Inspiration was created by a small team of pilots and aviation enthusiasts. Inspiration came from a near-mid-air collision one of the authors experienced in the fall of 2015 as he departed his favorite airport. In the information age, there is still no data about the airspace surrounding small airports, and that's what we're set out to improve.

What it does is a monitoring service specifically developed for general aviation. It relies on a network of airport-based ADS-B receivers to analyze air traffic data that can be used to enhance safety.

  • Tracking history: Facilitates pilot flight debriefs by tracking aircraft of interest, capturing 1-second grain data about takeoffs, maneuvers, approaches, landings, and even taxiing activity.

  • NMACs: Potential near mid-air collisions detected between two or more aircraft (as defined by the FAA, a good example here). Data is anonymous to protect privacy.

  • Events: Emergency transponder codes, unusually low flying aircraft, excessively high descending rates, and combinations thereof. Data is anonymous to protect privacy.

  • Live notifications for the above features, sent via SMS to subscribers.

  • Live traffic: Renders live traffic directly from all ADS-B stations (currently KRDU, KLZU, KJNX). Reuses work from the ADS-B Receiver and dump1090-mutability projects.

  • Airport traffic: Visualizes collective traffic pattern trends by airport in the last two weeks.

How we built it

The team leveraged years of experience in areas like NoSQL distributed databases and infrastructure and webapp development. The platform's MVP was originally envisioned in AWS but implemented using Kafka/Spark/Cassandra due to our familiarity with that stack. We finally ported it to AWS for scale, availability and as a stretch goal.

Challenges we ran into

  • Sourcing data from the devices took days of research. Thankfully, we were able to reuse open source work already in place to feed established tracker sites like FlightAware and ADSBExchange.
  • Integrating dependencies between Spark Streaming and Kafka for the first time took several days to get right. Turned out much easier for Kinesis and EMR Spark.
  • DynamoDB took some getting used to, given its restrictions coming from Cassandra.

What's next for

  • Continue to grow user adoption and airport coverage.
  • Better UI to depict altitude profile.
  • Slack integration for use in the field by flight schools
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