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FIND OPEN ON-STREET PARKING IN REAL-TIME

Parknav uses artificial intelligence to know where there is open on-street parking (free, metered and permit) for every street in real-time.

Photo of Jeremy Leval
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Parknav uses machine learning and predictive analytics to show drivers where there is open on-street parking in real-time. It is the only scalable solution that requires zero hardware and covers every street in a city, 24 hours a day/7 days a week, for all types of on-street parking (free, metered and permit) with accuracy that surpasses 80%. Our solution is licensed to automotive and related industries. Parknav’s customers include Car manufacturers (OEMs), Fleets (e.g. Utilities, Rental), and Geo and navigation infused producers such as maps providers, real estate, internet services, and mobility services with service that covers 255 cities throughout the USA and Europe. Parknav holds (3) issued USTPO patents and (1) pending PCT patent in the EU.

Describe who will use your solution (1,000 characters)

Our solution is used by those who drive in congested major cities across the United States in Europe. We have worked with various automotive manufacturers and our data solution was even integrated into a major German automaker's infotainment system. For the typical use-case, imagine the following: You are needing to drive to and park in a city where on-street parking is scarce and often difficult to find. You may or may not be familiar with the city, but you know finding parking will be a pain once you arrive at your destination. Luckily, you have Parknav. With Parknav, you can instantly see which streets are likely to have an open on-street parking space near your desired destination at any time. Additionally, Parknav can offer turn-by-turn voice guided navigation to guide you on the optimal route to drive where you are most likely to find an open on-street parking space the quickest.

Describe your solution's stage of development

  • Fully Scaled - you have already scaled your solution and are exploring new use cases

Tell us about your team or organization (500 characters)

Prof. Dr. Eyal Amir, CEO - Prof. Amir received tenured Professorship at UIUC in 2009, after joining it in 2004 - Ph.D. in Computer Science from Stanford University (2001) - Recipient of IEEE "10 to watch in AI" (2006) - Received the CAREER Award from NSF - Awarded the Arthur L. Samuel award for best Computer Science Ph.D. thesis (2001-2002) at Stanford University Sergei Kozyrenko, CTO - 15 years of big-data systems engineering and software architecture

Size of your team or organization

  • 11-50

Funding Request

  • $100,000

Describe how you would pilot your idea (1000 characters)

Our pilot will consist of selecting a target area within Grand Rapids where on-street parking is scare/difficult to find. We will use the Challenge funding to bring this selected area online by acquiring all data necessary to provide an accurate long-term solution for the city. We have done this before and have extensive experience in being successful. Our solution is profitable by licensing the data either per vehicle (automotive industry), per user or per API call.

Describe how you would measure the success of your pilot (1000 characters)

Our success is gauged by KPIs relating to the precision and recall of our prediction. We compile and analyze ground truth testing (where Challenge funds will also go) along with automated tests to continuously check and improve the accuracy of our prediction.

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Photo of Tom Bulten
Team

Hello Jeremy: Thank you for posting your Parknav proposal to the Grand Rapids Challenge. I am one of the online facilitators for Grand Rapids. The availability and cost of parking has been discussed widely here. Your proposal adds to the conversation with a specific focus on on-street parking availability.

Photo of Jeremy Leval
Team

Tom Bulten , we think so as well :-)

We have had incredible success in implementing our solution in cities around the globe and think it would be a great "Smart City" addition to the City of Grand Rapids without any need to modify the current infrastructure.