airBoi

airBoi, a smart mobility solution for traffic management and planning, accomplished by unmanned, CV aided drones.

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Inspiration

By 2050 75% of the population will reside in urban cities. This will highly affect traffic on big cities. As today the average American spends about 34 hours every year sitting in traffic and by 2050 this number will increase. To potentially solve this problem, our project consist on the idea of smart mobilities. Smart mobilities is a tool to achieve sustainable cities. With smart mobilities citizens can create better cities. Mainly, our project dives into the smart mobility idea, improving efficiency in transportation, and promoting security in a technological infrastructure. In 2016 there was reported 6.5 millions motor vehicle crashes over the year. In this same year 40 thousand people die in a motor vehicle accident. With air-boi this can be reduced. First, airBoi involves computer vision. This will assist traffic control operators to take better decisions regarding the city’s traffic. Second, air-boi graphical user interface is easy to use, yet is a powerful tool to visualise and analyze the traffic data in real time. To collect information, the end-user will send a drone or drones to gather information of the city’s traffic, later this information is used to prevent traffic congestions and possible motor vehicle accidents. The graphic user interface will detect if there is a collision or slow traffic and the system will alert the end-user so he/she can resolve the problem in real time, calming the situation. Additionally, air-boi can create waypoints where drones will follow, like in a circuit, to track information from a specific area. air-boi promise to decrease 20% of traffic accidents and to maintain congested areas as fluid as possible. This strong communication networks between software and humans leads to alleviate congestion in an efficient manner. Smart mobility – a good move for the future.

What it does

airBoi is a drone assisted system. It schedules drones to follow a predetermined route and records footage to analyze traffic.

How I built it

Built With flytBase, IBM Waston, Deep Neural Network and Python.

Challenges I ran into

There were some errors while training the model in IBM Watson Studio.

Accomplishments that I'm proud of

Our workflow was efficient to accomplish more tasks that were conceived as the Hackathon went on.

What I learned

Simulations are a great tool to advance in a prototype idea.

What's next for airBoi

Implement the flytBase with the airBoi GUI.

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