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...

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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 visualization of trips entering and exiting Salt Lake City
Nikola Markovic, University of Utah

The University of Utah has a new data visualization service to offer to state DOTs and other agencies. Using Small Starts funding from the National Institute for Transportation and Communities (NITC), researcher Nikola Markovic and his team have developed a suite of visual analysis tools to demonstrate how GPS trajectory data can help accurately model and analyze mobility trends. These data are typically purchased from...

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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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The Portland Streetcar
Principal Investigators: Kristin Tufte, Portland State University; Larry Head, University of Arizona
Project Overview: NITC Connected Vehicle Platform / Connected Streetcar Project (pending name change)

Learn more about this and other "Smart Cities" technology by registering for this September 14 workshop.

Connected Vehicle (CV) technology is coming to Portland, Oregon. We're excited to announce the first step in what could be a long-term game changer for the city: during the winter of 2018, researchers from Portland State University and University of Arizona will work with the City of Portland to deploy a test concept of CV tech on the Portland Streetcar.

Primarily funded by the National Institute for Transportation and Communities (NITC), the Connected Streetcar Project is one of the Portland...

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