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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
The divisive debate over bikes lanes in Toronto continues to remind me that we need far better urban data. People and politicians keep touting "evidence-based decisions," but what exactly is that evidence? The high-level figure being thrown around by the anti-cycling side is that only something like 1% of residents use bike lanes. So obviously it only makes sense to focus on the 99% and not give up any space to this small minority group.
But this is highly aggregated data. It also doesn't speak to any of the externalities associated with introducing new bike infrastructure. Looking at 2021 Census data, the number of cyclists was actually around 5% for the old City of Toronto and in some areas it was between 15-20%. However, it's absolutely critical to note that this is only the people who selected cycling as their "primary mode of commuting" when submitting their responses to the last census.
I had a dinner in the suburbs this evening. And so in the afternoon today, I opened up Google Maps to figure out how I was going to get there.
I didn’t have my car with me — because I hate driving into the office — so in my mind, I was either going to take transit or take an Uber.
These are the time estimates that Google gave me:
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
The divisive debate over bikes lanes in Toronto continues to remind me that we need far better urban data. People and politicians keep touting "evidence-based decisions," but what exactly is that evidence? The high-level figure being thrown around by the anti-cycling side is that only something like 1% of residents use bike lanes. So obviously it only makes sense to focus on the 99% and not give up any space to this small minority group.
But this is highly aggregated data. It also doesn't speak to any of the externalities associated with introducing new bike infrastructure. Looking at 2021 Census data, the number of cyclists was actually around 5% for the old City of Toronto and in some areas it was between 15-20%. However, it's absolutely critical to note that this is only the people who selected cycling as their "primary mode of commuting" when submitting their responses to the last census.
I had a dinner in the suburbs this evening. And so in the afternoon today, I opened up Google Maps to figure out how I was going to get there.
I didn’t have my car with me — because I hate driving into the office — so in my mind, I was either going to take transit or take an Uber.
These are the time estimates that Google gave me:
Meaning, it excludes people who maybe only cycle 1-2 days a week, or who ride for leisure and/or for exercise, or who ride to their French class in the evenings (like me). I would also assume that these numbers have generally grown since 2021 given the overall investments that have been made in biking infrastructure. So overall, this is weak data. It's a few years old. And it excludes many types of users. We need to get more granular.
Like, it's great to see local business owners speaking out about the benefits that they have seen as a result of the Bloor bike lanes, but in the end, this is also anecdotal. We need real-time data, precise modal splits, the throughput of every major street, and much more. Then maybe we'll be able to better optimize around the fact that we are a city divided by built form and by politics. That's the thing about evidence-based decisions, they tend to get stronger with accurate evidence.
It was going to take me over 4 hours to walk there. Over an hour to drive there. And 47 minutes to take the train there. Interestingly enough, cycling was also going to be faster than driving.
As soon as I saw this, I shut down the app and decided I would take the train. All I was interested in was the absolute fastest option. And for me at that moment, it was the train.
I recognize that this isn’t always the case. Sometimes driving is much faster than taking transit. It depends on a number of factors.
But as a general rule, when it comes to big and dense cities, you really can’t beat trains and bikes for moving the greatest number of people, as quickly as possible.
Meaning, it excludes people who maybe only cycle 1-2 days a week, or who ride for leisure and/or for exercise, or who ride to their French class in the evenings (like me). I would also assume that these numbers have generally grown since 2021 given the overall investments that have been made in biking infrastructure. So overall, this is weak data. It's a few years old. And it excludes many types of users. We need to get more granular.
Like, it's great to see local business owners speaking out about the benefits that they have seen as a result of the Bloor bike lanes, but in the end, this is also anecdotal. We need real-time data, precise modal splits, the throughput of every major street, and much more. Then maybe we'll be able to better optimize around the fact that we are a city divided by built form and by politics. That's the thing about evidence-based decisions, they tend to get stronger with accurate evidence.
It was going to take me over 4 hours to walk there. Over an hour to drive there. And 47 minutes to take the train there. Interestingly enough, cycling was also going to be faster than driving.
As soon as I saw this, I shut down the app and decided I would take the train. All I was interested in was the absolute fastest option. And for me at that moment, it was the train.
I recognize that this isn’t always the case. Sometimes driving is much faster than taking transit. It depends on a number of factors.
But as a general rule, when it comes to big and dense cities, you really can’t beat trains and bikes for moving the greatest number of people, as quickly as possible.
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