DEMO: https://demo.streetsdatacollaborative.org/commute/
Street maintenance is the most visible example of local government performance. Unfortunately, approaches to understanding the conditions of our street networks remain a relic of the past.
Today, data collection for resource allocation and performance evaluation exists at two unsustainable extremes.
At one end of the spectrum, cities rely on the low-touch, windshield surveys. These approaches provide little ground truth and attempts at network-wide collection are inevitably constrained by human subjectivity and fatigue. At the other end, cities have turned to high-touch, laser and lidar vans. But this ground truth comes with a price tag. It can take up to 3 years to cover a street network of a large city like Los Angeles once.
To use these public dollars respectably, the future of street maintenance must embrace a cost-effective approach to frequent, comprehensive surveys with sufficient ground truthing. The StDC calls this new approach Street QUality IDentification (SQUID).
A SQUID survey is as simple as a ride through the city. Accelerometer readings and street imagery are automatically collected to generate a network-wide map of ride quality, preserving ground truth at low cost. In addition, computer vision, the same set of proven techniques that automatically identify faces on Facebook and let Teslas autonomously stay in their lane, can identify potholes and other street defects even prior to inspection of imagery by city agencies to help focus our attention.
By using SQUID to reduce the time between surveys and their costs by an order of magnitude, the StDC is shifting us from a reactive, whack-a-pothole blitzkrieg to a paradigm of truly anticipatory street maintenance.