Handbook of Traffic Psychology. DOI: 10.1016/B978-0-12-381984-0.10033-5

Copyright � 2011 Elsevier Inc. All rights reserved.

Chapter 33

471

Public Policy

Rune ElvikInstitute of Transport Economics, Oslo, Norway

Public policy refers to any action taken by public bodies inorder to influence highway safety. Although transportpolicy in general has several objectives, the focus in thischapter is on policy designed to improve safety. The role oftraffic psychology in contributing to an effective roadsafety policy is discussed. The following main questionsare addressed in this chapter:

1. What are the principal elements of road safety policy?At what stages of policy making can traffic psychologycontribute?

2. What is the scope for improving road safety by applyingknowledge gained in traffic psychology and relateddisciplines?

It is shown in this chapter that traffic psychology can makea major contribution to improving highway safety byinforming public policy.

1. AN ANALYTIC MODELOF POLICY MAKING

Figure 33.1 shows an analytic model of highway safetypolicy making (Elvik & Veisten, 2005). The model is notintended as a description of actual policy making. It isa purely analytical model intended as a logical frameworkfor identifying the types of reasoning and activities thatconstitute policy making. The stages identified by themodel form a logical sequence; they should not be inter-preted as a chronological ordering.

The first stage of policy development is to find out whatthe problem is and identify factors that contribute to it. Inshort, what are the most important highway safety problemsandwhat are themost important factors contributing to theseproblems? The next stage is to develop targets for improvingsafety and decide on whether these targets should be quan-tified or not. Once the ambitions for improving safety havebeen defined, a broad survey of potentially effective safetymeasures (stage 3) is needed to identify those measures thatcan make the largest contribution to reducing the number offatalities and injuries. However, for various reasons, it maynot be possible to introduce all effective safety measures; an

explicit consideration of constraints on safety policy canhelp in developing realistic policy options (stage 4). Therewill very often be more than one safety measure that canaddress a given safety problem; hence, developing alterna-tive policy options that can be compared is instructive(stage 5).

A key activity in policy development is to estimate theexpected effects of safety measures on the number of acci-dents or the number of killed or injured road users (stage 6).These estimates should ideally be based on the best availableknowledge regarding the effects on safety of variousmeasures. Any prediction (i.e., prior estimate) of the safetyeffects of a programwill be uncertain, and it may be useful toexplicitly consider sources of uncertainty and how to reduceuncertainty (stage 7). As alreadymentioned, policy is alwaysmade within constraints that are not necessarily chosen orwanted by policy makers; usually, therefore, severalconsiderations are relevant for policy choice, requiringcomplex trade-offs (stage 8). Once it has been decided toimplement a set of safety measures, the effects of thesemeasures should be evaluated in order to increase knowledgeof their effects for use in future policy making (stage 9).

Traffic psychology is not equally relevant at all stages ofpolicy making. It can contribute in particular at stages 1e3,6, and 9. A brief review of the potential contribution oftraffic psychology to policy making follows.

2. OUTLINE OF THE POTENTIALCONTRIBUTION OF TRAFFICPSYCHOLOGY TO POLICY MAKING

2.1. Unsafe Road User Behavior as a RoadSafety Problem (Stage 1 of Policy Making)

Road accidents are influenced by many factors. One of themost important is unsafe road user behavior. This includesspeeding, drinking and driving, not wearing protectivedevices, talking on cell phones while driving, and a host ofother forms of behavior. No study has assessed the contri-bution of all types of unsafe road user behavior to accidentsor injuries. However, Elvik (2010a) tried to assess the risk

FIGURE 33.1 An analytical model of highway

safety policy making

472 PART | VI Interdisciplinary Issues

attributable to 15 different violations of road traffic law inNorway. Table 33.1 reproduces the estimates of the riskattributable to these violations. These estimates are highlyuncertain, but it is not possible to estimate statistically theuncertainty of each of the estimates. Confidence intervalsare therefore not provided.

Attributable risk shows the potential reduction of thenumber of fatalities or injured road users if the violation iseliminateddthat is, replaced by driving that complies withthe law. It is estimated as follows (Rothman & Greenland,1998):

Attributable risk ¼ PE$ðRR� 1ÞðPE$ðRR� 1ÞÞ þ 1

(1)

where PE denotes the proportion of exposure for which therisk factor is presentdfor example, the proportion ofvehicles exceeding the speed limit. RR is the relative riskassociated with a violationdfor example, it is 2 if risk isdoubled. If a violation represents 20% of traffic and doublesrisk, the risk attributable to it is 0.167. This means that byeliminating the risk factor, the number of accidents can bereduced by 16.7%, given an unchanged amount of travel.

It does not make sense to add the estimates of attribut-able risk presented in Table 33.1. To estimate the potential

for improving safety by eliminating all the violations, onecan apply what has been termed the “method of commonresiduals” (Elvik, 2009a). The residual of an estimate ofattributable risk is its complementary valuedthat is, theshare of fatalities or injured road users not eliminatedby eliminating the risk factor. Thus, for speeding, theresidual with respect to fatalities is 1� 0.230¼ 0.770.By applying the method of common residuals, it canbe estimated that by eliminating the violations listed inTable 33.1, the number of fatalities can be reduced by61% and the number of injured road users reduced by35%. For fatalities, the estimate is

1� ð0:770$0:834$0:867$0:907$0:928$0:950$0:962$0:974$0:976$0:981$0:990$0:994$0:998$0:998$0:998Þ¼ 1� 0:390 ¼ 0:610

These estimates are probably too optimistic because

violations tend to be correlated. A more conservativeversion of the method of common residuals, whichattempts to account for correlations, suggests that elimi-nating the violations listed in Table 33.1 can reducefatalities by 52% and injuries by 32%. For fatalities, thiswas estimated as

TABLE 33.1 Risks Attributable to Violations of Road Traffic Law in Norway

Estimate of attributable risk (proportion) with

respect to fatalities and injured road users:

Sorted by contribution to fatalities

Violation Fatalities Injured road users

Speeding 0.230 0.094

Drinking and driving 0.166 0.034

Not wearing seat belts 0.133 0.032

Health problems in drivers 0.093 0.080

Use of illicit drugs and driving 0.072 0.027

Service and resting hours 0.050 0.022

Not yielding at intersections 0.038 0.038

Not yielding to pedestrians 0.026 0.025

Use of cell phone 0.024 0.024

Red light running 0.019 0.019

Illegal overtaking 0.010 0.003

Engine tuning of motorcycles 0.006 0.007

Short following distance 0.002 0.012

Lack of child restraints in cars 0.002 0.001

Non-use of daytime running lights 0.002 0.002

Source: Data from Elvik (2010a).

473Chapter | 33 Public Policy

1� 0:3900:770 ¼ 1� 0:484 ¼ 0:516

Although these estimates are not very precise, they areprobably correct in suggesting that major improvements inhighway safety are possible by reducing or eliminatingunsafe road user behavior. Traffic psychology can contributeto informing policy in many ways by studying road userbehavior. The contributions include the following:

1. Identifying and describing the prevalence of variousforms of potentially unsafe road user behavior. Progressin unobtrusive techniques of observation, as illustratedby a large-scale, in-vehicle naturalistic study (i.e.,N¼ 100 cars; Klauer, Dingus, Neale, Sudweeks, &Ramsey, 2006), makes it possible to survey behaviorthat used to be difficult to observe.

2. Estimating the risk associated with unsafe road userbehavior, thus providing knowledge about factorscontributing to accidents and the size of theircontributions.

3. Studying why unsafe road user behavior is widespread:What are the motivations underlying this behavior? Canunsafe behavior be reasonably modeled as (subjec-tively) rational from the road users’ point of view? If

road users behave unsafely for reasons they think aregood, does this imply that efforts designed to modifybehavior will be ineffective?

4. To what extent can unsafe road user behavior be influ-enced by means of technical solutions that make suchbehavior impossible or unpleasant?

These are just some of the questions that are relevant forpolicy development.

2.2. Developing Targets That Are Motivating(Stage 2)

Many countries have developed national safety programs thatare based on a quantified target for improving road safety(Organisation for Economic Co-operation and Development(OECD), 2008). International bodies, such as the OECD,recommend setting quantified targets for improving safety.However, setting targets that will motivate both public bodiesand others that influence highway safety to make an extraeffort involves a number of complexities (Elvik, 2008):

1. The targets should be supported by the top level ofgovernment and be developed in a process that involves

474 PART | VI Interdisciplinary Issues

all relevant stakeholders to ensure a consensus on thetargets and a commitment to following them up.

2. The targets set should be challenging but in principleachievabledthey should have the “right” level ofambition.

3. There should not be too many targets in view of theavailable policy instruments designed to realize them.

4. There should be mechanisms ensuring that responsibleagencies have sufficient resources at their disposal toimplement all safety measures that are needed to realizethe targets.

5. There should be a system for monitoring progress inrealizing targets and providing feedback to responsibleagencies on their performance.

6. Incentives should exist to ensure commitment to targetsfrom all agencies responsible for realizing them.

Again, both general psychology and traffic psychology cancontribute to ensuring that the targets set for improving roadsafety will be as effective as possible. For example, psycho-logical research (Locke & Latham, 2002) has found thattargets that are ambitious are associated with better perfor-mance than less ambitious targets. On the other hand, there isrisk of fostering a sense of helplessness by setting overlyambitious targets. Such targets may be discounted as utopianand may not have the motivating effects that challenging butachievable targets often have (Anderson & Vedung, 2005).

To set ambitious but challenging targets, it helps toknow what is the potential for improving safety by intro-ducing various safety measures. A so-called “bottom-up”approach for setting targets derives a “realistic” target byadding up the estimated effects on safety of a number ofsafety measures that can be implemented. A “top-down”approach, on the other hand, approaches target setting froma more idealistic point of view. In practice, good targetsinvolve a mixture of idealism and realism.

2.3. Surveying Potentially Effective HighwaySafety Measures (Stage 3)

Many measures may contribute to improving road safety. Acomprehensive overview of such measures can be found inThe Handbook of Road Safety Measures (Elvik, Høye, Vaa,& Sørensen, 2009), which describes a total of 128 measuresaddressing the following elements of the transport system:

1. Highway design (20 measures)2. Highway maintenance (9 measures)3. Traffic control (22 measures)4. Vehicle design, safety standards, and protective

devices (29 measures)5. Vehicle inspection (4 measures)6. Driver training and regulation of professional driving

(12 measures)

7. Public education and information (3 measures)8. Police enforcement and sanctions (13 measures)9. Post accident care (3 measures)

10. General-purpose policy instruments (13 measures)

Traffic psychology tends to be given blame or credit forsafety measures that are directed at behavioral factors, suchas driver training, information campaigns, or policeenforcement. It is correct that traffic psychology has beeninvolved in developing many of these measures, but it isa misconception to think that traffic psychology does notcontribute to measures involving the technical componentsof the system. Knowledge produced by human factorsexperts regarding, for example, reaction times, cognitivecapacity, visual performance, ergonomics, and many otherspecialties, has contributed importantly to current designstandards for highways, traffic control devices, and auto-mobiles. A freeway, for example, has been designed tominimize the task demands on drivers. It has no accesspoints to properties along the road. There are no at-gradeintersections. There are no surprising, sharp curves or steephills. Pedestrians and cyclists are not permitted to usefreeways. The road surface is smooth. Oncoming traffic isseparated by a median. The risk involved in striking fixedobstacles has been reduced by impact attenuators. In short,a freeway is the type of road a psychologist might want todesign in order to make driving as simple as possible andthus minimize the probability of errors being made. Theeffects on safety of measures targeted at road user perfor-mance and behavior are discussed more extensively inSection 3 (see also Chapter 16 for a focus on humanfactors).

2.4. Estimating the Expected Effects of SafetyMeasures (Stage 6)

The Handbook of Road Safety Measures (Elvik et al., 2009)contains a wealth of information regarding the effects ofroad safety measures. However, a mechanical and uncrit-ical use of the book is not recommended when developingroad safety policy and estimating the effects of road safetymeasures. There are three main problems:

1. The Handbook of Road Safety Measures often statesonly an average effect of a measure, although the effectcan reasonably be assumed to vary systematically,depending, for example, on characteristics of themeasure.

2. The quality of studies that have evaluated the effects ofa measure may vary, and a summary estimate of effectshould be based on the best studies.

3. Not all measures have been evaluated with respect totheir effect on accidents; in particular, this effect will beunknown, but has to be predicted, for new measures.

475Chapter | 33 Public Policy

Traffic psychology can contribute in particular with respectto the second and third of these points. Psychology hasa long tradition of experimental research, and psychologistshave contributed to the development of comprehensivemethods for assessing the quality of research (Shadish,Cook, & Campbell, 2002). Any application of the results ofroad safety evaluation studies should rely on a criticalassessment of the quality of this research because poorlydesigned studies tend to produce misleading estimates ofthe effects of road safety measures. This topic is discussedin greater detail in Section 3.

The effects of well-established road safety measures onaccidents reflect the net impacts of all causal pathwaysgenerating these impacts. In particular, road user behav-ioral adaptation will be endogenous with respect to effectson accidents; the effects on accidents always capture theeffects of any road user behavioral adaptation. In otherwords, there is no need to “adjust for” behavioral adapta-tion when predicting the effects of measures whose effectson accidents have been extensively evaluated. The fact thatroad users adapt behavior is nevertheless not unproblematicbecause it normally reduces, and may even eliminate, theintended safety effect of a measure.

This is different in the case of new road safety measures.To predict their effects on accidents, it is necessary topredict whether behavioral adaptation is likely to occur. Aframework for analyzing and predicting the effects of roadsafety measures has been proposed by Elvik (2004) and isshown in Figure 33.2.

A road safety measure will influence safety by modi-fying one or more basic risk factors that are associated withaccidents. These risk factors include speed, mass, roadsurface friction, visibility, compatibility (differences inmass and crashworthiness between vehicles), complexity(the richness of information in a traffic environment),predictability (the accuracy of expectations), road userrationality, road user vulnerability, and system forgiveness(the safety margins built into the system). Changes in theserisk factors influence the structural safety margindthat is,the safety margin built into roads and vehicles. Thesechanges are sometimes referred to as the “engineeringeffect” of a road safety measure (Evans, 1985). The effect

of a road safety measure on accidents, however, is alsodetermined by the behavioral adaptation it may elicit.

Behavioral adaptation is sometimes in response to therisk factors a road safety measure is intended to influence,but it takes place before the measure is introduced. InFigure 33.2, this kind of behavioral adaptation is referred toas antecedent behavioral adaptation. As an example, driversmay adapt behavior to the technical condition of their cars.Technical defects may therefore not increase the risk ofaccident; once these defects are repaired following periodicmotor vehicle inspection, drivers adapt behavior again,knowing that the car is in good technical condition. The netresult could be that periodic motor vehicle inspection hasno effect on accidents. Behavioral adaptation will some-times also be the result of a safety measure, particularly ifthe measure is easily noticed, is associated with a largeengineering effect, and road users can obtain an advantageby changing behavior (Amundsen & Bjørnskau, 2003;Bjørnskau, 1994).

Will new safety measures, such as intelligent speedadaptation (ISA), intelligent cruise control, lane departurewarning, or fatigue monitoring, lead to behavioral adapta-tion? ISA is a system that supports the driver in complyingwith speed limits. There are several versions of the system;one of them makes exceeding the speed limit impossible byregulating fuel supply to prevent acceleration to a speedhigher than the speed limit. Because speeding is known tobe an important risk factor for accidents and injuries (Elvik,2009b), ISA would seem to be a potentially effective roadsafety measure. However, will drivers adapt their behaviorto ISA? One common form of behavioral adaptation,increasing speed, is blocked by the system. Drivers could,however, adapt by becoming less alert. Some maneuvers,such as overtaking, might require more time and thusbecome more risky. Speed is such a powerful risk factorthat it is difficult to believe that behavioral adaptationwould entirely eliminate the effects of ISA, but it couldreduce them.

Intelligent cruise control is also a system that exists inmany versions. Themost technically advanced will warn thedriver if headway becomes too small and activate the brakesif the driver fails to react. Despite the huge number of

FIGURE 33.2 A model of causal chains

that generate effects on accidents of

a road safety measure. Source: Based on

Elvik (2004).

476 PART | VI Interdisciplinary Issues

rear-end collisions, maintaining a safe distance fromvehicles ahead is generally a task drivers perform very well.The reliability of drivers in braking and stopping safely incar-following situations is probably well in excess of 999 in1000. The challenge for intelligent cruise control is to designa system that is more reliable than the average driver.Whereas drivers can take account of factors such as a slip-pery road surface, going downhill, and the possibility ofavoiding a collision by steering to the right or left, a technicalsystem may not be able to adequately handle thesecomplexities. If drivers come to rely fully on intelligentcruise control to perform a task currently done manually,there is a significant risk that the system will not improvesafety.

Lane departure warning devices present similar limita-tions. A lane departure warning system is basically unableto determine if a lane departure is intentional or not. Ifintentional, it may not necessarily involve any additionalrisk. The driver may change lanes on a freeway, havingchecked carefully that it can be done safely, but forget touse the indicator. The warning system may then be acti-vated, possibly irritating the driver. Another problem is thata lane departure warning system may not function if lanemarkings or edgelines are covered by snow or are worn out.In short, the system may be unreliable and may activatewarnings the driver perceives as false alarms. There is a riskthat drivers may ignore the system, thus diminishing itspotential effects on safety.

Concerning fatigue monitoring systems, the major issueis still whether a reliable system can be developed. Ifa technically reliable system is developed, there is clearlya risk that drivers may utilize the system to drive when theyare fatigued, trusting the system to wake them up in time. Inshort, an important task for traffic psychology is to try topredict if, and the extent to which, new road safetymeasures will be associated with behavioral adaptationsthat may reduce or completely offset the intended effects ofthese measures on safety.

2.5. Evaluating the Effects of Road SafetyMeasures (Stage 9)

To continue to improve highway safety, it is important toevaluate the effects of as many safety measures as possible.With its long tradition of experimental research, trafficpsychology can make a key contribution to evaluation byhelping to design experimental evaluation studies. There arefew such studies (Elvik, 1998), but if the huge advantages ofrandomized, controlled trials were more widely recognized,road safety evaluation could become a more rigorousdiscipline, relying less on imperfectly controlled observa-tional studies than it does today. Psychologists should regardit as one of their professional duties to advocate the use ofrandomized, controlled trials whenever they see a possibility

for implementing this design. When experimental studydesigns cannot be implemented, researchers should optfor the best possible quasi-experimental design (Shadishet al., 2002).

3. THE SCOPE FOR IMPROVING ROADSAFETY: AN OVERVIEW ANDA DISCUSSION OF SOME MEASURES

3.1. A Policy Analysis for Norway

Highway safety has been greatly improved in many highlymotorized countries in the past 35e40 years (Elvik,2010b). However, there is still potential for considerableimprovement of highway safety. A policy analysis forNorway (Elvik, 2007) indicated that the number of roadaccident fatalities could be reduced by more than 50% by2020 if all cost-effective road safety measures are fullyimplemented. The term “cost-effective” denotes a roadsafety measure whose benefits, according to costebenefitanalysis, are greater than its costs. In the road safety policyanalysis made for Norway, four main options for roadsafety policy were developed. Table 33.2 shows the esti-mated effects on the expected number of fatalities of themain categories of safety measures that were included ineach policy option.

The mean annual number of fatalities during 2003e2006was 250. In the baseline situation, involving no new safetymeasures but continued maintenance of measures already inuse, the number of fatalities is expected to increase to 285 in2020. These assumptions are common to all policy options.The following rows of the table show the estimated contri-butions of main categories of safety measures to reducingthe number of road accident fatalities in Norway until 2020.

Exogenous vehicle safety features are those already onthe market and whose use is expected to increase in the nearfuture without government regulation. These include airbags, electronic stability control, seat belt reminders,enhanced neck injury protection, and high ratings in new carassessment programs. New vehicle safety features includeISA, intelligent cruise control, eCall (automatic crash noti-fication), and event data recorders. Road-related measuresconsist of several large or small highway improvements,such as bypass roads, lighting, guardrails, and convertingintersections to roundabouts. Enforcement includes bothspeed cameras and traditional enforcement performed byuniformed police officers. New legislation includes makingbicycle helmets and pedestrian reflective devices mandatory.Road user-related measures are older driver retraining andstimulating more hours behind the wheel before licensing ofyoung drivers.

Policy option A, optimal use of road safety measures, isnot very realistic. It includes introducing a number of newmotor vehicle safety standards, which the Norwegian

TABLE 33.2 Potential Reduction of the Number of Road Accident Fatalities in Norway

Expected annual number of road accident fatalities: Contribution of main categories of road

safety measures to reducing fatalities

Baseline values and main

contributing factors

Policy option A:

Optimal use of road

safety measures

Policy option B: Optimal

use of measures controlled

by the Norwegian government

Policy option C:

Continue present

policies

Policy option D:

Strengthen

present policies

Baseline number of fatalities and forecast for 2020 (common to all policy options)

Mean 2003e2006 250 250 250 250

Expected in 2020 as a resultof traffic growth

285 285 285 285

Reduction of the number of fatalities attributable to main categories of measures

Exogenous vehicle safetyfeatures

49 55 58 55

New vehicle safety features 42 0 0 0

Road-related measures 26 28 34 39

Enforcement-related measures 24 29 3 43

New legislation 4 0 0 5

Road user-related measures 2 2 0 0

Total contribution of allmeasures

147 114 95 142

Expected in 2020 as aresult of policy option

138 171 190 143

477Chapter | 33 Public Policy

government cannotdounilaterally.Vehicle safety standards inEurope are introduced by consensus in international bodies,such as theUnitedNationsEconomicCommission forEuropeor the European Union (Norway is not a member of theEuropean Union). The new vehicle safety features already onthe market will contribute to reducing fatalities, but the mosteffective measures controlled by the Norwegian governmentare highway improvements and police enforcement.

How about driver training? Is it likely that improvingdriver training can make a major contribution to improvingroad safety? How about graduated driver licensing, which iswidely regarded as a success in North America? Is it reallytrue that road user behavior can only be effectively influ-enced by means of repressive measures such as enforcementand sanctions? These issues are discussed next, based ona critical review of current knowledge. Other chapters in thisbook treat some of the measures discussed here in greaterdetail. The review presented here is based mainly on TheHandbook of Road Safety Measures (Elvik et al., 2009).

3.2. Basic Driver Training

Elvik et al. (2009) reviewed and synthesized the results of16 studies that evaluated the effects of basic driver training

on accidents. Basic driver training refers to the formaltraining of car drivers before they are licensed for the firsttime. Depending on age limits, most drivers who are trainedfor the first time in their lives are 15e18 years old.

Figure 33.3 shows a funnel plot of 45 estimates of theeffect of basic driver training on driver accident rates(accidents per million miles of driving). The abscissa showsestimates of effect; the ordinate shows the statistical weightof each estimate of effect. Statistical weight is based on thenumber of accidents: Estimates of effect based on a largenumber of accidents have more weight than estimates basedon a small number of accidents. For a more detailedexplanation, see Elvik et al. (2009). If estimates originatefrom the same theoretical population, their distributionshould have a shape resembling a funnel turned upsidedown, with estimates based on small samples (at the bottomof the diagram) displaying a larger spread than estimatesbased on larger samples.

The summary estimate of effect is 0.97, correspondingto a small accident rate reduction of 3%. As can be seenfrom the diagram, a considerable number of estimates ofeffect indicate an increase in the accident rate. A closerexamination of the studies shows that the effects attributedto driver training vary depending on study design. This is

1800

1600

1400

1200

1000

800

600

400

200

00.100 1.000

Summary estimate (0.97)

Statistical w

eig

ht (fixed

-effects m

od

el)

Estimate of effect (log scale; 1.0 = no effect;< 1.0 = accident reduction; > 1.0 = accident increase)

10.000

FIGURE 33.3 Funnel plot of estimates of

effects of basic driver training on accident

rates

478 PART | VI Interdisciplinary Issues

demonstrated in Figure 33.4, which shows mean percentagechanges in accident rates in studies employing differentstudy designs.

Study designs have been ordered from the strongest tothe weakest. A favorable effect on accident rate has onlybeen found in nonexperimental studies. Elvik et al. (2009)discuss various possible explanations of these findings. They

1511

-13

10

5

0

-5

-10

Percen

tag

e ch

an

ge in

accid

en

t rate

-15

-20

-25

-30

-35Experimental

studiesBefore-after,

matchedcomparison

Casmu

a

Stud

FIGURE 33.4 Effects of basic driver training

conclude that methodological explanations are unlikely tobe correct, given the fact that a number of experimentalevaluations have been made. They conclude that the mostlikely explanation is that drivers adapt their behavior to theirperceived skills. In other words, drivers who think they aregood drivers may adopt smaller safety margins than driverswho are less confident about their own skills.

-2

-7

-31

e-control,ltivariatenalysis

y design

Case-control,stratification

on confounders

Simplecase-control

studies

on accident rates according to study design

479Chapter | 33 Public Policy

In short, the challenge in driver training is to teachpeople that they do not know anythingdor that what theyknow when obtaining a driving license is only a very smallpart of what they need to know to drive safely. This is analmost impossible challenge. The basic skills needed tooperate a motor vehicle are easy to learndmost teenagerscan acquire these skills in just a few hoursdwhereas higherorder cognitive skills are perhaps never fully learned.

3.3. Graduated Driver Licensing

Graduated driver licensing (GDL) has been introduced asa means of making novice drivers understand that they arenot yet mature drivers by restricting driving that involvesenhanced risk, such as nighttime driving or carrying teen-aged passengers. A large number of studies have evaluatedthe effects of GDL programs. Most of these studies reportthat GDL programs are associated with a reduction in thenumber of accidents (Elvik et al., 2009). However, there isevidence of publication bias, as tested by the trim-and-filltechnique (Duval, 2005). Publication bias denotesa tendency not to publish research reports, for example,because the findings are not statistically significant or areregarded as anomalous, difficult to interpret or explain, oreven unwanted. The trim-and-fill technique is a nonpara-metric statistical technique for detecting and adjusting forpublication bias based on an analysis of funnel plots. Thetechnique is based on the assumption that in the absence ofpublication bias, the data points in a funnel plot should besymmetrically distributed around the summary estimate. Ifthere is asymmetry, this is taken to indicate publicationbias, and symmetry is restored by adding data points thatare presumably missing as a result of publication bias(Høye & Elvik, 2010).

The crude summary estimate of effect for all accidentsis a reduction of 18%; adjusting for publication bias lowersthis to 11%. For injury accidents, the bias appears to beeven greater. The crude estimate is a 14% accident reduc-tion; adjusted for publication bias, the accident reduction is6%. Moreover, a tendency is seen for studies that do notcontrol very well for potentially confounding factors toattribute larger effects to GDL than do studies that controlbetter for potential confounding factors. Despite thesereservations, the literature does indicate that GDLprograms are associated with modest improvements innovice driver safety. However, the effects are far too smallto eliminate the difference in accident rate between novicedrivers and experienced drivers.

3.4. Speed Enforcement: An AccidentModification Function

The importance of speed enforcement should not be indoubt, given the fact that speeding is widespread and that

the risk attributable to it is substantial. It is neverthelessclear that neither police officers nor speed cameras canbe deployed at all locations and at all times. To applyspeed enforcement optimally, two issues need to beresolved:

1. How is the effect of speed enforcement on accidentsrelated to the amount of enforcement?

2. How should enforcement be carried out in order tomaximize its effect in time and space?

With respect to the first of these issues, Elvik (2010a)developed an accident modification function for speedenforcement performed by uniformed police officers.Developing this function required considerable data editingand smoothing. Figure 33.5 shows the accident modifica-tion function.

A reduction of the amount of enforcement froma certain baseline level is associated with an increase inthe number of accidents. An increase in the amountof enforcement is associated with a reduction of thenumber of accidents, but the marginal effect declinesrapidly.

To maximize the effects of speed enforcement, thedeployment of officers should be randomdthat is, theplaces and times targeted for enforcement should beselected at randomdso that every driver should, in thelong term, face the same probability of encountering thepolice (Bjørnskau & Elvik, 1992). The rationale behindthis is that a random deployment of enforcement willprevent road users from detecting any systematic patternin enforcement and adapt their behavior to this. Moreover,enforcement targeted at particular locations tends to beself-defeating in the long term: Once the police havesuccessfully deterred most violators, there is a tendencyfor enforcement to be reduced. Violations may then returnto the baseline level.

Regarding speed cameras, their effects tend to bevery local (Ragnøy, 2002). To extend effects to a longersection of road, it may be necessary to electronicallylink several speed cameras and measure mean speedfor the entire length of road covered by the linkedcameras.

3.5. The Need for and Settingof Speed Limits

The need for enforcing speed limits would not exist if speedlimits did not exist. Why not leave the choice of speed todrivers? Is there a need for speed limits? This question isdiscussed by Elvik (2010c), who argues that although mostdrivers probably think they choose the right speed and seeno need to change it, the choices of speed likely to be madeby drivers if speed limits did not exist would not produceoptimal outcomes from a societal standpoint. Specifically,

1.400

1.200

1.000

0.800

Accid

en

t m

od

ificatio

n facto

r (1.0 =

n

o ch

an

ge)

0.600

0.400

0.200

0.0000 1 2

Relative change in enforcement (current level = 1.0;< 1.0 = reduction; 2 = double of current level)

3 4 5 6 7 8 9 10

FIGURE 33.5 Accident modification function for speed enforcement. Source: Data from Elvik (2010a).

480 PART | VI Interdisciplinary Issues

Elvik concludes that speed limits are needed for thefollowing reasons:

1. Drivers tend to ignore, or assign minor importance to,impacts of speed that they do not immediately notice orthat do not directly affect their personal utility.Specifically, environmental impacts of speed choice arelargely ignored by drivers.

2. Drivers do not correctly perceive the relationshipbetween speed and travel time. Gains in travel timeattributable to small increases in high speed areoverestimated, whereas corresponding gains attribut-able to small increases in low speed are under-estimated. These misconceptions may lead drivers tocommit more serious violations of low speed limitsthan of high speed limits because drivers erroneouslythink that they need to increase speed substantially tosave time if initial speed is low, whereas smallincreases in high speeds do not produce the gains intravel time drivers think they do. Data on speedviolations in Sweden provide evidence supportingthese implications.

3. Drivers underestimate the increased risk of accidentassociated with increased speed.

4. Drivers underestimate impact speed in situations inwhich it is clear that an accident is unavoidable, but itsseverity can be reduced by braking.

5. Driver preferences with regard to safe speed are veryheterogeneous, making the coordination of speedchoices difficult.

In short, driver speed choice is not objectively rationaldthat is, it is not based on a correct assessment of allimpacts of speeding, leading to a convergence of prefer-ences regarding optimal speed. This does not mean driverspeed choice cannot be reasonably modeled as subjec-tively rationaldthat is, as optimal given driver prefer-ences and perceptions of the impacts of speed choice. Adistinction between subjective and objective rationality isalmost never made in modern analyses relying on theassumption that road user behavior is rational. Thisdistinction, however, makes perfect sense with respect tospeed choice.

The implications of the divergence between subjectiveand objective rationality are profound. Someone whoregards his or her choices as rational from his or her pointof view will rarely see strong reasons for changing thechoices. Making a different choice would suggest that theoriginal choice was somehow stupid or wrong. Most peopledo not like to be told that they are stupid. To the extent thatdrivers are satisfied with their choices of speed, persuadingthem to make a different choice is likely to be difficult.Moreover, because preferences regarding speed varygreatly among drivers, any speed limit is likely to be

481Chapter | 33 Public Policy

unpopular and regarded as either too high or too low bya considerable proportion of drivers, at least if speed limitsare close to the median preferences of drivers (i.e., limitsare set so that 50% of drivers think they are too low and50% think they are too high).

3.6. The Prospects for Rewarding SafeBehaviordand Its Price Tag

Can safer road user behavior be stimulated by rewardingit? Until now, the difficulties of reliably observing roaduser behavior have precluded the introduction of systemsdesigned to reward safe behavior. Today, technologiesfor unobtrusive observation of road user behavior arerapidly developing, enabling the introduction of rewardingsystems that have so far not been possible. For example,a driving computer, containing a digital map, can recordthe following:

1. Route choice2. Speed3. Use of daytime running lights4. Following distance5. Use of indicators6. Impact speed in case of an accident

The cost of the equipment needed to record these data israpidly decreasing. The advantages of recording the datawould be huge. One could, for example, in principleeliminate the problem of incomplete accident reporting andinaccurate information regarding where accidents takeplace. If, in addition to a computer, small cameras wereinstalled in cars, it would become possible to monitor driveralertness and distractions.

In principle, a road pricing system can be designed toreward safe behavior by putting a price tag on, for example,speeding or tailgating (Elvik, 2010d). With such a systemin place, drivers would soon discover that safe behaviorbrings a reward in the form of lower charges for usinghighways. A trial in Sweden, offering rewards forcomplying with speed limits, found that drivers do respondto economic incentives (Lindberg, 2006). However, manydrivers would regard the system as an unacceptable inva-sion of privacy and might not perceive the lower chargesassociated with safe behavior as a reward because theywould still be paying to use highways. An option thatdeserves consideration is to charge more for speeding thanthe societal cost it generates in order to make a surplus.This surplus could then be paid back to law-abiding driversto make the reward for safe behavior more tangible.

Drivers can be provided with monetary incentives forsafe behavior if they consent to having their behaviormonitored continuously and in great detail by a drivingcomputer and, possibly, a camera capturing their face.Because many drivers probably regard the current level of

accident risk as perfectly acceptable, it is not very likelythat they would see any advantage of introducing an inva-sive technology designed to discourage them fromspeeding, driving when fatigued, or committing simpleerrors such as forgetting to signal when turning.

4. DISCUSSION AND SUMMARY

Highway safety has been greatly improved in many highlymotorized countries in the past 40e50 years. The rate ofprogress has not been the same in all countries, but there isno doubt that highway travel is considerably safer today thanit was when traffic fatalities peaked in the highly motorizedcountries in 1970e1972. What accounts for the improve-ment in highway safety? To what extent has knowledgegained in traffic psychology contributed to it? It is difficult togive very precise answers to these questions. The improve-ment in highway safety is no doubt the result of a largenumber of safety measures that have been introduced butprobably also the result of less tangible factors, such assubtle changes in culture or a higher demand for and valu-ation of safety as a result of greater wealth.

Traffic psychology may, in a sense, be regarded as thedismal science of traffic safety. The term “dismal science”is usually reserved for economics because economists oftenremind us that resources are scarce, that we cannot geteverything we want, that we are greedy and egocentric, thatcycles of boom and bust will repeat themselves, and so on.Traffic psychology reminds us that humans are the mostdifficult part of the highway system to change. Road userswill commit errors, misperceive risks, or deliberately takerisks, such as drinking and driving, speeding, and so on.One is left with the impression that little can be done tochange this. This impression is too pessimistic.

During the past 40e50 years, several important changesin road user behavior have contributed to improved safety.The wearing of seat belts has increased in all highlymotorized countries. Children are more often restrained incars than in the past. Drinking and driving has probablybeen reduced in many countries, although data confirmingthis are less complete than the data on seat belt wearing andthe use of child restraints. In many motorized countries,more motorcycle riders wear helmets today than 40e50years ago. In the United States, however, laws mandatingthe use of helmets by motorcyclists have remainedcontroversial and have been repealed in many states.

Despite these improvements, unsafe road user behaviorcontinues to be a major road safety problem. What are theprospects of significantly reducing the contribution thatunsafe road user behavior makes to traffic fatalities? Itdepends on which measures are taken to influence road userbehavior. Persuasion alone is not likely to be very effective.Most road users think that their behavior is entirely appro-priate and see no reason to change it. Telling them to change is

482 PART | VI Interdisciplinary Issues

unlikely to impress them. Repression may be more effective.More police enforcement will contain speeding and othertypes of unsafe behavior, but it is impossible for the police tobe everywhere at all times. The risk of apprehension willremain low. From a theoretical standpoint, rewarding safebehavior is the most attractive option for promoting it.However, to reward safe behavior, it is necessary to observebehavior in some detail, and the technology permitting suchobservation will probably be regarded as highly intrusive bymany drivers. Drivers may reject this technology, although itcould make travel much safer than it is today.

Perhaps the key contribution that traffic psychologycould make to safety policy is therefore, in the manner ofa dismal science, to warn against all sorts of wishfulthinking that may influence this policy. It is wishfulthinking to believe that road users will suddenly realize thattheir behavior is sometimes unsafe and make amends. It iswishful thinking to believe that massive police enforcementcan solve the problem. It is wishful thinking to believe thatnew technologies, such as intelligent speed adaptation,intelligent cruise control, lane departure warning, or fatiguemonitoring, will not elicit behavioral adaptation that maypartly or fully offset the safety effects of these technologies.It is wishful thinking to believe that driver training can everreduce novice driver accident rates to the same level as theaccident rate of highly experienced drivers. It is wishfulthinking to believe that drivers will welcome technologiesthat continuously and in great detail monitor their behavior,even if by doing so new opportunities are created forrewarding safe behavior and thus reduce accident rates.

Although it is the role of traffic psychology to remindpolicy makers of the limits of their influence on the humanelement of the traffic system, psychologists should alsopoint out that effective ways of influencing human behaviorexist. Specifically, key contributions of traffic psychologyto road safety policy include the following:

1. Encouraging and contributing to systematic surveys ofroad user behavior, particularly behavior that isimportant for safety

2. Analyzing the relationship between specific types ofbehavior and highway safety

3. Modeling road user behavior, particularly by identi-fying factors that contribute to unsafe behavior

4. Analyzing human capabilities and performance to helpdevelop design guidelines for highways, traffic controldevices, and motor vehicles

5. Contributing to the estimation of expected effects ofroad safety measures, particularly by trying to predict ifnew safety measures will elicit behavioral adaptationfrom road users

6. Critically assessing the quality of road safety evaluationresearch and advocating the use of randomizedcontrolled trials whenever possible

7. Contributing to the development of targets forimproving highway safety that are maximally moti-vating for all stakeholders involved

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