By Nermeen Nabil Khear 
How many cars go down your street each day? How fast were they going? What about folks out on a walk or people riding bikes? It’s not an easy question to answer, as most of us have better things to do than watch the street all day and keep a tally. But at the same time, this is critically important data from an urban planning perspective.
Of course, you could just leave it to City Hall to figure out this sort of thing. But what if you want to get a speed bump or a traffic light added to your neighborhood? Being able to collect your own localized traffic data could certainly come in handy, which is where TrafficMonitor.ai from [glossyio] comes in.
This open-source system allows the user to deploy an affordable monitoring device that will identify vehicles and pedestrians using a combination of machine learning object detection and Doppler radar. The system not only collects images of all the objects that pass by but can even determine their speed and direction. The data is stored and processed locally and presented via a number of graphs through the system’s web-based user interface.
While [glossyio] hopes to sell kits and even pre-built monitors at some point, you’ll have to build the hardware yourself for now. The documentation recommends a Raspberry Pi 5 for the brains of your monitor, backed up by a Coral AI Tensor Processing Unit (TPU) to help process the images coming in via the Pi Camera Module 3.
Technically, the OPS243-A Doppler radar sensor is listed as optional if you’re on a tight budget, but it looks like you’ll lose speed and direction sensing without it. Additionally, there’s support for adding an air quality sensor to see what all those passing cars are leaving behind.
This isn’t the first time we’ve seen the Raspberry Pi used as an electronic traffic cop, but it’s undoubtedly the most polished version of the concept we’ve come across. You might consider passive radar, too.
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This articles is written by : Nermeen Nabil Khear Abdelmalak
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