Billy Williams Jr

The funds provided under this STRIDE proposal will support NCSU������������������s administration of the UTC grant, coordination with University of Florida and other STRIDE partner institutions, and support approved conference travel for students and faculty.

This project aims to help transport agencies use ����������������big data��������������� to help mitigate congestion and manage system performance, for both freeways and arterials (and especially arterials, which have seen less attention). Two investigatory objectives are planned. In the first, we will create and train an algorithm to spot the onset of incidents and recurring congestion, so that system managers can be more responsive. Our hypothesis is that early responses help reduce the impacts (the queues are shorter, disappear quicker, and create less delay). We will 1) fuse data such as real time traditional detector data, CV data, and other online data, to produce a significant and consistent data-stream of high volume and high velocity heterogeneous data; and 2) use deep reinforcement learning to train an AI-based algorithm to spot the onset of these events, distinguish between them, and generate response suggestions based on effective past system responses or modeling of the system with selected strategies. In the second investigatory thread, we will create a performance monitoring algorithm that uses policy-based targets (e.g., speeds of 45 mph or better during congested conditions) and ����������������big data��������������� technologies to help agencies improve the efficacy of their congestion mitigation efforts. We will use condition-based policy travel rates to simplify the inference process and ensure that performance management focuses on situations that have the greatest need. In both these efforts, we will produce analysis tools that practitioners can use (stand-alone, prototype software intended to be integrated into existing system management platforms); as well as guide books for the use of the algorithms; and a project report. In the first year we will develop the analysis procedures (e.g., congestion ����������������alarms��������������� and monitoring ����������������tools���������������); and in the second year, we will fine tune these algorithms and recommend real time strategies that use these tools to address impending or spreading congestion.

The NCDOT is launching a bold and forward-looking effort to establish multi-university transportation centers of excellence to provide broad-based, multidisciplinary research into the applications and impacts of cutting edge technologies and emergent, disruptive trends. The projects included in our center proposal were custom-built to address the research areas spelled out in the request for proposals for the desired Mobility and Congestion center. The three themes are as follows: ��������������� Theme #1: Big Data and Data-Driven Transportation Management and Decision Support ��������������� Theme #2: Active Transportation Management/Integrated Corridor Management ��������������� Theme #3: Transit and Mobility as a Service

Transportation state agencies and other public and private sector working have consistently identified workforce development on the top of the needed capabilities for the advancement and maturity of the programs. This realization has become even more with the advancement of emerging technologies and strategies and the establishment of associated programs. These technologies and strategies include connected and automated vehicles, Mobility as a Service, Micro-mobility, electric vehicles, business intelligence, big data, and other such services. The challenges of training the workforce of the future are exacerbated by additional trends in the transportation profession. There is a need for increase in the diversity of the workforce ������������������ racial and ethnic, gender, second career professionals, veterans and encore careerists and workers with diverse life skills. Another trend is the differences in wage scales between the public and private sector, which affect the retention of qualified staff at the public agencies. The establishment and maintenance of a workforce with the required knowledge, skills, and abilities require education, training, recruitment, and retention activities that are currently lacking. The Unites State Department of Transportation (USDOT) University transportation centers (UTCs) and academic institutions including both universities and community colleges can play an important role in the success of these activities. The goal of this project is to develop a framework for training and education to support the diverse workforce development needs of the transportation sector in the Southeast region with an emphasis on the role of the academic institutions. The project will identify current and future needs and define the roles of the UTCs, universities, and community colleges in the region in the training and education activities. The proposed effort will build on the national and the Southeast region efforts that have already been done in this regard.

Autonomous vehicle (AV) technology is expected to fundamentally change transportation systems. The Transportation Planning Branch at NCDOT, which is responsible for the state������������������s long-range transportation plan, needs state-of-the-art information and predictions on AV technology and its potential impacts on transport to be better prepared for the upcoming changes and maximize the social benefits that this technology will enable. The Transportation Systems group faculty (Drs. Bardaka, List, Rouphail, and Williams) and Dr. Frey (Environmental Engineering) in the Department of Civil, Construction, and Environmental Engineering at NCSU as well as Dr. Cummings, the Director of the Humans and Autonomy Laboratory at Duke University will work together to leverage existing research in the area of AV technology to evaluate impacts and provide policy and future research recommendations to NCDOT. The study will include a comprehensive literature review on AV technology and its impact on transportation demand, capacity, mobility, traffic safety, emissions, energy use, and land use. The results of previous research will be analyzed and case studies for North Carolina will be developed. The study will also provide recommendations to NCDOT regarding changes in policies and regulations, future test plans and test infrastructure, and research priorities in the area of AV technology. As part of this study, the researchers will work closely with the Transportation Planning Branch to provide guidance on how existing models (such as the statewide demand model) could be adapted to account for the presence of AVs.

The NGAT research team will work with the Duke project team that includes representatives from Durham County Emergency Services and other key stakeholders. The team will design a set of experiments for evaluating the human factors impacts of using drones to deliver AEDs in simulated out-of-hospital cardiac arrest events. The NGAT team will support the team with experiment design analysis, flight test coordination, drone flight operations, and data capture. All flight operations will meet federal, state, and local requirements for authorized use of small drones carrying an AED unit for research. The NGAT team will provide the aircraft and ground support resources necessary to accomplish the flight operations.

This project consists of a series of tasks intended to yield results that will better inform transportation agencies in their planning, design, and operations of work zones. A selected set of freeway lane closure scenarios across a wide range of traffic volumes and vehicle mixes will be modeled using microscopic traffic simulation. The modeling effort will produce key measures of effectiveness such as travel time, delay, and queue lengths so that the impact of these common lane closure scenarios across a range of traffic conditions will be documented. Additionally, a range of lane merge configurations will also be examined. Data archives from previous research on freeway work zone mobility by some of the team members will be utilized to describe driver behaviors and extend freeway work zone modeling efforts. Driver behaviors and actions under a range of traffic conditions and roadway geometric configurations will be examined. Also, additional modeling needs identified during the study will be supported using an extensive traffic data archive from previous research.

Traffic monitoring is the centerpiece of congestion mitigation and traffic management. Whilst surveillance technologies have matured enough to provide informative depiction for the traffic, the status quo of the system cannot support immediate congestion mitigation acts. Proactive congestion mitigation requires a) real-time surveillance for traffic parameters, b) prediction for imminent congestion onset, in order to c) inform responsible parties to take immediate actions to prevent congestion. This framework is founded on short time analysis (1-5 minutes) which is not valid up to date. We foresee that using a ����������������flock��������������� of interconnected, self-managed drones, can establish a deployable system to perform immediate monitoring/assessment for traffic condition to infer if congestion is approached. The drones will use their own computational and communication capabilities to host an integrated reconnaissance platform that performs traffic monitoring and traffic analysis in real-time fashion. Unlike conventional image processing approaches, a specialized ����������������inverse image��������������� processing technique will be investigated in this project to suit the limited computing abilities for the drones, namely ����������������Model Based Image Processing��������������� (MBIP). In this technique, the targeted feature is represented graphically using specific statistical distribution patterns and/or equations. The patterns/equations will be utilized as an inverse filter that will be applied by the reconnaissance device to trigger if the feature has been observed, in this case the feature will correlate to imminent congestion onset. This module requires a very limited computational power and therefore it can be easily integrated in onboard drone circuits. The targeted feature (herein, features correlated to traffic congestion), will be reproduced utilizing a traffic simulation models. The developed framework will introduced as Traffic Model-Based Image Processing (T-MBIP). The proposed methodology will be tested first in a hardware in the loop simulation environment examine its fidelity and then, if possible, in full scale field environment.

The rural areas of North Carolina have freight transportation needs that are very different from those of the urban areas. Much of their economic activity is focused around agriculture, forestry, and tourism/retirement. The principal transport mode is highway. Socio-economic conditions are challenging. Moreover, two of the rural areas, in the southwest and northeast, do not have urban areas that naturally serve as economic hubs. In the southwest, the nearest such areas are Atlanta and Chattanooga, both out of state; in the northeast, it is Norfolk which is again out of state.

This project will develop tools for analyzing and optimizing system reliability on freeways. These tools include analytical and simulation frameworks for the optimization and near real-time performance forecast of active traffic management (ATM) systems. The proposed traffic congestion mitigation toolbox will include local and/or system-wide adaptive ramp metering, integrated rampmetering and variable speed limit control, hard shoulder running, speed harmonization, dynamic pricing of express lanes, optimized traffic diversions and efficient incident response and management. ATM deployment is a means to meet specific reliability goals below a desirable agency specified threshold. This two-year project will develop a methodological framework, a novel integrative process of system modeling, and will select and optimize appropriate strategies from the ATM toolbox to meet reliability goals. We will also test the validity of the proposed approach using data from a minimum of three freeway facilities in the Southeast region at both rural and urban locations.