The Institute for People and Technology announced the results of their first Smart & Connected Communities Data Pilot Grant program. These pilot grants will provide funding for one semester to further interdisciplinary research within the area of Smart & Connected Communities. IPaT will support data centric projects aiming for one or more of the following:
- Creating new forms of smart city data
- Leveraging and making available legacy city data
- Prototype targeted uses of smart city data
The result of this pilot grant program will be 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. Among the six project that were selected to receive funding were the following project by Digital Media faculty:
Developing a Robust Archive of Environmental Data to Support Smart Cities Initiatives
Carl DiSalvo, Ivan Allen College of Liberal Arts
Smart cities achieve more livable and sustainable outcomes for their residents when physical and social infrastructure 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 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
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 are the highest value data sources 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 are needed to reliable 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.
To find out more about the grants read: http://ipat.gatech.edu/2017-ipat-smart-connected-communities-data-pilot-grants