Safety Science, Vol. 22, No. l-3, pp. 103-I 17, 1996 Copyright 0 1996 Elsevier Science Ltd

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ACCIDENT RISK AND DRIVER BEHAVIOUR

Heikki Summa/a

Traffic Research Unit, Department of Psychology, P.O. Box 7 7, 00014 University of Helsinki, Finland

Abstract-The concepts of risk compensation and risk homeostasis are often used to describe or

to explain drivers tendencies to react to traffic system changes whether in roads, vehicles, weather conditions or in their own skills. However, it is important to distinguish between the

general phenomenon and mechanisms underlying it This paper first points out that to understand the basic mechanisms it is necessary to split accidents and exposure into smaller entities to arrive at basic units of exposure which also represent fundamental driver tasks. Risk-related behaviour should be considered at several hierarchical levels with different

mechanisms to produce risk compensation. At a high level, tip decisions modify populations at risk in different circumstances, sometimes attenuating, sometimes amplifying population risk differences. At a low level of vehicle control and guidance in real dynamic traffic situations, simpler control mechanisms which result in behavioural adaptation can be identified. All these

effects influence the end result of accident risk as separate mechanisms. Copyright 0 1996 Elsevier Science Ltd

1. Introduction

In driver behaviour theory, the concept of risk has been a focus of discussion since the early 197Os, and risk measures have been proposed as a major control variable in driver behaviour. It became obvious at that time that an engineering or skill model of driver behaviour and safety was not a sufficient basis for safety work. Earlier attempts focused on increasing the level of driver performance and decreasing environmental demands, ignoring the fact that drivers have motivational tendencies other than safety. The pace chosen by a driver is crucial to safety rather than his/her maximum skill level (NTGtZnen and Summala, 1974). Consequently, in introducing safety counter-measures, it should always be recognised that the driver is inclined to react to changes in the traffic system, whether they be in the vehicle, in the road environment, or in his/her own skills or states, and that this reaction occurs in accordance with his/her motives. This principle of behavioural adaptation, also called risk compensation, is a focal topic in driver behaviour research.

Parts of this paper were presented at the 2nd European Congress of Psychology, Budapest, Hungary, July 1991 and at the 17th National Meeting of Road Safety Research, Siikaranta, Finland, April 1992.

103

104 H. Summala

Two major attempts to explain behavioural adaptation to changes in traffic systems were proposed in the 1970s one by NZitZnen and Summala (1974, 1976) and the other by Wilde (1974, 1975, 1976) which utilised the concept of risk. (For the respective economic utility models, see also ONeill, 1977; Peltzman, 1975.) The former proposed a threshold model on the assumption that in the dynamic driving situation, drivers actually control safety margins rather than some specific risk measure, and only when the risk or fear threshold is exceeded or expected to be exceeded, does it influence behaviour. They postulated a subjective risk or fear monitor (Summala and NUt%nen, 1988) which alarms and influences driver decisions when safety-margin thresholds are violated. One aspect of this approach is that with repeated confrontations, drivers adapt to situations which at first elicited a risk response and drive most of the time with overlearnt habitual patterns based on safety margins, with no concern for risk: hence the label zero-risk theory (NZZtZnen and Summala, 1976; Summala, 1985, 1988).

One major element in the NZXrinen and Summala model was the motivation module, used to explain the tendency to approach the risk threshold, which includes both motives brought from outside traffic, trip-centred motives such as hurry, and those inherent in the behaviour of human beings when in movement, such as maintaining present speed and conservation of effort. Generally speaking, drivers look for opportunities to satisfy their motives in traffic, which basically means looking for sufficient gaps and for means to maintain their desired speed.

Wilde (1974, 1975, 1976) proposed the risk concept instead as a principal control measure in driver behaviour and, in his later formulation of risk homeostasis theory (Wilde, 1982), postulates a concept of target risk, as determined by the utilities and disutilities of driving in a context of utility theory, and a major homeostatic or thermostatic feedback loop via the accident rate.

These two theories stress feedback mechanisms at opposite ends of the spectrum in explaining behavioural adaptation: one is of low-level control in dynamic driving situations, the other at high-level knowledge of the accident rate. Wilde (1994) recently extends the ongoing adjustment actions to a more basic level of driver behaviour adjustment while listing decisions having short-term effects upon safety such as changing ones pathway, speed, following distance or trajectory (pp. 40-41).

Several writers have critic&d motivational models for using risk as a central control variable (e.g. McKenna, 1988; Rotbengatter, 1988; Summala, 1988), as well as for lacking well-formulated mechanisms for producing testable hypotheses (e.g. Michon, 1985; Hoyes and Glendon, 1993; Ranney, 1994).

This paper first notes the importance of splitting accidents and exposure into elementary units, to determine the connections between accidents and driver behaviour levels. Next it looks at the driving task at several hierarchical levels, outlining mechanisms which result in what is called risk compensation. Finally, this paper concludes that several mechanisms at several levels contribute to these phenomena, each of which should be explained separately in terms of behaviour.

2. Need for disaggregation

In road safety research and practice, it is customary to consider accidents in relation to exposure, for example, when accident rates are computed on the basis of number of accidents

per head of population, number of registered vehicles or vehicle kilometres. Such measures are typically also referred to as accident risk, and can be conceived as relative-frequency probability measures. However, these measures mean different things and are useful for different purposes. For example, road fatalities per head of population describes the total cost of road traffic to society, whereas fatalities per vehicle kilometres very generally describes the safety of a road traffic system (Haight, 1973; Trinca et al., 1988).

However, these aggregate measures combine considerable variance from many sources. For any meaningful analysis of the basic mechanisms of driver behaviour, as well as for the analysis of risk factors from accident data, it is necessary to disaggregate accident data and corresponding exposures.

In his 1942 book Why we Have Automobile Accidents, DeSilva noted that the degree of hazard to which a driver is subjected, or, expressed differently, the extent of his exposure, is determined by how much, and where, and when he drives (DeSilva, 1942, p. 11). Thus, it is necessary to split exposure by type of road, visibility, weather conditions, time of day, day of week and so on. Finally, instead of counting crossing accidents in a given area, we should consider crossings of a certain type with certain sight distances and selected passes of vehicles through these crossings (left turns differ from right turns, for example). A left turn in a well-defined street or highway crossing is an elementary unit of exposure, as well as a pass of an oncoming vehicle of a certain type on a highway of a certain width. If weather and road conditions are added, as well as time of day and light, then we approach elementary units of exposure which may also be considered as equivalent to basic driver tasks.

These are general features of the traffic system shared by all road users present at certain places at certain times. However, these general features covary with driver (and trip) variables. Therefore, ultimately, we should consider sub-groups of drivers in particular situations and integrate all the elementary exposure units to achieve their total exposure.

Chapman (1973) points out that accident opportunities are occasions when cars cross each others path, when they are following one another or when a vehicle is travelling alone. Exposure can be defined as the number of opportunities of a certain type in a certain part of the road network and/or road user population. Accident risks can still be computed as relative frequencies if a sufficient number of similar trials can be obtained. (See for example studies which relate accidents or conflicts at crossings to their respective traffic flows with rather good results; see e.g. Older and Spicer, 1976; Spicer et al., 1980; Hakkert and Mahalel, 1978.) Risk can also be interpreted as the propensity of a system to produce an accident, in the sense of the propensity interpretation of probability (Peirce, 193 l-32; Popper, 1957; Hacking, 1965; Hauer, 1982; Summala, 1985). Thus, Hacking (1965) equates the propensity of the traffic system to produce accidents with the propensity to get a head in a coin tossing: the frequency in the long run of heads from a coin tossing device seems to be a property of the coin and device; the frequency in the long run of accidents on a stretch of highway seems to be the property of, in part, the road and those who drive on it.

Why then is it so important to split, or disaggregate exposure and accidents, and why is the propensity interpretation so important? It has at least three important implications.

2.1. Populations at risk change

The first reason which makes disaggregation of accidents and exposure necessary is that changes in general factors such as time of day and week, road, weather, visibility, and other factors covary with inter-individual ones: the driver population changes from time to time, and

106 H. Summala

50

40

30

20

10

0 Ion

male d

L

hu ri It Sun

Day of week and hour

Fig. 1. Disaggregation of accident loss in the search for black spots. Weekly distributions of fatal accidents for three age groups for the years 1984-92 in Finland. Although the absolute figures shown indicate different exposure, young drivers weekend night peaks, for example, show black spots which demands attention from society. Rather than the separate hourly and weekday distributions normally shown in official accident statistics, routine presentation of somewhat more disaggregated statistics such as this figure is recommended (data from the fatal collision data base studied by the Finnish accident investigation teams, provided by the Traffic insurance Center).

changes in the road and traffic conditions modify the population at risk. Thus, any valid comparisons of even major conditions (daylight, dark, dry, wet, slippery road) should take the population at risk into account in trying to achieve unbiased effect estimates (see also e.g. Roine and Kylmala, 1994).

Thus, for example, Evans (1987, 1992) estimated that the oldest age groups in the U.S. population have a five-fold greater risk of a road fatality per kilometre driven compared with the safest middle-aged ones, and a two- to three-fold greater rate of involvement in severe crashes when corrected for the more serious consequences due to same trauma among older people. However, these estimates ignore the fact that elderly drivers tend to avoid impaired conditions (e.g. darkness) in which younger and middle-aged drivers typically have a substantial part of their serious crashes (see Fig. 1 and e.g. Brainn, 1980; Hakamies-Blomq- vist, 1994; Summala and Mikkola, 1994). Therefore, elderly drivers have a substantially higher involvement rate although the average conditions in which they drive are more favourable. To estimate how elderly drivers succeed in controlling their risks in different circumstances, not only of total loss, accidents of different age groups should be related to corresponding exposure units, time of day and week, weather, and other general conditions.

That prevailing conditions modify the population at risk is the other side of the selective recruitment of drivers in choosing safety equipment (Evans, 1985, 1991). Drivers who use seat belts or studded tyres differ from those who do not use them, and the smaller the

Accidenr risk ad driver behaviour 107

percentage of the latter the more eccentric they probably are in many other respects. This effect, arising from drivers own decisions, legislation and other interventions, makes it difficult to arrive at unbiased estimates of the safety effects of these measures (see e.g. Janssen, 1994, for seat belts and Rumar et al., 1976; Summala and Merisalo, 1980; and IGikinen et al., 1994, for studded tyres).

In conclusion, estimates of general factors are invalid if the size of relevant sub-populations at risk are not taken into account, and conclusions on behavioural mechanisms behind aggregated results are problematic.

2.2. Search for black spots

The second reason for splitting accidents is very practical. In reducing accidents, we do not always need risk measures (accident rates), but it is practical to search for black spots in the accident mass - accumulations of accidents which indicate where effort to save lives may be concentrated. The search for black spots continues to be a major safety activity among road and traffic engineers. Although not always based on proper understanding of causes, and at risk of directing resources on a random basis (e.g. Hauer, 19861, cumulative efforts by road and traffic engineers, together with improved design standards, have gradually made this approach less and less useful.

Along with this geographical black spot method, however, it is essential to continue searching for black spots in the accident mass by splitting it into smaller units by type, road and traffic conditions, time of week and, where possible, on the characteristics of road users involved. Well-defined black spots or peaks in the accident mass, which may be widely distributed geographically, are of practical and theoretical significance to accident prevention even without corresponding exposure measures. An example of a major black spot which is widely distributed geographically is young male drivers crashes at night on weekends. Fatality data provided by Finnish road accident investigation teams (see Hantula, 19891, in Fig. 1, shows marked fatality peaks among young male drivers on Friday and Saturday nights, in sharp contrast with middle-aged drivers whose fatalities tend to follow peak traffic hours and elderly drivers who increasingly collide in non-peak daytime hours (see also e.g. Brorsson et al., 1993). These young male drivers peaks explain about 25% of all fatalities in the 18-20 year-old age range, during the three first years of driving in Finland. About 50% of young drivers in weekend night fatalities are intoxicated.

These black spots necessarily reflect exposure: both of populations at risk and their behaviour. For example, motivational (life-style) factors on weekend nights especially tend to get young male drivers into problems in contrast to older drivers who appear to avoid impaired conditions (e.g. night driving). These data confirm many earlier findings, some of which have resulted in major restrictions on youngsters driving at night (Karpf and Williams, 1983; Robertson, 1981; Williams, 1985; Williams et al., 1985). These youngsters peaks are undoubtedly the most prominent in the total road accident loss, and even without exposure data they are sufficient to demand actions from society.

(Accident data should be disaggregated more than they are. Thus, official accident statistics typically show time-of-day and day-of-week variation for the whole driver population, thereby masking much useful information. Taking into account both variation, and including driver age group into routine tables or figures, accident data black spots could more readily be seen. The OECD International Road Traffic and Accident Database is a useful step in this direction; see Briihning and Bems, 1993.)

108 H. Summala

2.3. The link between accident theory and driver behaviour

A third reason for splitting accident mass and exposure is that it should reveal a direct link between accident theory and driver behaviour, since the elementary exposure units essentially represent basic functional driver tasks. Thus, meeting another vehicle on a two-lane road, merging into a priority road, responding to braking by a lead car, etc., are basic model building tasks whether it be in simulations of traffic flow or of driver behaviour.

It is to be noted, however, that a similar basic task can be done differently. In order to derive estimates from accident frequency statistics, Wass (1982) split the accident load of given road-user groups into exposure, which is a measure of the number of situations which may result in an accident that the group in question encounters in traffic, and liability, which is a measure of road user/vehicle-centred factors which may contribute to the occurrence of an accident. The degree of hazard to which a driver is subjected is thus not only determined by how much, and where, and when he or she drives (DeSilva, 1942), but also by how he or she drives - which is related to the level of control over potential dangers.

The traffic code in motorised countries typically includes a very strict general rule which requests that drivers should be able to stop within visible distance of road in all situations which can be anticipated (e.g. the British Highway Code, Para. 57; the Finnish Road Traffic Code, Para. 23). However, most drivers fail to do this under certain situations although some drivers do so more often than others. Occasionally, the drivers task appears to be out of control due to speed being too high for the conditions, short safety margins or attention problems. Summala et al. (1996) call the corresponding events black events, as an extension of the black spot concept, including relevant behaviour by road users.

3. Driver task analysis

3.1. Task cube

Because total accident loss derives from different sources, a number of mechanisms may operate to modify both populations at risk and their behaviour in the control of (potential) dangers. To examine these mechanisms, the structure of driver sub-tasks related to risk is outlined. Figure 2 shows a task cube outlining basic dimensions of the drivers task. It considers the drivers task as a functional hierarchy from the vehicle control level up to trip decision and vehicle choice level (on the right); as a functional taxonomy of behaviour principally at the guidance level equating to the basic exposure units when on the road (below); and at a somewhat simplified psychological processing level (left).

This structure follows earlier hierarchical classifications of driving behaviour and informa- tion processing proposed in the early 1970s (e.g. Alexander and Lunenfeld, 1972; Janssen, 1979; Michon, 1971, 1979; Mikkonen and Keskinen, 1980). However, for a more complete understanding of driver behaviour and total accident loss, the higher level of trip decisions, including short- and long-term transport mode selection, and choice of vehicle and use of safety devices is added. Detailed functional taxonomies have been devised for educational purposes (e.g. M&night and Adams, 19721, but only the major categories of continuous lane and headway control are given here as well as control of conflicting flows at crossings and specific manceuvres such as passing and lane changes.

Accident risk md driver behaviour 109

LEVEL OF PSYCHOLOGICAL PROCESSING

Decision making

Attention control

$ Perceptual-motor control (constant mapping, automated)

FUNCTIONAL HIERARCHY

Vehicle choice

Trip decisions

Navigation

Guidance

Vehicle control

FUNCTIONAL 0 z

TAXONOMY 5 (D u)

Fig. 2. Driver task outlined in three dimensions relevant to accident causation.

The third dimension focuses on the distinction between automated perceptual-motor control and the conscious decision-making and monitoring level, the attention control in between being an important intervening element. The higher the task in the functional hierarchy, the more often conscious decision-making and supervising is applied, depending on the level of skill and automatisation: drivers also learn to perform trip decisions and navigation habitually without conscious consideration. Attentional control is applied both bottom-up, e.g. when sudden changes in the environment or difficulties in maintaining safety margins trigger an attention shift, and top-down, when drivers deliberately monitor guidance and vehicle control level tasks. (For related general task descriptions, see e.g. Rasmussen, 1983; Hale and Glendon, 1987; Lehto and Papastavrou, 1993; for supervisory and attentional control see e.g. Wickens, 1980, 1984; Reason, 1984; Norman and Shallice, 1986; Neisser, 1994.)

Rather than one of many basic tasks, as often conceived in traffic research, speed control is here given an even more central role. Speed and time control directly determine mobility, the basic issue and one of the basic high-level goals in transportation. Increased speed provided by motor vehicles and high-standard roads enlarges the area we can reach within reasonable time. Trip decisions are largely based on time and convenience factors.

Second, speed works as a major long-term and temporary motivational aim of driving. Trip decisions set the approximate desired or target speed level, together with driving costs and speed limits (for a related discussion on the optimum speed level, see e.g. Carlsson, 1976; Salusj%vi, 1981; Johansson, 1990; Summala, 1989, 1992). Target speed level largely deter-

110 H. S-iu

mines lower-level goals such as overtaking, and maintaining speed is suggested as a strong momentary goal in the same vein as continuing any activity which is going well (Matthews, 1978; Norman, 1985; Summala, 1988; Summala and Pihlman, 1993). It can be seen as an intrinsic motivating factor just as fast driving is seen (Fuller, 1986; N%t%nen and Summala, 1976; Rothengatter, 1988).

On the other hand, speed control serves as a primary control tool through practically all the guidance level tasks; drivers learn for example to adjust speed to maintain lane position and following distance. Time control can also be seen as a central part in the low-level automated safety margin adjustment.

Two major categories are identified to highlight different risk compensation mechanisms at different hierarchical levels. (Janssen and Tenkink (1988) already noted the need to look these mechanisms at different levels.)

3.2. High-level decision-making

High-level (pre-trip) decisions modify populations at risk albeit in different ways. Some decisions operate in order to distribute total accident loss more evenly across the driver population and environmental conditions and therefore appear to result in a phenomenon which can be called risk compensation. Avoidance of adverse conditions by elderly drivers undoubtedly works in this way. This behaviour may also depend on different daily routines among retired people rather than on risky decision-making and avoidance of risky or difficult driving in the dark.

Other high-level decisions, however, amplify risk differences. While safety-conscious or simply more wealthy middle-aged consumers tend to buy larger cars, while younger drivers with inferior skills generally have to choose smaller (and older) cars, the two groups, consciously or not, select different consequences in the crash, even in crash avoidance. This is due to differences in size (mass> and passive safety devices as well as in active safety devices and car condition (e.g. Evans, 1991). In addition to purely economic determinants, so-called extra motives (NZitZnen and Summala, 1976) can also be found in car choice which amplify risk differences. For example, Hatakka et al. (1995) show a positive relationship between self-assessed risky tendencies and selection of rear-wheel drive cars which are typically less stable but provide more opportunities for sports driving. Among trip decisions, some young drivers choice of using cars very dangerously at night on weekends, for strong motivational reasons indeed, enormously amplifies risk differences between driver sub-groups (see Fig. 1).

3.3. Low-level on-line control

At the low level, in dynamic traffic situations drivers adjust their on-road behaviour from moment to moment. Critical questions include, what processes and what control measures are used, and how can behavioural adaptation be explained? Taking a simple case, driving speed varies greatly on sections of road with different width and curvature. What mechanism can explain this variation?

The roadway and the lane painted on it forms a tube within which drivers have to drive. There is some tolerance in this tube (DeFazio et al., 1992; Drury, 1971; Macdonald and Hoffmann, 1980; Summala, 19941, depending on lane and vehicle width while adjacent lanes are open to occasional visits. Donges (1978) first proposed a two-level control model for lane-keeping, taking into account open-loop tolerance, and Godthelp et al. (1984) subsequently

Accident risk und driver behviour 111

proposed a time-based control measure in lane-keeping, the time-to-line-crossing @TLC), which is the momentary time until crossing either of the lane boundaries if the present course is being kept. This time margin can provide a feasible control measure for drivers in the basic continuous control task of lane-keeping. For a similar margin, decreasing road width or increasing curvature calls for slowing down (or more effort), and a wider or straighter road allows higher speeds or more time.

Lane-keeping requires practice to become automated. Evidence indicates that with increas- ing driving experience, lane-keeping eventually no longer requires fovea1 information (Bhise and Rockwell, 1971; Mourant and Rockwell, 1972; Smiley et al., 1980; Summala et al., 1996). This suggests that drivers learn a different, more efficient mechanism (or a multiple mecha- nism) for lane-keeping which could be based on time rather than distance or angle measures (Godthelp et al., 1984; Riemersma, 1987; Summala et al., 1996).

The roadway thus appears to be a tube in the tracking sense, which has substantial tolerance. But what is the real tube in terms of time measures? While the time-to-lane-boundary (whether it be left or right boundary) may provide the minimum threshold, providing the maximum speed level for a driver given a certain road width and curvature (and for a given car with a certain steering linkage) and therefore the lower limit of the tube, what is the upper limit for it?

A tendency to go to the limit may be postulated as an inherent motivational pressure to increase speed (NHHtZnen and Summala, 1976; Senders et al., 1967; Summala, 1985) which appears quite obvious indeed in terms of mobility needs alone. Why drive at, say, 30 kmh on a normal highway even without other vehicles to push from behind? This tendency is then pushing time margins shorter. With increasing time, however, drivers may allocate part of the extra time for in-car tasks and thus make the time-margin tube narrower (Summala, 1994). On the other hand, drivers are supposed to learn typical time margins they prefer habitually.

The lane tube and the time margin concept in lane-keeping exemplify a control measure which can be used to explain driver behaviour and behavioural adaptation, without the need for risk concepts. However, it can be seen that driving is a self-paced task in which large amounts of variance in different control tasks should be expected. This is clearly shown in time-margin data in lane-keeping (Godthelp, 1986; Godthelp, 1988; Godthelp et al., 1984; Piersma, 1993), as well as in vehicle following (Janssen and Nilsson, 19921, and in stopping (Favallo et al., 1986; Koivisto and Summala, 1989; Summala and Koivisto, 1990; Nilsson and Aberg, 1992; Summala et al., 1994; van der Horst, 1990). Finally, the lane-keeping tube provides an analogy to Wildes (1982) thermostatic/homeostatic risk model in which drivers opt for the target risk within a certain homeostatic tube. The former model, however, has the advantage that it specifies the sub-task along with the mechanism with quantifiable variables, free from problems associated with the risk concept, and is therefore testable.

3.4. The link between decision and control levels

The mechanisms by which high-level decisions translate into low-level control and vice versa will now be considered. Low-level control is largely automated, although it can easily be consciously monitored. Sudden changes in the environment or violating safety margin thresholds may trigger attention shift and conscious supervision. Threatening incidents and near misses may also result in changes in present and future behaviour (NZZtZnen and Summala, 1976).

112 H. Summala

A specific link from the control level to trip decisions may also be mediated by mental workload and effort. In line with Hancock and Girds (1993) model, which predicts that mental workload grows as effective time for action decreases, time margins can be seen as reflecting workload (Summala, 1994). Whenever we cannot readily keep within our safety margin thresholds we feel our task is overloaded and uncertain. To maintain speed when the road standard (e.g. width) decreases we have to put more effort into the task or slow down and, if we continuously meet problems in keeping within time margins in certain conditions, we learn to avoid these conditions. The control-level task has therefore been translated into the trip decision level, mediated by subjective workload, effort, and even uncertainty. Accord- ingly, based on her survey showing that elderly drivers feel more load in the situations they appear to avoid such as night driving or where they have many accidents such as at priority crossings, Hakamies-Blomqvist (1993) proposed that subjective workload is a major factor governing trip decisions and driving among elderly drivers.

As to top-down effects, people certainly learn from knowledge given by driving instructors at the beginning of their driving career, and later from newspapers and other mass media. High-level knowledge does not necessarily translate into behaviour however, at least not for a longer time, where low-level mechanisms do not confirm this knowledge, i.e. if personal experience shows that it is not necessary to conform to driving educators, safety peoples or newspapers warnings. It appears that the components of driver skills do develop differently with driving experience, depending on the feedback. Those with immediate and frequent feedback such as the use of clutch and gear shift expectedly improve but others, such as use of mirrors or turning indicators for which the feedback (for not using them) is stochastic, tend to drop out (Duncan et al., 1991). Correspondingly, self-assessed levels of skilled and safety- oriented aspects of driving also develop differently (Lajunen and Summala, 1995).

4. Safety work as exposure control: the example of rear-end crash risk

Just as there are several levels of processes which accumulate total accident loss, safety work may be seen as having a similar hierarchical structure. For example, in combatting rear-end crashes - generally considered to be due to driving too close - enforcement of stipulated following distances is now widely applied on the assumption that regulation and enforcement of low-level control is essential for reducing exposure to such contingencies. However, a majority of close-followers in two-lane roads say they do so when in a hurry, when waiting for an opportunity to overtake, or when pushing a slower car ahead to drive faster, all of these reasoned explanations being related to overtaking needs and, basically, to the fact that target speeds by drivers vary (Rajalin et al., 1995). We should therefore consider how higher-level aims result in short safety margins. Higher-level regulation of behaviour and modifications of the transport system may be much more influential. This is also the case with intelligent vehicle and highway systems. We should not only develop intelligent support for keeping sufficient following distance but also consider more traditional solutions at higher levels.

Table 1 lists some examples of exposure control in road traffic related to the problem of headway control and rear-end accidents. Exposure can be regulated by controlling time on the road for which the relevant driver task is traffic mode selection (trip decisions, including travel need adjustment); related driver problems are mobility needs (and the major problem of marginal costs, i.e. the private passenger car is a very competitive form of transport if only

Arcidenr risk and driver behoviour 113

Table 1 Exposure control in road traffic: the problem of headway control/rear-end accidents as related to highway driving

Exposure Relevant driver Driver problems Driver support w task G; _I

1. Time on road and in platoons: vehicle choice, trip decision and navigation level Time on road

Time in platoons

Traffic mode selection Trip decisions Trip decisions Route selection Timing of trips

Mobility needs Pricing policy

2. Time with short distances: guidance level Steady-state following Distance control

Headway selection

Momentary tailgating Overtaking performance (waiting for an opportunity, starting overtaking) Merging performance

3. Disturbances in theflow Decelerating vehicles Detection of speed

change/brake lights Slower vehicles Detection of speed

difference Stopped vehicles Adequate response

Marginal cost problem Habitual daily schedules Navigation problems (alternative routes)

Habitual models Position-keeping aims

Conflicting safety aims

Perception thresholds

Attention problems Time sharing Sensory and cognitive adaptation: Tendency to maintain speed, delayed responses

Pricing policy Navigation aids

Education Enforcement Speed limiters Intelligent headway control Education Traffic flow control (for more homo- geneous flow) Speed limits/ limiters

Intelligent headway control Norms for in-car equipment Education

a 5 8 a

marginal costs are considered); and, in the last column, for driver support as generally understood, pricing policy is among the means that society has available. At the lowest level, time in platoons is determined by traffic flows and therefore can be influenced, e.g. by changing daily travel patterns. The next lower level, time with short distances, is additionally due to specific driver behaviour, both in the case of steady-state following and momentary tailgating.

At different levels of analysis, different types of theories need to be applied to predict the effects of any interventions applied within traffic systems, including drivers behavioural responses to changes. Although there will be substantial overlap, at the lower level of the hierarchy, in the vehicle control and guidance tasks, control-theoretical models are applicable, while at the upper level, in navigation and especially in trip decisions, decision-theoretical models are more likely to apply.

114 H. Summa10

5. Conclusion

To understand the basic mechanisms of accident process and driver behaviour both accident mass and related behaviour should always be disaggregated. Second, both high-level decision processes and low-level control processes should always be taken into account. At both levels, there are processes which tend to distribute accidents more evenly in time, place, and driver population so that the end result resembles risk compensation or homeostatic processes. However, the mechanisms underlining these outcomes vary considerably. (Furthermore, there are processes that rather amplify population risk differences.) Therefore, it is important to distinguish the general phenomenon of behavioural adaptation or risk compensation from the mechanisms which underpin it, as well as from theories which aim at explaining it. To avoid misunderstandings, for example, the term risk homeostasis should not be used as a label for this general phenomenon but be reserved for Wildes risk homeostasis theory with the target risk concept as a major control variable.

The multiple-level and multiple-process nature of accident causation outlined here implies that to explain changes in accident loss adequately the related behavioural changes should also be found. Second, while closed-loop control processes can be found at lower levels of driver performance, the total accident loss in a jurisdiction is an output of an open system and any conclusions of the mechanisms cannot be based on changes (or lack of changes) in it. Therefore, Smeeds (1949) conclusion still holds for the total accident loss (see also Evans, 1985, 1991; Summala, 1985): there is a body of opinion that holds that the provision of better roads, for example, or the increase in sight lines merely enables the motorist to drive faster, and the resuit is the same number of accidents as previously.. . . There will nearly aiways be a tendency of this sort, but I see no reason why this regressive tendency should always result in exactly the same number of accidents as would have occurred in the absence of active measures for accident reduction.

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