From Streetsblog:
Here’s a great visualization of what cities get out of the billions of dollars spent on highways and road expansion: more traffic.
Justin Swan at City Clock made this chart showing the relationship between congestion levels, as measured by TomTom, and car use. (Yes, it has no X axis — see below for explanation.) The pattern that emerges is that the places with the most traffic and driving also have the least congestion.
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Every point (small red dot) on the chart represents one of the 74 cities reviewed. Look to the right side of the chart and you’ll see the congestion level for that point/city. If you draw a vertical line through any point/city you are looking at, it will cross the top edge of the green shaded area. Where that vertical line crosses the top of the green area represents the % of trips by car in that same city (see left side).
Example: Toronto has a 27% congestion rate. Draw a line from it’s data point to the top of the green area and you hit a point that falls at 56% auto mode share.
As for the red shaded “cloud”, it just represents where the majority of data points fall within to demonstrate correlation.
We know from the work of Joe Cortright that the traditional definition of congestion is a poor way to measure people’s ability to get around their city – because it doesn’t reflect the actual time people spend traveling. Drivers in Dallas and Houston may stew in gridlock less than people in other cities, but they spend more time on the road.
Swan notes that the most congested places are also the places where people have good travel options that don’t involve driving. His chart suggests that car congestion itself is not the problem that needs to be solved — as long as there are other ways to get around, in a congested city few people will actually have to sit in traffic.
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PT: I’m not a big fan of the Tom-Tom Index, partly because anyone who uses a Tom-Tom box is likely exceptional (otherwise they wouldn’t need a Tom-Tom box.) And because they’re measuring the difference between free-flowing traffic at or above the posted speed limit, and actual speeds experienced – which may not be particularly severe or even seen as a cost incurred, even though a value in dollars is often attached to it.
But the main point above is that a shorter more congested trip is frequently preferable to a longer less congested one – especially if there are other modes to travel (which aren’t comparatively measured at all).
I do not understand this chart. What is the X Axis depicting ? Why are some cities more to the right than others ?
It seems the points are ordered left to right from highest % travel by car to lowest. There’s no specific x-axis value — they are evenly spaced. The vertical position of the dots only corresponds to the y-axis on the right, “congestion level”.
I would be averse to drawing any causal connections from this data. The two measures certainly appear inversely correlated, but I don’t think it’s at all obvious why. The cities on the right, for example, have messy old road networks in their historic centres. It could be that a need for metro transit came even before automobiles because of the general difficulty of getting around these cities, which then obviated the need for auto commuting later on. I can think of lots of other possibilities.
The explanation of the graph clarifies things, but I still don’t see how the green shaded area was generated. And I don’t see how this presentation of the data is any better than a standard x vs y scatter plot.
I think the data would have been better presented as a bar graph – with either 2 bars side-by-side or overlapping for each city – one representing its congestion value and another representing its % travel by car.