Whether one has a casual interest in keeping an eye on the roadways in their neighborhood, or is involved in planning the flow of traffic through a city, they would do well to get acquainted with automated traffic monitors. These devices continuously monitor things like vehicle and bicycle traffic, and pedestrian activity — the raw data that is necessary for understanding roadway utilization over time. That data could be used, for example, to reprogram traffic signals to minimize rush hour congestion.
Traditional methods of monitoring traffic are just not good enough, which is one reason why we have so many issues with congestion in urban areas today. Manual counting is time-consuming and expensive, so limited data can be collected in this way. Pneumatic road tubes only provide coarse data and they often produce inaccurate readings. Other existing solutions suffer from similar problems.
Summaries of traffic patterns can be generated (📷: glossyio)
Traffic monitors that leverage cutting-edge artificial intelligence (AI) and computer vision algorithms generate much better data, and they can keep running day after day to provide rich information to planners. However, they can be on the expensive side. That may not be a problem for the hobbyist that only needs one, but for the city planner that needs thousands, it places them out of reach.
A hardware hacker that goes by the handle glossyio has put forth a solution that could make traffic monitors more accessible in the future. Unimaginatively called The Traffic Monitor, these devices are equipped with a camera and Doppler radar, as well as the compute resources necessary to run object detection algorithms. But unlike many other options, the hardware is inexpensive and widely available. And since the project is open source, you can even build your own copy of the monitor (pre-built kits will be available at a later date).
If you decide to build a monitor, you will first need a Raspberry Pi 5 single-board computer, and either a Google Coral AI Tensor Processing Unit or a Raspberry Pi AI HAT+ to handle the onboard processing. A Raspberry Pi Camera Module 3 is the suggested camera option, although other options should work fine as well. Aside from that, you will need an OmniPreSence OPS243-A Doppler Radar Sensor and a 3D-printed weather-resistant case.
Details of each event can be drilled down on (📷: glossyio)
Once the hardware has been gathered, extensive documentation is available to help with the assembly process (although some sections are still under development). Source code and usage instructions are also available in a GitHub repository.
With a completed unit in hand (or rather, on a street corner), you will be able to monitor the number of vehicles, bicycles, pedestrians, and more that pass by throughout the day. The algorithms also determine the speed at which each detected object moves, so you can better understand how safe the area is. And since all processing takes place on-device, there is no need for concern about the privacy of those that are caught on camera.
With the possible exception of the radar sensor, many readers of Hackster News will already have all of the components needed to build a traffic monitor on hand. If you take the next step and make one, be sure to let us have a look at it!
