The pilot grants will provide funding for one semester for recipients to further interdisciplinary research within the area of smart and connected communities. IPaT is supporting data-centric projects aiming to create new forms of smart city data, leverage and make available legacy city data, and prototype targeted uses of smart city data.

The grant program will result in new collections of smart city data that can be made available to the Georgia Tech research community and new prototypes for working with that data.

DiSalvo was funded for $10,000 and Le Dantec for $5,000. Their funded research proposals can be found below. Learn more about the other grant recipients here: http://ipat.gatech.edu/2017-ipat-smart-connected-communities-data-pilot-grants

Developing a Robust Archive of Environmental Data to Support Smart Cities Initiatives
Amanda Meng, College of Computing
Ellen Zegura, College of Computing
Carl DiSalvo, Ivan Allen College of Liberal Arts

Smart cities achieve more livable and sustainable outcomes for their residents when physical and social infrastructureS are connected through networked technologies. Citizens aid the city in making better service delivery and policy decisions through open and participatory data collection. Despite the recent launch of Atlanta’s smart city initiative, there is currently a dearth of data. As environmental sensors are a key component of the planned deployment of sensor technologies in Atlanta, we propose a project focused on environmental data. The goal of this project is to construct a robust archive of environmental data, from multiple sources, and make that data readily available for prototyping, and later, service development.

Sensing Traffic Conditions to Model and Predict Rider Stress
Christopher Le Dantec, School of Literature, Media, and Communication
Kari Watkins, School of Civil and Environmental Engineering

Recent work within transportation research has begun to question the accepted models for assessing cycling infrastructure. Metrics like levels of service or compatibility indexes are based on models for vehicular traffic and miss important elements of what goes into choosing one route over another. A more recent and promising model — Level of Traffic Stress — does a better job of accounting for the subjective experience of cycling; however, that model is new and would benefit from a stronger empirical foundation to clarify the transitions between the four levels of stress in the model. We propose to develop a new data set that would provide a more empirical and ground-truth foundation for modeling levels of traffic stress. To do so, we will begin prototyping and deploying purpose-built sensors to an established population of Cycle Atlanta app users in the spring of 2017 to determine which data sources have the highest value for determining the Level of Traffic Stress. To accomplish this, we will build a set of prototype IoT sensors that will complement route data collected by the cyclist tracking application Cycle Atlanta (and its derivatives used in other locations). The sensors we are looking to build will augment data collected by the app and will collect noise, air quality, and road condition data as baseline. More importantly, we will determine which constellation of off-the-shelf sensors is needed to reliably detect object proximity and approach speed. The latter being very important to the experience of stress as near-miss and high-speed encounters with cars and trucks are the primary contributor to rider stress.