Drexel University is leading a team of experts from New Mexico State, The Ohio State University and the University of North Dakota to what should - and should not - be automated in UAS and how to display critical information to UAS crews. The University of North Dakota and New Mexico State will then research how to train and certify UAS crew based on these requirements. This research will help the FAA set standards for how critical functions are automated in a UAS ground station and how UAS operators receive vital flight data. It will also help the FAA decide how to certify and train UAS crew.

Using driving scenarios and a driving simulator, where the driver interacts with a voice-driven email app that systematically controls cognitive load, the project team has collected 50 sessions (50 different drivers, about 25 minutes each), containing user distraction. The project team is formatting this database to make it publicly available. After the demo at the University Transportation Center's (UTC�)s recent DC safety session, the team has seen interest in obtaining the database from transportation colleagues. Last year the project team identified two specific phenomena that they found in distracted driver's speech: fillers (ums, and ers), and hesitations (the amount of silence). The team built classifiers that automatically detect a speaker�s distraction based on those aspects of their speech (and without the linguistic content, the words they said). These detectors have been recently integrated into a Yahoo email reader platform (the Yahoo InMind agent) as an android app. The detection has been demonstrated to Yahoo and at conferences. It will be integrated in the Yahoo news reader app within the next two months. This use of the detector informs the driver by gracefully shutting down an app when distraction has been detected and restarting where it left off when it is safe to do so.

Approximately 60 percent of fatalities on the nation's roadways are the result of lane departure crashes. In some cases, the vehicle crossed the centerline and was involved in a head-on crash or opposite direction sideswipe. In others, the vehicle left the roadway to roll over or impact one or more natural or man-made objects, such as trees, utility poles, bridge walls, embankments, or guardrails. A variety of transportation engineering solutions have been proposed to mitigate the occurrence of lane departure crashes including but not limited to: the safety edge, nighttime visibility, rumble strips, retroreflectivity, and pavement lane markings. While these strategies have shown varying degrees of promise in particular contexts, they do no immediately address all of the causal factors inherent in road users (motor vehicle and all-terrain vehicle operators) such as fatigue, operating under the influence, distraction driving, etc. There is a critical need to raise the awareness of the traveling public in the Pacific Northwest about the risks regarding lane departure crashes and how behaviors can mitigate their occurrence. The economic impact of these crashes needs attention so as to help prioritize alternative investments in such transportation engineering solutions. Essentially, users need to understand the benefits and costs of alternative programs.

Transportation agencies are turning to smarter ways with a variety of new technologies to improve travel experience and to manage the transportation system more efficiently. Over the last decade, new technologies and innovative transportation systems produce big data, which has tremendously enriched ways to monitor and manage the transportation systems. A challenging question is, how do we make the best use of the big data to design and operate our transportation system? In addition, those data sources are usually established by disparate public agencies and private sector. They rarely communicate with each other and as a result, each part of the transportation systems is individually operated and clearly, the entire transportation system is far from being socially optimal. Integrating and learning the big data are the keys to success of smarter transportation systems, which consist of the following three components. (1) Integration of various data sources; (2) Understanding of integrated data; and (3) Optimal decision making for systems management and for individual travelers. A mobility data analytics center is necessary to accommodate needs of data fusion and analytics. The ultimate objective of mobility data analytics center is to; (1) Provide archived and real-time traffic data of every element of multi-modal transportation systems; (2) Reveal the behavior information for both passenger transportation and freight transportation; (3) Serve as a key managerial instrument for legislators, transportation planners, researchers, and engineers; (4) Serve as a key information platform for individual travelers and transportation industries. This research serves as a first stage of developing the mobility data center. The project will focus on integrating and learning large-scale transportation data sets that are immediately available to collect and the most promising from public agencies and travelers. The City of Pittsburgh was chosen as a demonstration city. Pittsburgh has been successful in moving towards smart cities and smart transportation systems, with ample resources for us to collect and work with large-scale citywide data.

Drexel University is leading a team of experts from New Mexico State, The Ohio State University and the University of North Dakota to what should - and should not - be automated in UAS and how to display critical information to UAS crews. The University of North Dakota and New Mexico State will then research how to train and certify UAS crew based on these requirements. This research will help the FAA set standards for how critical functions are automated in a UAS ground station and how UAS operators receive vital flight data. It will also help the FAA decide how to certify and train UAS crew.

Concrete repairs for bridge and pavements require high early strength such that traffic can be resumed as soon as possible. Currently, this is often achieved, by simply adding cement to a standard concrete mixture. This can have a detrimental effect on the repair because the higher cement content will inevitably lead to higher shrinkage. This has resulted in higher observed cracking and overall lower repair durability. In recent Utah Department of Transportation (UDOT) projects, internal curing (saturated lightweight aggregate) has been used to reduce shrinkage and improve overall concrete durability for bridge decks and pavements. Concrete mixtures used for repairs can also benefit from this technology, but must maintain a high early strength so operations may be resumed as soon as possible. Furthermore, increasing the concrete creep properties, while also decreasing the shrinkage, will result in further crack mitigation and ultimately durability. The primary objective of this research project is to develop concrete mixtures that exhibit favorable time-dependent properties to mitigate the potential for cracking in UDOT infrastructure repairs. Secondary objectives of this research project are to develop a set of material tests that can qualify future concrete designs for use in UDOT repairs. Major Tasks include: (1) Literature review: Investigate admixture performance to achieve the objectives of a high early strength and creep, low shrinkage concrete. Preliminary literature review has shown saturate lightweight aggregate and blast furnace slag are ideal candidates. (2) Laboratory Mixtures: Using results from the literature review, develop a test matrix for development of the candidate mixtures and perform tests. Laboratory testing will use ASTM standards (e.g., compressive creep, shrinkage, strength, modulus and freeze thaw and more) and customized testing (shrinkage, tensile creep and restrained shrinkage). (3) Small Scale Mock-ups: Using the best performing mixtures from Task 2 and working with UDOT engineers, identify situations of past poor performance and fabricate concrete repair situations at Utah State University. These mock-up repairs will be monitored for overall performance through the duration of the project and beyond if requested.