National Institute for Transportation and Communities

The National Institute for Transportation and Communities (NITC) is proud to introduce a new Dissertation Fellow, Adrian Cottam of the University of Arizona. Cottam's doctoral research project, Machine Learning and Big Data-Based Approaches for Quality Freeway Volumes, will focus on improving the quality of freeway volumes and expanding their spatial availability.

"I was drawn to Transportation Engineering because it is a field where you can make major impacts to better your community. I found that by merging my passion for Computer Science with Transportation Engineering, I could use data to obtain more information on how local transportation could be improved through Intelligent Transportation Systems (ITS) and performance measurement. Throughout this process, I found that it was frequently a challenge obtaining high-quality data, or having access to data where you need it. This drove me to focus my dissertation on using machine learning techniques to improve data quality, and estimate traffic parameters where sensors were unavailable. My research is specifically focused on freeways, and seeks to use available data sources, such as crowdsourced data, to estimate data that isn’t always available. I am a firm believer in applied research, and hope to better my community through my research efforts. As such, I seek to develop methods that can be applied in a practical way, so...

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...

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. ...

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...