Chris Frey

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.

NCDOT owns six F59PH diesel locomotives. NCDOT is committed to providing an energy efficient and environmentally friendly alternative for travelers. NCDOT is exploring the use of alternative fuels and retrofitted emission control technologies. Furthermore, NCDOT has made extensive capital improvements to the rail corridor, including removing grade crossings and other updates that eventually will improve travel speed. Field measurements of locomotive energy use and emissions are needed to assess fuels and retrofit technologies, and to develop planning-level models to assess the benefits of corridor capital improvements for energy use and emissions.

Our proposal will determine the most important variables that explain spatial and temporal variance of near road traffic-related pollutant concentrations: We will explore the relative influence of traffic activity, the built environment (roadways and other built structures), and environmental (e.g. temperature, wind and background concentrations) factors on multi-pollutant transport, differential evolution and how all of these influence human exposure. We will also demonstrate novel surrogates of near-road traffic-related pollution: We will develop data and modeling approaches to quantify exposure concentrations of multiple pollutants emitted from vehicles or formed as secondary pollutants in the near-roadway microenvironment: fine particulate matter (PM), ultrafine particles (UPF), semi-volatile organic compounds (SVOCs), nitrogen dioxide, and carbon monoxide. The role of individual pollutants and mixtures of pollutants, and whether some pollutants are good surrogates for others, will be assessed. We will improve inputs for exposure models for traffic-related health: We will explore the implications of our measurement findings by applying them in spatial and temporal analysis of the relationship between human exposure (or surrogates for human exposure) and adverse effects, including evaluation of mixtures of pollutants and other proxies for exposure

The National Transportation Center at the University of Maryland (NTC@Maryland) with Arizona State University, North Carolina State University, and the University of Florida, supported by Google, Uber, RubyRide, INRIX, TomTom, HERE, and State Departments of Transportation in Maryland, North Carolina, and Florida, proposes an Integrated, Personalized, REal-time Traveler Information and Incentive (iPretii) technology that encompasses: a) A person-level travel behavior, traffic simulator, and energy estimator called the System Model (SM); and b) A Control Architecture (CA) with personalized signal design based on behavioral research, user intent prediction, signal optimization, and signal delivery modules. We envision that iPretii will enable public and private-sector entities to deliver personalized incentives to guide a subset of travelers to adjust both their driving behavior and choices of route, departure time, and mode. These personalized incentives minimize energy consumption by optimizing driving style, mitigating congestion, and increasing vehicle occupancy. iPretii will be developed within two years to quantify possible energy efficiency gains in the Washington, DC-Baltimore megaregion with a population of 8.3 million, under recurrent and non-recurrent congestion (e.g., accidents, work zones, adverse weather). Multiple field tests are designed to identify and address technology gaps, and to demonstrate iPretii readiness for real-world implementation.

Since 2008, Dr. Frey������������������s research group at NC State has measured the activity, fuel use, and emission rates of light duty gasoline vehicles using GlobalMRV Axion portable emission measurement systems (PEMS). Each vehicle has been measured on four designated routes in the Research Triangle Park, NC region. We will review our legacy data for each vehicle and convert the data to a consistent format for delivery to the sponsor. This project will focus on demonstrating our procedures for preparing data for delivery to EPA, on identifying the vehicles for which we have data, and on delivering data for 180 vehicles. The focus of this project is on light duty gasoline vehicles, fueled with retail gasoline (E10). We will deliver an Excel file or a .cvs file for each vehicle. We will provide a summary table of the metadata for each vehicle.