# SWITRS: Accidents After Daylight Saving Time Ends

We all hate the change to daylight saving time (DST) in the spring; it makes us tired, grumpy, but worst of all it causes us to crash our cars at a higher rate! The end of DST is not as universally reviled, probably because we get back the hour of sleep we lost earlier in the year, but Varughese & Allen found that there was still a “significant increase in number of accidents on the Sunday of the fall shift from DST”.1

With the SWIRTS data that I collected, and the analysis code I developed for my post last year looking at car accidents after the DST change, it should be pretty easy to check if I see the same trend as Varughese & Allen.

The Jupyter notebook used to perform this analysis can be found here (rendered on Github).

## Accident Ratio

Just like last time, I will look at the number of accidents on the days following the end of DST. In order to help cancel out effects other than the time change—like the fact that accident rates vary by 30% depending on the year—I will divide each day’s total by the number of accidents a week later, when people are presumably back to normal.

Unlike last time, I am not normalizing by the number of accidents two weeks after the change. The reason for this is simple: that’s Thanksgiving week, and as I showed before the number of accidents is greatly reduced during the holidays.

Just as last time, the violin plots below show the distribution of these ratios from the years 2001 to 2017. A value greater than 1 means that there are more accidents during the week when DST starts than two weeks after.

There is, on average, a larger number of crashes on Sunday when the time changes as seen by Varughese & Allen. However, the same excess is not seen when a different normalization is chosen, like using the week before or two weeks after.2 The week before has different lighting during commute times and so it is easier to dismiss, but two weeks after has similar lighting.

## t-Test

Instead, we turn away from our “chi-by-eye” test and do an actual statistical test: a two-tailed paired t-test, the same test used by Varughese & Allen. They find a significant (p < 0.002) increase in the number of deadly accidents on the Sunday that DST ends, but I do not (p = 0.082).

Our methods are different in a few key ways:

• They look only at fatal accidents while I look at all.
• They compare to the mean of the week before and after while I use only the week after.

If I reproduce their methods with my dataset, I still do not find a significant result (p = 0.158).

As for California, Kansen Chu has once again given us a chance to get rid of the time change with Prop 7. His earlier bill failed, so he has gone directly to the voters this time. Although permanent DST is not the ideal solution, I’m still for getting rid of the time change itself!

1. Varughese, J. and Allen, R., Fatal accidents following changes in daylight savings time: the American experience, Sleep Medicine, Volume 2, Issue 1, p. 31 - 36, doi: https://doi.org/10.1016/S1389-9457(00)00032-0

2. The large deviations on the two week plot for Thursday and Friday are explained by Thanksgiving and Black Friday.