In my opinion, we need far better urban data if we're actually going to make evidence-based decisions. Thankfully, there are lots of great companies that are focused on this space. One of them is Eco-Counter, which makes devices to count pedestrians and cyclists, among other things. This is an important job, because as Peter Drucker used to say, "you can't manage what you don't measure."
Let's look at their bike counters. According to their global map, they have 464 of them installed around the world. Montreal has 58 of them, which we've spoken about before, and is an impressive install base. And Toronto looks to have only one, which is located on Bloor Street on the north side of High Park.
The busiest route/counter in Montreal is at St-Denis Street and Rue des Carriéres. So far this year -- up to November 17, 2024 -- this counter has seen an average of just under 5,000 trips per day and a year-to-date total of 1,600,468 trips. Both of these metrics are notably up compared to 2023 when I last looked at the data.
The busiest route in Eco-Counter's entire network is on Boulevard de Sébastopol in Paris (an important main roadway, not a side street). It has seen an average of 13,667 trips per day and a year-to-date total of 4,386,996 trips. Not surprisingly, the Paris counter exhibits less seasonality. People still cycle in the winter in Montreal, but it's less than in the warmer months.
Finally, our lone Toronto counter adjacent to High Park has seen an average of 1,186 trips per day and a year-to-date total of 380,813 trips. Not quite Paris or Montreal (the latter of which has a colder climate), but I would argue that this really isn't an indicative location for Toronto given how underdeveloped the area is. Plus, you need to see each route as part of a network.
If you look at Montreal's top 5 bike counters, all of them have a year-to-date total that exceeds 1 million trips. This is important information if you're trying to make mobility decisions and these are significant figures. Imagine if these millions of people got off their bikes and instead decided to take transit or drive a car. That would change things.
Photo by Celine Ylmz 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.
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.
Many of you have probably heard of the concept of a "digital twin." Put simply, it is a digital representation of a physical thing. This could be a thing that already exists or, in the case of a new building, it could be a thing that you're about to make exist.
But there's no reason to stop at the scale of a building. Right now, there are groups working on modeling entire cities. Sadly, in Ukraine, it is being done to document important buildings that could get destroyed. But in other places, it is being done in order to create a new kind of urban testing environment (via FT):
“In the city, you don’t have a development environment; you only have one city. The laboratory is the place where the planners go to test. So test in a digital twin and then develop or implant in the city. That’s going to be the value.”
The thinking is that if you combine a digital twin with good real-time urban data and AI, then you might actually be able to start testing new city building initiatives. For instance, maybe you could ask it: What would happen if we added a traffic lane, here? Would it actually help congestion or would it induce new demand?
It's hard to model this kind of stuff today, which is one of the reasons why there's usually fierce debate about seemingly everything. But if we had accurate models that could tell us something close to reality, that feels like it would be a game changer for city builders.