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...
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Connected Vehicles Illustration showing icons of wifi over a road
Image by metamorworks/iStock
Xianfeng Yang, University of Utah; Mingyue Ji, University of Utah

Now that we are decades into the Age of Information, it's increasingly important to minimize the age of information: that is, to make sure the information we have is the very latest.

In the world of connected vehicle technology, Age of Information (AoI) is a concept that was introduced in 2012 to quantify the “freshness” of knowledge...

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A worker measures the distance from a bike light to the ground
 

NITC researchers Stephen Fickas and Marc Schlossberg of the University of Oregon are on a mission: bring the benefits of V2I (vehicle-to-infrastructure communication) to bicycling. Earlier this year they published their proof-of-concept of a DIY vehicle-to-infrastructure "bike box" in Oregon for communicating with traffic signal controllers. In the most recent round of NITC grants awarded this past summer they secured funding for Green Waves, Machine Learning, and Predictive Analytics: Making Streets Better for People on Bike & Scooter.

APPLYING GLOSA TO CYCLING

The latest report to come out of this body of work focuses on a new technology being integrated into modern cars: GLOSA, or Green Light Optimized Speed Advisory. GLOSA allows motorists to set their speed along corridors to maximize their chances of catching a "green wave" so they won't have to stop at red lights. This project demonstrates how GLOSA can be used by bicyclists in the same way it is used by motorists, with a test site on a busy car and bike corridor feeding the University of Oregon campus: 13th Avenue in Eugene, Oregon.

Fickas and Schlossberg created a...

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Bicyclists cross an intersection with a bike signal, near a red car
John MacArthur, Portland State University

What if your bicycle could warn you that a car is coming from a side street you can't see? Or let you know that your front tire is getting a little low, or that you're approaching a pothole that wasn't there yesterday? A NITC research project led by John MacArthur of Portland State University explores how connected vehicle (CV) technologies could encourage an increase in bicycling. As CV technology moves forward in the rest of the transportation system—with buses and connected streetcars requesting early green lights from the traffic signals, and cars chatting with each other...

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Cars waiting at a traffic signal
Photo by Canetti
Principal Investigator: Gerardo Lafferriere, Portland State University
Learn more about this research by viewing the Executive Summary and the full Final Report on the Project Overview page.

Automobile traffic congestion in urban areas comes with significant economic and social costs for everyone. According to the 2015 Urban Mobility Report, the total additional cost of congestion was $160 billion. As more people move to metropolitan areas, the problems only intensify. The latest NITC report offers a new approach to urban traffic signal control based on network consensus control theory which is computationally efficient, responsive to local congestion, and at the same time has the potential for congestion management at the network level.

Traffic signals represent a significant bottleneck. As...

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Cyclists cross the road at a bike signal
Investigators: Stephen Fickas, University of Oregon; Marc Schlossberg, University of Oregon
Learn more about this research by viewing the Executive Summary and the full Final Report on the Project Overview page.

Most people who bike for transportation can probably think of "that one intersection:" The light where it's impossible to get a green without waiting. Even when there are no cars, pedestrians or other bikes in sight, you still know you'll have to stop and wait a while, sacrifice all your momentum, and wish you could have given the signal advance notice that you were coming.

Researchers at the University of Oregon have created an app for that.

Lead investigator Stephen Fickas, a computer and information science professor at the UO, developed the app, along with a specially-designed Bike Connect ‘box' (watch the 3-minute video) that attaches to a traffic signal controller. With the box installed, the app allows a cyclist to...

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Photo by anyaberkut - Thinkstock Photos
Principal Investigator: Xianfeng (Terry) Yang, University of Utah
Learn more about this research by viewing the Executive Summary and the full Final Report on the Project Overview page, or sign up for the free January 24th webinar.

It can be expected that automated vehicles and human-driven vehicles will coexist in the transportation network for quite some time. In order to support various traffic control tasks it is critical to develop a reliable model to understand the real-time traffic patterns in this mixed environment. A new report from the National Institute for Transportation and Communities (NITC) contributes three new tools to help planners model freeway traffic with both connected automated vehicles (CAVs) and human-driven vehicles (HVs). 

RESEARCH TEAM

The project...

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A red car travels along a highway
Photo by Felix Tchverkin on Unsplash
Principal Investigator: Liming Wang, Portland State University
Learn more about this research by viewing related publications, open-source data, and the full Final Report on the Project Overview page.

The latest report from The National Institute for Transportation & Communities (NITC) offers help to planners seeking to incorporate emerging travel modes—including car sharing, bike sharing, ride hailing, and autonomous vehicles—into regional travel demand models. More specifically, it brings these new travel modes into the Regional Strategic Planning Model (RSPM) tool. As more people start taking advantage of new...

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If you weren’t one of the 10,000 people who attended the Transportation Research Board’s Annual Meeting in January, there are fifty students and twenty faculty for PSU, UO, OSU and OIT who can tell you what they learned there.  OTREC's bright yellow lanyards made our presence especially visible! PSU student Brian Davis blogged about his experience, OTREC’s Jon Makler was interviewed in a local newspaper, and the Oregon “delegation” at the conference was covered by both local and national blogs. Team OTREC filed some daily debriefs, highlighting presentations on topics such as federal stimulus investments in Los Angeles and Vermont’s efforts to address their transportation workforce crisis with returning military veterans (as well as the...

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Connected Vehicles and Rural Road Weather Management

Changing weather patterns and increases in extreme weather events has led to the deployment of more weather responsive traffic management strategies. As the transportation system moves towards a connected vehicle environment, questions arise as to how connected vehicle technology can support weather responsive systems. The presentation will discuss the use of connected vehicles in a rural environment as providers of mobile weather data. Two projects will be...

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