Trends and Patterns

Count of number of casualties for each Vehicle Manoeuvre.

The data has been displayed in a bar charts, as this best communicates the comparison between the categories of data (Alexander & Wiley and Sons, Inc. Staff, 2014).

The colour shows details about accident severity. The most number of Fatal accidents happen when the vehicle was known to be going ahead. The next categories which had the most numbers of casualties were when the vehicles were turning right or when stopping or slowing down.

There were generally very few fatal accidents in comparison to serious accidents. Whilst a majority of accidents were slight casualties, it is known that many casualties which are categorised as slight are under reported.

The category with the fewest accidents appears to be when waiting to turn, where it could be assumed that the vehicles would have been stationary, therefor these would likely be low speed collisions with less severe consequences.

Sum of number of casualties for each road surface conditions. Colour shows details about light conditions at the time of the accident.

This bar chart illustrates that 441,327 accidents occurred in dry road conditions and 346,994 of these accidents were in the sub-category of occurring in day light.

The next most dominant category were the accidents occurring in wet road conditions. While one might expect wet conditions to lead to more accidents because of unsafe driving conditions, it is important to remember that proportionally road conditions are dry for more of the time throughout the year than they are wet or damp. This is why there are still more accidents in 'safer' dry conditions. The proportions of light conditions in this category were similar to those displayed in the dry conditions.

The data in this next chart shows a normal distribution of casualties at different age brackets; a bell curve that is slightly skewed to the right hand side. Again the reduced number of casualties for drivers below 21-25 is almost certainly because there are less total drivers at those ages.

The chart identifies female drivers have less casualties in comparison to males. Generally the ratio appears to average at 70:30 making men more than double the risk of female road users, assuming that there are a similar number of men and women driving.

All the trends from the above charts indicate that the most number of accidents happen:

• While driving straight ahead.
• In dry weather conditions.
• During day light hours.

All these factors might suggest that the accidents are occurring as a result of speeding, as opposed to slippery road conditions or poor visibility. Upon looking into this further, the chart below was produced.

The trend of count of Accident Index for speed limit. Colour shows details about Sex of Driver.

We can clearly see there is a peak at the speed limit of 30 mph where most accidents occur. When comparing males and females, females were in approximately less the half the number of accidents that males were in. It opens up questions such as:

• Are males more reckless drivers than females?
• What is it about female drivers that decreases the number of accidents by more than half of male drivers?
• What could reduce the number of male accidents to the number of female accidents?

It can be said from this chart that male drivers are at significantly more risk than females.

It is interesting that there are 2 peaks on the charts, one at 30 mph and the other at 60 mph. Once again this is a reflection of how the vast majority of roads in the UK are either 30 mph or 60 mph speed limits. This means most accidents will occur on these roads because you are much less likely to be driving on anything else, not necessarily because they are any less safe than other speed limits.

It would be of interest to look at the data as a percentage of accidents per total number of drivers on the road. For example, comparing a theoretical accident rate of 0.1% during dry conditions with a rate of 0.3% in wet conditions. This would give a better indicator of how each condition affects the likelihood of having an accident, rather than being skewed by sample bias.

The following pages in the trends and patterns drop down menu explore the data further.

References:

• Alexander, M. & Wiley and Sons, Inc. Staff (2014) Data Visualization for Dummies. Somerset: John Wiley & Sons, Incorporated.