Travel time reliability – or the consistency and dependability of travel times from day to day, and at different times of day – is a key metric that significantly affects people’s travel behavior. Since businesses rely heavily on transportation systems, an unreliable transportation network can also impact the economic competitiveness of urban areas. As such, reliable travel times are important for transportation agencies to promote economic stability within a community. Having accurate methods to evaluate reliability is important for both transportation practitioners and researchers.

A new report from Portland State University offers an improved method for determining the confidence interval of travel time reliability metrics. Researchers Avinash Unnikrishnan, Subhash Kochar and Miguel Figliozzi of PSU’s Maseeh College of Engineering and Computer Science used a highway corridor in Portland, Oregon as a case study to evaluate their method, and found that it compared favorably with other methods of evaluating the confidence interval of travel time reliability metrics.

"Traffic engineers can apply this method to come up with a range of estimates for the unknown true travel time reliability metric. The travel time reliability metrics calculated by traffic engineers and transportation planners will have variability due to factors such as road...

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NITC researchers Anne Nordberg, Jaya Davis, Stephen Mattingly, Sarah Leat and Mansi Patel of the University of Texas at Arlington have published two new journal articles related to their NITC project, Optimizing Housing and Service Locations to Provide Mobility to Meet the Mandated Obligations for Former Offenders to Improve Community Health and Safety. Read about the original study here, which focused on helping former offenders overcome transportation challenges to reintegrate into society.

The two articles, published in Mobilities and the International Journal of Offender Therapy and Comparative Criminology, disseminate the NITC study among different audiences and disciplines; highlighting the need to address transportation and complex social issues through more than one lens.

In the November 2021 issue of Mobilities, "Towards a Reentry Mobilities Assemblage: An Exploration of Transportation and Obligation Among Returning Citizens," the authors investigated the mobility needs of returning citizens from the perspective of service providers and employers in Dallas, Texas. They interviewed 17 participants who directly served returning citizens in their professional...

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Are e-scooters just the first sign of a shared-mobility revolution? If they are, then researchers at the University of Arizona intend to make sure that the emerging transportation system has functional models on par with other modes of transportation. In 2018, approximately 100 U.S. cities had already launched shared e-scooter programs, accounting for 38.5 million trips. However, the models to manage e-scooter sharing are only recently being developed. In a project funded by the National Institute for Transportation and Communities (NITC) and led by Dr. Jianqiang Cheng, the research team set out to develop data-driven, decision-making models for shared-mobility system design and operation in Tucson, Arizona.

"The decision making process for e-scooter companies is complex. One of the first questions is where to locate the scooters – In the transportation network, where do e-scooters need to be placed to meet demand? The second question is how to distribute them. It gets more complicated when you introduce different electric charging methods, so that some scooters are being collected by paid contractors and others are being charged by customers, through incentives," Cheng said.

As the researchers see it, the main benefits of shared mobility are threefold:

  1. ...
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Imagine you've just been released from prison. You don't have a phone yet, or a car, but through your reentry service, you are set up for now with a place to stay. They also got you a job interview for next Monday, but it's across town. You also have mandatory mental health, medical health, and parole-related appointments to make it to this week, so right now— transportation is your biggest problem. You have three complementary bus tickets, and you need to figure out the best way to use them.

"I can't imagine trying to navigate my way through a city, tackle the bus system and find my way around without a smartphone - in a community that I haven't been in for ten, twenty, however many years," said Dr. Stephen Mattingly. 

That's the scenario facing roughly 2,000 former inmates who return to communities every day in the U.S. 

To help them to reintegrate into society, researchers Anne Nordberg, Jaya Davis and Stephen Mattingly of the University of Texas at Arlington (UTA) leveraged funding from the National Institute for Transportation and Communities (NITC) on ...

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Intelligent Transportation Systems (ITS) need traffic data to run smoothly. At intersections, where there is the greatest potential for conflicts between road users, being able to reliably and intelligently monitor the different modes of traffic is crucial.

The Federal Highway Administration (FHWA) estimates that more than 50 percent of the combined total of fatal and injury crashes occur at or near intersections. For pedestrians the intersection is a particularly dangerous place: the City of Portland, Oregon identified that two-thirds of all crashes involving a pedestrian happen at intersections. And when darkness comes earlier in fall and winter, crashes increase dramatically. So knowing what's going on in low-visibility conditions is essential for mobility and safety of all road users.

Some agencies use cameras to monitor traffic modes, but cameras are limited in rainy, dark or foggy conditions. Some cities use radar instead of cameras, which works better in low-visibility but typically can't provide as rich a picture of what's going on. Conventional radar gives movement and position data for all...

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Roadside construction – be it a detour, a closed lane, or a slow weave past workers and equipment – work zones impact traffic flow and travel times on a system-wide level. The ability to predict exactly what those impacts will be, and plan for them, would be a major help to both transportation agencies and road users. Funded by the National Institute for Transportation and Communities, the latest Small Starts project led by Abbas Rashidi of the University of Utah introduces a robust, deep neural network model for analyzing the automobile traffic impacts of construction zones.

The top three causes of non-recurring traffic delays are crashes, work zones, and adverse weather conditions, with work zones accounting for 10% of all non-recurring delays. Precise work zone impact prediction could significantly alleviate fuel consumption and air pollution.

"Machine learning and deep learning are powerful tools to build different types of data and predict future situations. Using AI for analyzing data is the future of transportation engineering in general," Rashidi said.

The Utah Department of Transportation (UDOT) collects various types of data related to work zone operations. Working with these data, Rashidi and graduate research assistant Ali Hassandokht...

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A bus in Utah
Photo by KuntalSaha/iStock
Reid Ewing, University of Utah

Conventional four-step travel demand modeling is overdue for a major update. The latest NITC report from the University of Utah offers planners better predictive accuracy through an improved model, allowing for much greater sensitivity to new variables that affect travel behavior. Specifically, it accounts for varying rates of vehicle ownership, intrazonal travel, and multimodal mode...

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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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Pedestrians crossing a street
Principal Investigator: Kelly Clifton, Portland State University
Learn more about this research by viewing 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 Jaime Orrego-Onate of...

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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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