Applying Data-Driven Multimodal Speed Management Strategies for Safe, Efficient Transportation

Aug 03, 2021

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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Bicyclists cross an intersection with a bike signal, near a red car

If Bikes Could Talk: Would Connected Vehicle Tech Get More People Riding?

Sep 26, 2019
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

Exploring New Solutions to Urban Congestion Through a Network of Traffic Signal Controls

Apr 04, 2019
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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A red car travels along a highway

Where Do Emerging Travel Modes Fit Into Regional Models?

Nov 15, 2018
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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TRB, In Case You Missed It

Feb 25, 2011

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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200th Seminar! Travel Time Estimation for Traffic Management and Traveler Information

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The video begins at 7:41.

Connected Vehicles and Rural Road Weather Management

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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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Webinar: Modeling Freeway Traffic in a Mixed Environment: Connected and Human-Driven Vehicles - Terry Yang

Webinar: Modeling Freeway Traffic in a Mixed Environment: Connected and Human-Driven Vehicles

 

PRESENTATION ARCHIVE

Miss the webinar or want a look back?

OVERVIEW

Although connected vehicles (CVs) will soon go beyond testbeds, CVs and human-driven vehicles (HVs) will co-exist over a long period. Hence, it is critical to consider the interactions between these two types of vehicles in traffic flow modeling. In this study, we aim to develop a macroscopic model to understand how CVs would impact HVs in the traffic stream. Grounded on the second-order traffic flow model, we study the relationships among flow, density, and speed by two sets of formulations for the groups of CVs and HVs, respectively. A set of friction factors, which indicate CVs' impact to HVs, are introduced to the speed equation for accounting CV speed impacts. Then extended Kalman Filter is employed to update both model parameters and friction factors in real-time. By using CVs trajectory data as measurements, the difference between CV average speed and overall traffic mean speed will be fully accounted. The proposed model will serve as a basis for designing CV-based traffic control function,...

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Webinar: Letting Bike Riders Catch the Green Wave

PRESENTATION ARCHIVE

Miss the webinar or want a look back?

OVERVIEW

The "Fast Track" project at the University of Oregon focuses on a mode of transportation that is sometimes left out of vehicle-to-infrastructure, or V2I, conversations: Bicycling. NITC researchers developed an app based 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. Researchers developed a smartphone app that tells a cyclist whether they should adjust their speed to stay in tune with the signals and catch the next green. The project demonstrates how university researchers, city traffic engineers, and signal-controller manufacturers can come together to help bicyclists be active participants in a smart transportation...

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Connected Vehicle System Design for Signalized Arterials

Webinar: Connected Vehicle System Design for Signalized Arterials

 

PRESENTATION ARCHIVE

OVERVIEW

It can be expected that connected vehicles (CVs) systems will soon go beyond testbed and appear in real-world applications. To accommodate a large number of connected vehicles on the roads, traffic signal control systems on signalized arterials would require supports of various components such as roadside infrastructure, vehicle on-board devices, an effective communication network, and optimal control algorithms. In this project, we aim to establish a real-time and adaptive system for supporting the operations of CV-based traffic signal control functions. The proposed system will prioritize the communication needs of different types of CVs and best utilize the capacity of the communication channels. The CV data sensing and acquisition protocol, built on a newly developed concept of Age of Information (AoI), will support the feedback control loop to adjust signal timing plans.

Our multidisciplinary research team, including researchers from transportation engineering and electrical engineering, will carry out the project tasks along four directions that capitalized on the PIs’ expertise:

  1. Data collection and...
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