We believe that any application assisting the City of Austin in their initiative to become a Smart City should continuously learn and adapt to the growing demands of the population. It will need to be powered by a technology that enables flexibility and avoids vendor lock-in.
Carnak is our solution. Carnak is a cloud-based application that gathers normal and emergent traffic pattern behavior data from multiple city systems, using Artificial Intelligence and Machine Learning computing power to help analyze the traffic flow, accessibility, accidents, and road and asphalt maintenance. Carnak then recommends route modifications, predicts and suggests road repair schedules and potential roadway plans for the future, allowing the City planners to do complex "what-if" analysis that would be impossible with spreadsheets.
Carnak can help the City avoid costly construction mistakes by predicting the rippling impact of changing traffic and roadways on cars, trucks, bicycles, pedestrians, public transportation users, and the disabled. To help Carnak better predict the impact of changes, it will be trained with baseline data such as human behavior patterns (how much traffic before tempers flare, etc.). Carnak will also be invaluable in assisting the City with integration of autonomous vehicles and robots into the traffic flow.
Carnak can become the central hub for the City's many Smart City traffic initiatives, as it can read any type of structured or unstructured data, including live data streams from Internet of Things devices. Carnak is based on Apache's Unstructured Data Management applications architecture (UIMA), so it will be a sound common ground for exchanging data from the city's myriad of devices.
Carnak turns the City's data into actionable knowledge, which can also be marketed to pay for the initiatives it suggests. Carnak can even be trained to predict the worth of its own data.
The technology we use is cutting edge: cognitive computing and blockchain. For purposes of our prototype, data collected by the City will be aggregated and analyzed by IBM’s Watson, with IBM Blockchain used as a source of truth to insure the veracity, integrity, security, and auditability of the information. Carnak will be designed with multiple tiers so as to be able to change AI and blockchain vendors in the future. It is a system designed to guide City planners with a plan for the future, while taking into account the ever-changing present.
Our team consists of Smart City futurists who use technology to make the world a kinder place. We are finalists in IBM's Watson Build Challenge with our app RealLIfe Adventure (Riley), which helps the blind and visually impaired to visualize their surroundings by using a smart phone or tablet.
Karen Kilroy: Architect, AI Developer, full-stack open source software and blockchain engineer. Time travels to miss rush hour - brings ideas back from the future.
Deepak Bhatta: Architect, AI Developer, full-stack open source software and blockchain engineer. Leaps tall buildings to avoid traffic - crafts cognitive solutions.
Lynn Riley: AI Developer, data scientist, project manager. Walks through walls instead of being stuck - leads the way for humankind.
Kara Williams: Business and financial management, quality assurance and user acceptance testing. Uses x-ray vision to see through gridlock - finds better solutions.
Kristen Baranovic: Brand strategist and innovator, marketing wizard. Envisions the future - makes sure we are equipped to shine through it.
Carnak can predict how the City can be made more accessible by weighing what works in Austin as well as other cities, and make recommendations on how to proceed in serving the population evenly. Also, with blockchain as a source of truth, equity among the City's population can be ensured through an immutable, auditable, permanent record.
We would use the prize money to pay our team to build the proof of concept of Carnak. This includes our engineers, as well as a project manager and a quality assurance manager.
