A perennial topic at this time of year in New Zealand is the holiday period road toll, probably because there is precious little in the way of domestic news at this time of year. The inure that gets discussed the most is the Christmas / New Year Holiday Period Road Toll, defied as the number of road deaths occurring between 4pm on Christmas Eve and 6am on the first work day of 2015 (that being January 5th this year).
Prior to events in France, our news was full of the high road toll – 17 deaths, up from 6 last year. This has caused no end of consternation in the media. People have blamed everything from drug use, to driver attitude to the Police’s low tolerance for speeders to causing the sharp jump in deaths.
There are, in truth any number of reasons one can come up with to explain the increase in deaths, but all of them have the same glaring weakness – they all start from the premise that the road toll has jumped sharply. That the case is made with just two data points irked at me until I decided to investigate a little.
First off, there are three problems I noticed in the media analysis:
- If we are worried about road safety we should arguably focus on fatal crashes rather than deaths. This would make the proper comparison 6 to 15 instead of 6 to 17.
- Furthermore, the holiday period was 11.6 days this year, but 9.6 last year. All things being equal, one would expect the road toll to be 21% higher for that reason alone. The best way to deal with this is to look at crashes per day, instead of just crashes.
- But more than either of these points, the biggest problem is trying to establish a trend with two data points. More data exists, why not look at it?
On the same Ministry of Transport page I linked to above is a table of historic Holiday Period road deaths (just scroll down a little). Plotting out fatal crashes per day, you get the following time series:
Looking at the data in its historical context, the recent number doesn’t look look all that scary. It’s a little on the high side, but still looks like part of the historical downward trend in crashes. This becomes more apparent if you apply a smoother to see how the 2014 figure compares to the historical trend:
The grey region represents a 95% confidence interval around the the trendline, which means that values within that range can be considered typical, given the downward trend. As you can see, the 2014 figure fits comfortably within the confidence interval range, suggesting that it is not anomalous. This makes attempts to explain the road toll figure largely redundant, there is nothing to explain.
There is a natural human tendency to jump to explanations when something happens, especially when that something is emotionally-charged as the deaths of 17 people. But the first rule of explaining a phenomenon is “confirm the phenomenon actually exists”, and that may be the real lesson to learn from this data.
Images created by the author using the ggplot2 and ggthemes packages in R.