What is the effect of enforcement methods for each priority?

Answer

For each of the five national enforcement priorities, the effect of the enforcement efforts on the associated violations are given. Most efforts are combined with information campaigns and media coverage, and sometimes with new legislation.

Repeat traffic offenders

Although repeat traffic offenders represent less than 0.5% of the population, as drivers they are involved in 6% of all crashes [19]. Plenty of reason, therefore, to pay attention to this group by means of enforcement and accompanying measures.

In the Netherlands, research was done into the effect of warning letters to repeat offenders (defined as individuals who often receive fines for annoying or anti-social traffic behaviour). This showed that these warnings led to fewer violations [29]. The warning letters said that a large number of CJIB orders (orders issued by the Central Fine Collection Agency CJIB) were registered under their names/vehicles, that police would intensify surveillance of said individuals/vehicles, and that they were urged to comply with the traffic rules. The letters were signed by the local district chief. For repeat traffic offenders having received a warning letter, the number of fines decreased by 35% in the following year. For a control group of repeat traffic offenders the number of fines decreased by only 3% in the same period.

In the Netherlands, no research is known about the effect of enforcement focusing on repeat traffic offenders. A theoretically possible measure is the introduction of a progressive penalty system. This encompasses higher penalties as more violations are committed, which may, in theory, motivate repeat traffic offenders to adapt their behaviour. A scenario analysis shows that  – on the basis of a few assumptions – a progressive penalty system for speeding could reduce the number of road deaths in the Netherlands by an annual 5% [30]. Also see SWOV fact sheet Progressive penalty systems in traffic.

Distraction

In the Netherlands, several dozens to well over a hundred road deaths in car crashes involve distraction (also see SWOV fact sheet Distraction in traffic ). A ban on specific forms of distraction, coupled with intensive police enforcement of compliance with such a ban, may result in less distracted driving and fewer road deaths [31]. In the United States, an explicit ban on handheld texting in traffic resulted in a 3% reduction of road deaths (95% CI[i]: 0 to 5%) and 7% (95% CI: 1% tot 12%) fewer road injuries, in spite of limited enforcement of compliance [32] [33]. In the US, increasing penalties for texting (a step-up from a secondary offence to a primary offence) only resulted in a temporary effect (three months) [34]. The authors said the effect of the penalty increase diminished so speedily because of the low level of enforcement.

Red light negation

Red light negation at 50km/hour intersections leads to an approximately fourteenfold greater chance of crashes than red light compliance [35]. At 50km/hour intersections with traffic light control, red light negation is definitely or possibly related to 41 to 67% of crashes [35].

A 2017 meta-analysis [36] shows that the implementation of red light cameras is associated with a 61% reduction of red light negations (95% CI: 56 to 64%), 20% fewer road injuries (95% CI: 5 to 32%), 24% fewer side impacts (95% CI: 10 to 35%) and 29% fewer side impacts involving injuries (95% CI: 14 to 42%), but also with 19% more rear-end collisions (95% CI: 9 to 31). These figures concern effect estimates for intersections monitored by cameras. The latest safety effect estimates of red light cameras, based on 2013-2017 studies, confirm this picture [37]: 24% fewer road injuries (95% CI: from a 51% reduction to a 17% increase) and 29% fewer side impacts involving injuries (95% CI: 21% to 36% reduction), but also 14% more rear-end collisions (95% CI: from an 11% reduction to a 46% increase).

Alcohol and drugs

In 2015, an estimated 12 to 23% of the road deaths in the Netherlands were caused by alcohol [38], while one in ten seriously injured drivers was estimated to have used drugs (see SWOV fact sheet The use of drugs and medicines behind the wheel). The combination of drugs and alcohol use leads to higher risks similar to the higher risks related to drink driving with a BAC > 1,2‰, which may be labelled as an extremely high risk (see SWOV fact sheet Driving under the influence of alcohol).

Between 2003 and 2017, the percentage of drivers with an over-the-limit Blood Alcohol Content (BAC) decreased from 3.4% to 1.4%. The share of serious offenders (a BAC > 1.3 ‰) remained at a steady 0.6 to 0.4%. In 2011, it decreased to 0.3% and in 2017 to 0.1% [39]. A note of caution in this respect is that drivers are ever better able to avoid alcohol checks by updated information on social media/in apps [40]. The positive trend may therefore be biased; a group of drivers is still engaged in drink driving, but is better (than before) able to stay under the radar of alcohol checks. A meta-analysis of the results of 40 studies from different countries [41] shows that drink driving enforcement leads to a 17% decrease in crashes (95% CI: 11 to 22%) not corrected for publication bias, and to a 14% decrease (95% CI: 11 to 18%) corrected for publication bias [ii]. There is no proof that higher penalties for alcohol offenders affect violation reduction (see SWOV fact sheet Driving under the influence of alcohol).

On 1 July 2017, legal limits for drug use in traffic were introduced. No evaluations of the road safety effect of this legislation and its enforcement are known. Drug legislation and enforcement in traffic are expected to have both a general preventive effect and an offender-specific effect. It should be noted, however, that drug and medicine use causes far fewer road casualties than alcohol use does. If traffic enforcement for drugs were carried out at the expense of traffic enforcement for alcohol use, this would have a negative effect on road safety [42].

Speed

International research shows that about one third of fatal crashes are (partly) caused by speeding or by improper speeds (see SWOV fact sheet Speed and speed management).

A meta-analysis of studies on speed enforcement [43] shows that implementation of camera enforcement results in a 7% speed reduction (95% CI: 0 to 13%), a 57% decrease in the share of speed offenders (95% CI: 50 to 64%), a 19% decrease in the number of crashes (95% CI: 14 to 24%), an 18% decrease in the number of road injuries (95% CI: 13 to 23%) and a 21% decrease in the number of serious and fatal crashes (95% CI: 13 to 29%). A meta-analysis specifically focusing on the effects of road section control (average speed check), found that this method resulted in a 30% decrease in all crashes (95% CI: 24 to 36%) and a 56% decrease in serious crashes (resulting in fatal or serious injuries) (95% CI: 42 to 66%) [44].


[i] The confidence interval (CI) is a range of values it is fairly certain the true value lies in. The 95% interval indicates that the true mean is 95% likely to be between the minimum and maximum value. For example: an estimated effect of 57% with a CI of 50 to 65% means that the true effect is 95% likely to be between 50 and 65%.

[ii] Publication bias is the bias that occurs when positive results of scientific research are pubished, but negative or unequivocal results are not.

Part of fact sheet

Traffic enforcement

In the Netherlands, a sustainable road safety approach, in which measures in the fields of Engineering, Education and Enforcement (3Es) are… Meer

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