Young driver accidents in the UK: The influence of age, experience, and time of day

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Abstract

Young drivers, especially males, have relatively more accidents than other drivers. Young driver accidents also have somewhat different characteristics to those of other drivers; they include single vehicle accidents involving loss of control; excess speed for conditions; accidents during darkness; accidents on single carriageway rural roads; and accidents while making cross-flow turns (i.e. turning right in the UK, equivalent to a left turn in the US and continental Europe).

A sample of over 3000 accident cases was considered from midland British police forces, involving drivers aged 17–25 years, and covering a two year period. Four types of accident were analysed: right-turns; rear-end shunts; loss of control on curves; and accidents in darkness. Loss of control on curves and accidents in darkness were found to be a particular problem for younger drivers. It was found that cross-flow turn accidents showed the quickest improvement with increasing driver experience, whereas accidents occurring in darkness with no street lighting showed the slowest rate of improvement. ‘Time of day’ analyses suggested that the problems of accidents in darkness are not a matter of visibility, but a consequence of the way young drivers use the roads at night. There appears to be a large number of accidents associated with voluntary risk-taking behaviours of young drivers in ‘recreational’ driving.

Introduction

The aim of this paper is to study the effects of age, experience, and time of day on a sample of young driver accidents in the UK. Forsyth (1992) quotes figures from the UK that show male drivers between the ages of 17 and 20 years having an average of 440 injury accidents per 100 million km driven. The average for all male drivers was 106 injury accidents. Comparable figures for female drivers in this age range were 240 versus 125 injury accidents per 100 million km driven.

Accident rates appear to drop rapidly above this age range. Figures for male drivers in the age range 20–24 years, for example, show a drop to 180 injury accidents per 100 million km driven. While this is a massive drop, it still represents an injury accident rate that is nearly 70% higher than the baseline for all male drivers.

Methodologically, it has always been difficult to separate the effect on accident frequencies of simple age compared with the experience of the driver concerned. Does a 24 year old with 6 months driving experience have the same risk of an injury accident as a 17 year old with equivalent experience, for example? If this were true, the effect would not show up in accident statistics because there are many more 17 year olds with only 6 months driving experience than there are 24 year olds with 6 months experience. The most common measure of experience is, nevertheless, time in years since passing a driving test. Waller et al. (2000), for example, looked at the decline in offences and crash incidents over seven years from the date of full licence attainment. The odds of any driving offence committed being serious decreased by approximately 8% per year of licensure, independent of gender. Similarly, the odds of an at-fault crash occurring decreased overall around 6% per year of licensure, but the decline was more than twice as fast for women as for men. However, in any given sample of drivers, age and experience when measured in this way are very highly correlated, and this makes any separate effects very hard to determine. In the end, as Jonah (1986) observed, “the attempt to separate the two concepts may well prove fruitless” (p. 256).

Attempts have been made to define experience as the distance in miles/km driven since the test pass date, but not only is this difficult to determine, it also complicates the issue owing to the exposure effect. The driver in question may be more experienced as a result of driving a greater distance, but the greater the distance travelled, the more likely it is that he/she will have an accident. However, in Jonah's (1986) review of Canadian research on the subject, he concludes “…even when one controls for the quantity and quality of exposure to risk, young drivers are still at the greatest risk of casualty accident involvement, particularly those aged [under] 19” (p. 257).

The causation of real road accidents can be a difficult phenomenon to study. One possible solution to this is the use of methodology that investigates road accidents after they have occurred.

One such well known approach involves the use of multi-disciplinary accident investigation teams that travel to the site of accidents soon after they occur to collect data. However, in a review of the work of multi-disciplinary team research world-wide, Grayson and Hakkert (1987) pointed out several disadvantages to this method. Operational costs are very high, and only a small number of accidents can be studied. The accidents sampled are bound to be of a heterogeneous nature, which works against any approach that aims to study a specific problem.

Case study methods, using police accident reports, were used successfully by Clarke et al. (1998b), and placed more emphasis on the interpretation of causal patterns by human coders, using the powers of a computer database for the later stages of storing, sifting, and aggregating explanatory models of individual cases.

In a previous study at Nottingham, Clarke et al. (1998a) discovered that young drivers under the age of 25 years were more than three times more likely to be involved in cross-flow turning accidents (i.e. turning right either onto or off a more major road, in the case of UK roads; an equivalent of the left turn in most other countries) than typical mileage travelled each year by this age group would lead one to expect. West and French (1993) discovered that young drivers were at greater risk of ‘passive’ right of way violations, i.e. where another driver turns in front of them. West says that this is most likely to occur due to a combination of such factors as speeding, slow perception of potential hazards, and a “[determination] to assert their own right of way.”

Rear-end shunts (a term commonly used in the UK for accidents where one vehicle runs into the rear of another, ‘shunting’ it forward) have been found to be amongst the most common types of accidents for all drivers. West and French (1993) estimated that at least 30% of all accidents on UK roads were shunts. While many of these accidents are seemingly trivial, whiplash injuries that can result from them are a significant problem. West, in his analysis of different types of shunt, found that “active involvement in shunts was a function of being young and male.”

When the type of manoeuvre in aggregate statistical records such as STATS19 (a national dataset gathered by police traffic officers in the UK) is examined, it can be seen that younger drivers (17–19 years) are involved in twice the proportion of accidents while negotiating a curve that older drivers are (in this example, those aged 30–39 years). This is a feature associated with the over-representation of younger drivers in single vehicle accidents.

Accidents for all drivers per unit of distance travelled are much higher during the hours of darkness than during the daylight. Laapotti and Keskinen (1998) found that fatal loss of control accidents involving young male drivers typically took place during evenings and nights. Ward et al. (2004) found that the casualty rate for the youngest group of males (aged 17–20 years) remained much higher (even when exposure had been taken into account) than for other male drivers, with a large increase in the early evening becoming larger again between 22.00 and 01.59 h. Williams (2003) similarly found that the hours of 21:00 to midnight have both high fatal crash risk and high miles of teenage driving. Ferguson (2003) suggests that there is little evidence that these accidents are caused by fatigue, as might be expected.

The aim of this paper is to examine a sample of road accident cases of the four types specified above, involving young UK drivers, to identify common causal factors in each instance.

Section snippets

Method

Our method relies on the human interpretation of road accident case reports by a special team of researchers with driving experience in several types of vehicle; training in research methods; and several years experience in the causal interpretation of road accident files.

Initially, we assign causes (or ‘contributory factors’ as the UK police call them) to individual cases on a non-statistical basis. These may be multiple necessary causes (causal chains), or single causes (faults), depending on

Driver age and accident type

The results detailed below relate to the entire dataset (n = 3437) except where otherwise stated.

An overview of the entire sample reveals that accidents occurring in the hours of darkness are notably high in 17–19 year old drivers. In addition, this appears to be a problem for young males in particular, as Fig. 1 shows. By contrast, rural curve accidents involving young females are relatively rare.

Although young females have a higher percentage of shunts in their total accidents, when these cases

Conclusions

There are important differences in the way the four types of young driver accidents described above seem to occur and there are definite propensities for accidents of various types depending on driver age, gender, experience, and the use young drivers make of the road at different times of the day.

Generally, being a young (17–19 years) male accident-involved driver means being significantly more likely to be involved in accidents during the hours of darkness; on rural curves; and actively

Acknowledgements

This study was funded by the Transport Research Laboratory, Crowthorne, England, UK, and this paper is abridged with permission from the final project report TRL 542 “In Depth Accident Causation Study of Young Drivers.” We are most grateful to Nottinghamshire and Derbyshire Police for their patient assistance in locating suitable cases for analysis; to members of Nottinghamshire County Council Accident Investigation Unit for assistance with the selection of the sample; and to Geoff Maycock of

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