Sleep-related vehicle crashes - the relationship to traffic density

Results

Road traffic crashes

• We collected data on 1,904 RTCs which occurred on the 15 different roads and which resulted in death or injury (serious or slight).
• Of these records, 76 files (4%) were categorised as missing, being unavailable to view, for reasons such as files were at court or we were unable to classify into causation categories due to a lack of information.
• Thus, we are able to report on 1,828 RTCs resulting in death or injury (96% of available data).

Sleep-related crashes

• Overall, we found that 17% (316) of the RTCs were sleep related.
• It should be noted that the percentage of crashes caused by driver sleepiness varied between the road 'types', ranging from 3% (A19 single carriageway, North Yorkshire) to 30% (M40 Warwickshire).
• Of the 316 crashes caused by driver sleepiness, 62% (197) were 'possible' and 38% (119) were 'probable'.

Sex and age of drivers

Data on sex and age was available for 14 of the 15 roads, and some records were incomplete, for age in particular. Thus, we are able to report on 86% (1,645) of the full sample. From this smaller sample, 82% (1,343) of the crashes were caused by men, and 18% (302) were caused by women. Figure 1 shows the sex and age of drivers in RTCs.

 

Figure 1 Sex and age of drivers in RTCs

Of the data available, 36% (597) of the drivers were aged 30 or under. (Note that three drivers were under age at 16.)

In SRCs (278), 85% (236) of crashes were caused by men and 15% (42) by women. Figure 2 shows the sex and age of drivers in SRCs.

 

Figure 2 Sex and age of drivers in SRCs

Of the data available on age, 38% (107) were drivers aged 30 or under. Young male drivers are at a much higher risk of having an SRC. This sample shows that young female drivers are at a higher risk of having SRCs than older women. Table 3 shows the proportion of men and women (all ages), and aged 30 and under, for all crashes and for crashes caused by driver sleepiness.

Table 3

 

Sex and age as predictors of crashes caused by driver sleepiness are similar to those for all causes.

Traffic density

We were interested to see what effect, if any, traffic density has on SRCs. The average sample distance of the 15 roads was 21 miles, but one included a short section of dual carriageway of some 8 miles. The maximum distance was 34 miles. For comparison, we calculated the number of crashes per mile, and report on RTCs and SRCs per mile per year.

Figure 3 shows the number of RTCs for each road per mile per year against 24-hour traffic density. For illustrative purposes, motorways are shown using a different data style to other roads.

 

Figure 3 Traffic density versus fatal and injury RTCs per mile per year

As expected, the rate of injury RTCs rises with an increase in the volume of traffic. There is a strong positive correlation (all roads) (r = 0.77, p < 0.01). Motorways show a higher volume of traffic and higher accident rate per mile than the 'A' and 'B' roads.

We were interested to see if there is a similar correlation for SRCs. Figure 4 shows SRCs for each road per mile per year against 24-hour traffic density.

 

Figure 4 Traffic density versus fatal and injury SRCs per mile per year

The rate of SRCs per mile per year is similarly correlated with traffic density as for crashes from all causes (r = 0.77, p < 0.01). SRCs rise as traffic density increases, as do all RTCs.

Given that we have identified the early hours of the morning when SRCs are more likely to occur, we were interested to see if there is a significant correlation between these 15 roads and their differing traffic density at this time of day. Figure 5 shows the traffic flow during 02:00-05:59 hours for each road, and crashes occurring per mile per year during these hours.

 

Figure 5 Traffic density versus RTCs per mile per year, 02:00-05:59 hours

We found a similar relationship between traffic volumes and RTCs in the early hours of the morning, with RTCs rising as traffic increases. There is a highly significant correlation here: r = 0.91, p < 0.01. Again, motorways have a higher crash rate than the other roads. Figure 6 shows the relationship between SRCs per mile per year and traffic volume during the early hours of the morning.

 

Figure 6 Traffic density versus SRCs per mile per year, 02:00-05:59 hours

Again, a significant correlation was shown for SRCs (all roads) (r = 0.9, p < 0.01). The likelihood of having an SRC during the early hours of the morning is similar to that for all causes. We conclude that the incidence of crashes is largely due to the driver rather than the road type. Given that we have already stated that boredom greatly increases sleepiness, and, therefore, driving on undemanding and monotonous roads such as motorways would enhance driver sleepiness, it seems most likely that crashes caused by drivers falling asleep at the wheel will be associated with driving in low traffic. However, Figure 6 shows that on motorways, with higher traffic flows, the likelihood of an SRC during 02:00-05:59h is greater than on 'A' and 'B' roads. There are some differences between roads that are not explained by these factors alone.

We were interested to see if there is a relationship between the proportion of crashes caused by driver sleepiness and traffic volumes. Figure 7 shows traffic density and SRCs as a proportion of RTCs on motorways and other roads.

 

Figure 7 Traffic density versus per 24-hour SRCs as a proportion of total road crashes

Our sample of 15 roads shows an interesting relationship between 24-hour traffic volumes and SRCs as a proportion of all RTCs. On the 'A' and 'B' roads the proportion of SRCs increases with an increase in vehicle density. This trend seems to continue between 'A' roads and motorways, to a motorway with an AADT of just under 80,000. However, the proportion of SRCs decreases when the 24-hour traffic volume is 80,000, and continues to decrease as traffic flow increases.

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