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Harnessing IoT and big data to reduce parking time by 70 percent


Comprehensive SDKs for building and running IoT software


Transportation and Logistics

Type of work:
Transportation, IoT

Technology and Platforms:
Machine learning, Artificial intelligence

Driving in circles, searching in vain for an open parking spot, is a stressful experience we all dread. Coupled with the uncertainties of traffic and trip-planning, parking adds another layer of frustration to a hectic commute.

But what if we could use real-time traffic data to make on-street parking easier and more predictable? Using machine learning and real-time data, that’s what ParkNav hoped to accomplish — taking the stress out of parking.
To make their idea a reality, the team of data scientists, engineers, and entrepreneurs behind ParkNav came to Forte Group.


  • Accurately predicting where open parking spots can be found requires a mass of real-time data.
  • Street parking availability is highly volatile and can be affected by numerous factors such as time of day, weather, and nearby events, making this data challenging to measure.
  • To build a highly functional mobile application, ParkNav needed to capitalize on its machine-learning ability while incorporating feedback from thousands of its users.

Results achieved

  • With Forte Group’s help, the ParkNav app reduces the time it takes to locate a parking spot by 70 percent.
  • The app has expanded to over 240 cities across the US and Europe. In 2016, ParkNav was awarded the South by Southwest (SXSW) Conference Accelerator Enterprise and Smart Data prize.
  • ParkNav has also used its machine learning and big data processing capabilities in other industries, offering its data to help real estate, automotive, and internet companies.


Business Challenges

  • Build an application from scratch to solve a variety of day-to-day traffic challenges
  • Design a convenient tool people can use on the move
  • Take into account all parking areas’ parameters that might be important for the driver

Featured-based  Challenges

  • Optimize the user experience that supports on-the-go decisions
  • Integrate real-time monitoring and predictive analytics
  • Enable user preferences for more personalized and safe parking experiences

 Solution features 

This predictive parking data is updated in real time. Users can filter the results according to the type of parking they’re looking for, as the map includes free, zoned, and metered spots or they can search for nearby parking garages.

Users can enter their desired parking area, and the application displays the results in green, orange, and red. Based on data, each color represents a different level of predicted parking difficulty. Green areas have the most open parking spots, orange zones are less ideal than green, and red sections are unlikely to have available spots.

Because ParkNav is a tool that needs to be used quickly by people on the move, the user interface (UI) and usability were essential. The Forte UI team improved the app’s usability to optimize the user experience.

An application combines the company’s database of parking information with traffic data to create precise, accurate, and real-time recommendations.

Users can also save their preferences in the app, making the whole search experience more pleasant and stress-free.





70% reduction in parking time


Used in more than 240 cities worldwide


SXSW Conference Accelerator Enterprise and Smart Data Award winner




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