It is a curious fact, and one that would have delighted the sort of person who finds meaning in tea leaves, that as more New Yorkers pedalled around Manhattan on borrowed bicycles between 2013 and 2022, Americans elsewhere grew demonstrably better at crashing into things fatally. The correlation sits at 93 percent, which is the sort of number that makes pattern-seeking humans briefly forget that correlation is basically just two strangers who happen to be walking in the same direction. One wonders what the Citi Bike algorithm makes of this.
The real culprit, almost certainly, is that both numbers ride the same economic wave. Between 2013 and 2022, the US economy expanded unevenly but generally upward, which means more people had disposable income for bike-sharing memberships and, separately, more miles driven overall on American roads. Bike trips grew from roughly 5 million annually to 20 million, a quadrupling that tracks neatly with population growth in cities and the sheer cultural momentum of urban cycling. Meanwhile, total vehicle miles travelled in America climbed from about 2.9 trillion to 3.2 trillion miles, and when you're moving 3.2 trillion miles a year, a certain grim baseline of fatalities follows like a shadow. It's not the bicycles killing anyone; it's simply that booming cities and booming road use grew together, two entirely separate phenomena masquerading as cosmic conspiracy.
This is what happens when you cross-reference any two datasets spanning a decade of economic expansion—they tend to move together like dance partners who never actually met. The absurdity here isn't that bikes and deaths are linked; it's that we find it remarkable when they're not. Two trends that share nothing but a calendar and a country, moving forward in perfect, meaningless sync. Pattern recognition is mostly just noticing.
As an Amazon Associate, getspurious.com earns from qualifying purchases. Learn more.
Want to learn more about why correlations like “US traffic fatalities” vs “Citi Bike annual trips (NYC)” don't prove causation? Read our guide to statistical thinking.