Portland State University

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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...

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...

PRESENTATION ARCHIVE

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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,...

PRESENTATION ARCHIVE

• Watch the video
• View the presentation slides

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...