Man people in 1980 just fucking hated pedestrians. No phones, light cars and they still mowed people down at a greater rate than the post smartphone peak.
Drunk driving was cool and fun in the 70s
Just in it for the love of the game
Probably poor infrastructure, people crossing in the middle of the street, fewer strict speed limits, etc.
And probably still poor drunk driving awareness and/or enforcement
I don’t think this is a useful comparison and I think it’s a dishonest to present it this way.
You’re trying to show that smartphones cause pedestrian deaths. This leaves out some very important info like figuring out if traffic accidents per mile driven have increased, if there are more pedestrians, the rates of death per accident and which kinds of vehicles are most associated with these deaths vs what people walk (or limp) away from.
From one of your sources, let’s look at what that red line looks like in a different scale
. You’ve stretched that way out. Looks like the overall trend for non pedestrian deaths is going down over the long run. So pedestrian deaths are increasing while non pedestrian deaths are decreasing. No wait, this graph shows a huge increase in non pedestrian deaths!
No wait, this graph shows a huge increase in non pedestrian deaths!
But it does also show an increase in pedestrian deaths at around the same time, being shortly after the smartphone adoption, so couldn’t it be said that distracted drivers are killing themselves and/or passengers, and pedestrians more since the smartphone gained popularity?
We could draw a bunch of conclusions that the data doesn’t point at, sure. Show me the fatality rate per crash by vehicle manufacture year and I’m betting we’d see a steady trend downward.
Here’s another graph that shows a change around 2010 that I could lay over the data that would correlate a rise in car sales with a rise in pedestrian deaths
And since the fatalities are in absolute numbers and not rate, the number of fatalities per car on the road might have been holding steady or going down. (Note that that graph has no sources, so you shouldn’t take it at face value anyway)
Not trying to defend cars, just trying to call out massaging data to fit what one has already decided is true.
These comments are all over the place. If you must look at correlations, there are some more rigorous methodologies to study cell phone use or vehicle weight you can use.
My preference is when you’re given historical data is to find a natural experiment and use something like an interrupted time series regression. For example, if there are two cities with mostly similar characteristics but one criminalizes cell phone use more explicitly, you can compare statical differences in accidents while controlling for historical trends.
And that’s just from the top of my head. That said, there’s fairly concrete evidence without looking at correlations… at least, we know heavier cars are much more lethal from physical tests, no correlation needed. The historical trends also have conflicting factors too, like safety features; e.g. mandatory rear view camera in… 2017, I think it was? So yeah, things can also just be murky if you don’t isolate your variables.
Yea but I added the weight and prevalence of heavier cars and nothing changed around the time of reversal, it was steadily increasing as the amount of pedestrian deaths were going down from 1980 to 2008
Doing a per capita on fatalities should be a requirement in all such statistics. Thanks
Yeah… It would be a mess to sort out with any kind of real methodology. Most likely impossible considering that medical advances in trauma care would be really difficult to factor in the equation.
Well, that’s why you hope that gets controlled with a natural experiment. You can’t, for instance, use a control group in a country with different medical policies, but you could have comparable cities and neighborhoods that, arguably, should have similar medical advancements over time. The closer the control group is, the better your validity should be (too bad there’s no mirror universe!).
There’s a couple other tricks you can use, but honestly my expertise is in education so I’m not sure how widely these are used; Instrumental variables (basically proxies for your target) for instance, like adding property value or something to the model can inadvertently control for things associated with that, like better medical care or infrastructure. You risk over-specifying the model but we have diagnostics that help with that.
(Btw, education, the main concern is there’s a billion possible factors in the home, classroom, society, etc, we can’t directly capture, so that’s how it’s used there).
- National Highway Traffic Safety Administration (NHTSA): The historical pedestrian fatality statistics are sourced directly from the NHTSA’s Fatality Analysis Reporting System (FARS), which tracks traffic fatalities nationwide. https://www.nhtsa.gov/book/countermeasures-that-work/pedestrian-safety
- Environmental Protection Agency (EPA): The historical vehicle weight data and the market shift toward light trucks and SUVs come from the EPA’s annual Automotive Trends Report, which maintains data on every new light-duty vehicle produced since 1975. https://www.epa.gov/automotive-trends/highlights-automotive-trends-report
- Pew Research Center: The timeline of smartphone adoption and mobile internet use in the United States is tracked by the Pew Research Center’s National Public Opinion Reference Surveys. https://www.pewresearch.org/short-reads/2026/01/08/internet-use-smartphone-ownership-digital-divides-in-u-s/
- Insurance Institute for Highway Safety (IIHS): The safety analysis regarding vehicle lethality, front-end geometry (the “leading edge”), and blind spots during pedestrian impacts relies on multiple IIHS studies, including their specific analyses of pedestrian crash lethality by vehicle type. https://www.iihs.org/research-areas/fatality-statistics/detail/pedestrians
It’s not like the phones we had before 2007 weren’t “smart” though, they just generally weren’t touchscreen. People were still just as distracted by them!
While that’s true, it was possible to use t9 without looking at the device and “touch type”. Smart phones, text communication, and big screens is the real distinguishing factor on thebsmsrt phone axis.
