these agencies, as they prefer to obtain higher capacity by managing existing freeways rather than buying right of ways," Khademi said. 

Systematic, well-designed research provides the most effective approach to the solution of many problems facing highway administrators and engineers. Traffic congestion on freeway systems is one significant concern in urban areas throughout the U.S.A. In this era, building new freeways to...

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...

Led by Dr. Stephen Fickas of the University of Oregon (UO),  transportation researchers are working to give bicyclists smoother rides by allowing them to communicate with traffic signals via a mobile app. 

The latest report to come out of this multi-project research effort introduces machine-learning algorithms to work with their mobile app FastTrack. Developed and tested in earlier phases of the project, the app allows cyclists to passively communicate with traffic signals along a busy bike corridor in Eugene, Oregon. Researchers hope to eventually make their app available in other cities.

"The overall goal is to give bicyclists a safer and more efficient use of a city’s signaled intersections. The current project attempts to use two deep-learning algorithms, LSTM and 1D CNN, to tackle time-series forecasting. The goal is to predict the next phase of an upcoming, actuated traffic signal given a history of its prior phases in time-series format. We're encouraged by the results," Fickas said.

Their latest work builds on two prior projects, also funded by the National Institute for Transportation a Communities: in which Fickas and his team successfully built and deployed a hardware and software product called ‘Bike Connect’ which allowed people on bikes to give hands-free advance...