Broadly speaking, cities tend to have better data on vehicular traffic than on pedestrian and bicycle traffic. This is because road design has traditionally prioritized the movement of cars, above all else. So it has felt right to bias traffic counts. But there are lots of places where pedestrians and cyclists greatly outnumber vehicles.
For example, I was on Queens Quay yesterday visiting my mom and, if you've ever been to Toronto's waterfront in the summer, you'll know that it has one of the busiest bike lanes/trails in the city — if not the busiest. But if you ask ChatGPT just how busy it is, it will more or less say, "I don't know. Really busy?" And that's because we don't have real-time usage data. We have estimates. And the same is true of pedestrian counts.
(If you're aware of a great dataset, please share it in the comment section below.)
But this is starting to change with the advent of AI traffic monitoring solutions that can handle multi-modal environments. Meaning they're capable of counting everything from pedestrians and scooters to cyclists and trucks. This is what cities need to make better decisions. And as this new tech becomes more widespread, I think it's going to show us just how much we've been missing.
Cover photo by Joshua Chua on Unsplash