Cars waiting at a traffic signal
Apr 04, 2019
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 cars queue up at a stoplight, then gradually move again once the light turns green, incremental delays are introduced and compounded by this bottleneck. Exploiting new developments in communication, sensing and intelligent infrastructure systems, our opportunities...

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Pedestrians crossing a street
Mar 06, 2019
Principal Investigator: Kelly Clifton, Portland State University
Learn more about this research by viewing the Executive Summary and the full Final Report on the Project Overview page.

The latest NITC report offers improved tools for pedestrian modeling.

Led by Kelly Clifton of Portland State University, researchers had previously created the the MoPeD pedestrian demand model as well as a pedestrian index of the environment (PIE) for forecasting pedestrian travel. The PIE index improved the sensitivity of walk trip models by incorporating contextual features of the built environment that affect walking behavior in the Portland, Oregon region. Read about Clifton's previous body of work on context-specific modeling.

Useful for academic researchers in transportation, Clifton's research provides a framework for incorporating pedestrian travel behavior forecasts into traditional four-step travel demand models.

Since the method was based on Portland, the next step was to adapt the tools for wider use. In this new report, Clifton and...

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Dec 18, 2018
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 was led by Xianfeng (Terry) Yang, an assistant professor of civil and environmental engineering at the University of Utah, with...

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A red car travels along a highway
Nov 15, 2018
Principal Investigator: Liming Wang, Portland State University
Learn more about this research by viewing the one-page Executive Summary, 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 opportunities, like hopping into and out of self-driving taxis, the needs of the roadway system will inevitably change.

THE REGIONAL STRATEGIC PLANNING MODEL

The RPSM is a performance-based planning tool first developed by the Oregon...

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Sep 19, 2016

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Pricing and Reliability Enhancements in the San Diego Activity-Based Travel Model

The estimation of demand for priced highway lanes is becoming increasingly important to agencies seeking to improve mobility and find alternative revenue sources for the provision of transportation infrastructure.

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Jan 09, 2016

Sunday, the first day of the Transportation Research Board annual meeting in Washington, D.C., is workshop day. Portland State University doctoral student Tara Goddard presents in a showcase of research stemming from the prestigious Dwight D. Eisenhower Transportation Fellowship program.

Goddard probed the question of why so many bicyclists die in traffic crashes. Cyclists are 12 times more likely to be killed in a crash than a driver or passenger in a car. She wondered what role drivers' attitudes toward cyclists might play.

Goddard's research uses a survey to measure drivers' attitudes and self-reported behaviors and to test drivers' implicit attitudes toward both other drivers and cyclists. She pairs the survey piece with a lab experiment that uses hazard-perception video clips to examine whether drivers notice cyclists. 

By this approach, Goddard hopes to understand drivers' attitudes and whether those attitudes can predict how they act on the road. That understanding can potentially lead to steps to improve cyclist safety. Her workshop runs 9 a.m. to noon in Room 202B of the Walter E. Washington Convention Center.

Disaster recovery workshop

John MacArthur of TREC presents "Smart, Shared and Social: Enhancing All-Hazards Recovery Plans With Demand...

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Dec 04, 2015

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Nicholas Stoll, Graduate Research Assistant, Portland State University

Topic: Utilizing High Resolution Bus GPS Data to Visualize and Identify Congestion Hot-spots in Urban Arterials

The research uses high resolution bus data to examine sources of delay on urban arterials. A set of tools were created to help visualize trends in bus behavior and movement, which allowed for larger traffic trends to be visualized along urban corridors and urban streets. By using buses as probes and examining aggregated bus behavior, contoured speed plots were used to understand the behavior of roadways outside the zone of influence of bus stops. These speed plots can be utilized to discover trends and travel patterns with only a few days’ worth of data. Congestion and speed variation can be viewed by time of day and plots can help indicate delays caused by intersections, crosswalks, or bus stops.

This type of information is important to transit authorities looking to...

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Dec 04, 2015

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Steven Gehrke, Ph.D. Candidate, Portland State University

Topic: An Activity-related Land Use Mix Construct and Its Connection to Pedestrian Travel

Land use mix is a central smart growth principle connected to active transportation. This presentation describes the indicators of local land use mixing and their association with pedestrian travel in Oregon’s Willamette River Valley. It argues that land use mix is a multidimensional construct reflected by the complementarity, composition, and configuration of land use types, which is positively linked to walk mode choice and home-based trip frequency. Findings from this study underline the conceptual and empirical benefit of analyzing this transportation-land use interaction with a landscape pattern measure of activity-related composition and spatial configuration.

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