Drivers adapt – Be prepared for It!

Accident Analysis and Prevention 135 (2020) 105370

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Accident Analysis and Prevention journal homepage: www.elsevier.com/locate/aap

Drivers adapt – Be prepared for It!

T

Alison Smiley*, Christina Rudin-Brown Human Factors North Inc., Canada

A R T I C LE I N FO

A B S T R A C T

Keywords: Behavioral adaptation Driver behavior Road safety Countermeasures

Behavioral adaptation refers to the change in road user behavior in response to new conditions (Kulmala & Rämä, 2013). Behavioral adaptation can improve safety, but it can also reduce or even eliminate anticipated safety benefits of many well-intentioned road safety countermeasures. To expect driver behavior to remain the same after the implementation of a change in the road, vehicle, or driving environment, is naïve. Empirical studies that do not consider the full range of behavior affected by a countermeasure may similarly overlook the consequences of behavioral adaptation. This paper considers a number of examples of driver safety countermeasure implementation where unexpected results occurred and behavioral adaptation was the likely culprit. These examples are drawn from highway design, traffic control device design, vehicle countermeasures, enforcement countermeasures, driver education countermeasures and impaired driving policies. A previously presented inventory of characteristics to consider when estimating the likelihood for behavioral adaptation (Rudin-Brown et al., 2013) is expanded and presented within the context of the Qualitative Model of Behavioral Adaptation (Rudin-Brown & Noy, 2002; Rudin-Brown, 2010), in the hopes of addressing the question “When can we anticipate the safety effect of a treatment, and when not?”

1. Introduction When crashes occur, it is common to single out some factor (e.g., absence of edgelines in the case of a run-off-road crash) and then to remedy the assumed-to-be-causal factor in the belief that doing so will benefit safety by preventing similar crashes in future. But the anticipated safety improvements are not always realized. The reason may be behavioral adaptation – that is, the change in driver (and other road user) behavior in response to new conditions (Kulmala and Rämä, 2013). Behavioral adaptation can reduce or even eliminate the anticipated safety benefits of many well-intentioned countermeasures. The lead paper in this Special Issue (Hauer, 2019) argues that policy decisions likely to affect road user safety are, at times, made on the basis of opinion, rather than on findings of research. Opinions about safety impacts that ignore the possibility of behavioral adaptation are likely to overestimate those impacts. Similarly, empirical studies that do not consider the full range of behavior affected by a countermeasure may overlook the consequences of adaptive behavior, such as increases in speed or changes in lateral lane position. This paper considers a number of examples of driver safety countermeasure implementation where unexpected results occurred and behavioral adaptation was a likely culprit. While the concept of driver behavioral adaptation has been around

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since at least the 1990’s (OECD, 1990), a number of questions remain unanswered. For example, what do we know about the specific manifestation(s) of behavioral adaptation for a given countermeasure or type of countermeasure? To what extent are drivers likely to change their behavior in response to an intervention or to a change in the driving task? How and when is that behavior likely to affect crash risk? Who is most likely to exhibit behavioral adaptation? It is intrinsic to the intelligent nature of humans to modify behavior to suit new conditions. Creativity and adaptation are essential elements in definitions of intelligence. For example, the Encyclopedia Britannica (Sternberg, 2017) defines intelligence as a “mental quality that consists of the abilities to learn from experience, adapt to new situations, understand and handle abstract concepts, and use knowledge to manipulate one’s environment.” Therefore, given an intelligent operator, to expect driver behavior to remain the same after the implementation of a change in the road, vehicle, or driving environment, is naïve. This means that improvements in road safety cannot be predicted on the basis of proof-of-concept studies alone. The anti-lock braking experience (Smiley, 2000) illustrates this point. Early predictions were based primarily on performance of the specific driving sub-task being aided (e.g. braking on wet roads), and assumed that, in all other respects, performance would remain unchanged (e.g. there would be no increase in speed). However, drivers who experience antilock braking

Corresponding author. E-mail address: [email protected] (A. Smiley).

https://doi.org/10.1016/j.aap.2019.105370 Received 11 February 2019; Received in revised form 16 November 2019; Accepted 16 November 2019 0001-4575/ © 2019 Elsevier Ltd. All rights reserved.

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systems firsthand have been found to drive faster, and to follow a lead vehicle more closely, than drivers who do not, suggesting a reduction in benefit of this technology (Sagberg et al., 1997; Vaa et al., 2007). We must look at changes in other aspects of the driving task, at the type of driving being done and at the tendency to maintain a steady mental load (what is often referred to as driver “workload”) by taking on secondary tasks, as well as other driver motivations such as utility, convenience, and even driving “pleasure” or comfort, in order to more accurately estimate the true likely overall effect on safety. In particular, we should assume that there may be trade-offs of mobility for safety; that is, more driving in more difficult conditions and/or at higher speeds when using a safer road or vehicle, leading to less improvement in safety than was predicted. Further, we should expect drivers to attempt to increase secondary task involvement while driving in what they perceive to be reduced driving task demand conditions, for instance when ambient traffic is scarce or when one is driving on a straight, well-lit road. Prolific use of cell phones by drivers may be an example of this phenomenon (Teh et al., 2018). This paper considers how drivers change the way they drive in response to various well-intentioned road safety countermeasures, and the ubiquitous nature of behavioral adaptation. Though not exhaustive, its effects will be described in a variety of realms: highway design countermeasures (e.g. roadway paving, extending sight distances), traffic engineering countermeasures (e.g. traffic signal phasing and timing, center and edge-line markings, post-mounted delineators [PMDs], raised pavement markers, raised crosswalks), vehicle countermeasures (e.g. antilock brakes [ABS], electronic stability control [ESC], back-up cameras and reversing aids, adaptive cruise control [ACC], lane departure and assist systems), enforcement countermeasures (e.g. banning of hand-held cell phones, implementation of red-light and speed control cameras), driver education countermeasures (e.g. school-based driver education, skid control training) and impaired driving policies. The goals of this paper are

The level of ambient traffic surrounding a driver affects driver workload. On-road research using eye tracking technology (Mourant and Rockwell, 1970) shows that the distribution of driver eye fixations narrows dramatically when drivers closely follow a lead vehicle, a higher workload task as compared to driving without nearby traffic. Sign reading while driving is also a highly adaptive process that is dependent on driver workload (Bhise and Rockwell, 1973). Eye movement data show that, in the 8–10 seconds a guide sign is legible, drivers spend on average:

• 2.6 s per sign in low density traffic • 2.3 s per sign in moderate density traffic • 0.9 s per sign in high density traffic (Bhise and Rockwell, 1973) Driving simulator research has also shown a relationship between road characteristics, workload and driver-selected speed. Compared to when driving on simulated sections of road of low complexity (i.e., with no, or empty, parking bays), when the same drivers experienced road segments of higher complexity (occupied parking bays), they lowered their speed and shifted their lateral position towards roadway center to compensate for the higher mental workload they reported experiencing (Edquist et al., 2012). Some authors who have studied behavioral adaptation at length (e.g., Wilde, 1982; Summala, 1988; Fuller, 2011) believe that perceived risk, or some other construct, plays a more important role in determining behavioral adaptation than workload. An on-road study measured driver speed as influenced by various geometric features and traffic control devices (Lerner et al., 1988). Participants’ vehicle speeds were recorded, as were their estimates of the risk of a crash (for safety reasons, these estimates were made when they were passengers). In general, the more risky a driver perceived the road to be, the lower the chosen speed (see Fig. 1). Drivers adapted to the roadway by lowering their speeds on sharp curves, and in areas with limited sight distance or with crest vertical curves but, interestingly, not at locations such as intersections where objective risk is high, suggesting that driver perception of risk is not necessarily accurate.

1) to lay out the breadth of situations in which behavioral adaptation can be expected to occur, 2) to lay out the (generally) unintended changes that occur in various aspects of driver performance, and 3) to identify countermeasure characteristics (such as transparency) that are likely to lead to adaptive behavior.

3. Behavioral adaptation to road safety countermeasures Before turning to examples of behavioral adaptation to various types of road safety countermeasures, it is important to acknowledge the potential benefits of many countermeasures in terms of reductions of fatal and serious injury road crashes (American Association of State Highway and Transportation Officials (AASHTO), 2010; Elvik and Vaa, 2004). Furthermore, much research into the effects of these countermeasures shows that significant improvements in road safety are possible when they are appropriately introduced. For example, automated traffic enforcement countermeasures are rendered more effective when accompanied by clear and effective messaging, including road signage and associated driver educational campaigns (e.g., Decina et al., 2007). The same is true during the introduction of unusual roadway designs and traffic calming measures like roundabouts and speed bumps (WHO (World Health Organization), 2008). It should also be noted that there is much unknown about adaptation. The magnitude of safety benefit of a given countermeasure, and how much of any benefit is due to intended changes in driver behavior (compared to other, unintended changes in driver or other road user behavior), remains undecided in many cases. Only by identifying and understanding unintended consequences of countermeasures that are designed and intended to improve safety can the full complement of safety benefits and dis-benefits of a road safety countermeasure be estimated. An important factor in that estimation is driver behavioral adaptation.

A previously presented inventory of characteristics to consider when estimating the likelihood for behavioral adaptation (Rudin-Brown et al., 2013) is expanded and presented within the context of the Qualitative Model of Behavioral Adaptation (Rudin-Brown and Noy, 2002; RudinBrown, 2010). This is done in the hopes that road safety decision-makers and researchers will anticipate a countermeasure’s potential to trigger behavioral adaptation in drivers, with the ultimate goal being improvements in countermeasure effectiveness and therefore overall road safety. 2. Adaptive driver behavior and the role of driver workload and perceived risk Before considering driver response to various highway design, traffic engineering, vehicle and driver countermeasures that change the nature of the driving task, let us consider the adaptations that occur on a moment to moment basis in response to changes in driving task workload and perceived risk. Workload includes elements of mental demand, time pressure, and effort, and can be measured using various metrics. These include primary task measures (e.g. steering and lane position variability), secondary task measures (e.g. performance counting backwards by 3), physiological measures (e.g. heart rate variability) or subjective measures (e.g. ratings on the NASA task load index, or “TLX”). 2

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Fig. 1. Driver subjective rating of risk and actual vehicle speed by road feature / geometry. Adapted from Lerner, Williams & Sedney (1988) and from Transportation Association of Canada Canadian Guide to Geometric Design (2017).

4. Highway design countermeasures and behavioral adaptation

result was a 37% drop in the mean and 80th percentile approach (the speed at or below which 80% of drivers were travelling) speeds. Subsequent to the field test up until the time of publication, no serious injury crashes had occurred. Similar findings come from a more extensive study of 26 single lane roundabouts in the United States. Statistical models were built using speed, geometric and operational variables to predict 85th percentile speeds and crashes. Research on the relationship between speeds and sight distance found that intersection sight distance predicted 85% of the variance in 85th percentile roundabout entry speeds (speeds at or below which 85% of drivers travelled). Models were developed to predict rear-end crashes, loss of control crashes and total crash risk. A single variable, circulating sight distance (the distance along the centerline of the circulating lane at which a driver that entered from the subject approach can detect another vehicle on the circulating roadway), was found to explain 33 to 48% of the variance in crash risk. The conclusions were that more sight distance allows drivers to approach a conflict point at higher speeds with a negative impact on safety, and that providing more than the minimum sight distance leads to increased crash risk (Angelastro, 2010). As recommended in FHWA Roundabouts: An Informational Guide, (FHWA, 2000, p163) traffic engineers should “provide no more than the minimum required intersection sight distance on each approach. Excessive intersection sight distance can lead to higher vehicle speeds that reduce the safety of the intersection for all road users (vehicles, bicycles, pedestrians).”

4.1. Roadway resurfacing Resurfacing a worn roadway is generally considered a safety improvement. However, quieter roadways lead to higher speeds especially in rainy conditions (Van der Zwan, 2011). What about driver safety? Hauer (1997) investigated crash rates following the implementation of two kinds of pavement resurfacing projects in New York State in the 1980s: ‘Fast Track’ (FT) projects involving only resurfacing, and ‘Reconditioning and Preservation’ (R & P) projects where roadside and roadway safety improvements, such as super-elevation, shoulder and roadside improvements, had been incorporated with resurfacing. The roads involved were rural, two-lane, undivided, free-access road sections. The expected number of non-intersection crashes was higher by an average of 4.15 collisions for the first 30 months for the FT projects. (The term “expected” is used here as in the theory of probability and corresponds roughly to “average in the long run” [Hauer, 1997]). In contrast, there was no change in crash rate for the R & P projects. A likely explanation is that resurfacing makes the road smoother, which leads to higher speeds. Higher speeds need to be counteracted therefore by other safety improvements to maintain at least the same level of safety as prior to resurfacing. 4.2. Improving sight distance

5. Traffic engineering countermeasures and behavioral adaptation

Although more sight distance is generally thought to improve visual search and therefore safety, it appears that more is not always better. A human factors assessment was carried out for a rural intersection that had a high rate of injury crashes (Charlton, 2003). The analysis suggested that most of the crashes were due to “anticipatory decisionmaking occasioned by visual characteristics” of one of the intersection approaches. Although it ran counter to highway design conventions, after much discussion, as a countermeasure, the view to the intersection was blocked with a hessian screen (woven fabric made of plant material) beginning 125 m prior to the intersection up to 25 m prior. The

5.1. Traffic signal phasing and timing Small differences in traffic signal phasing and timing can have major impacts on run-the-red-light offenses and crashes. Traffic signals can use a fixed-time control scheme or a vehicle-actuated (long distance detection) scheme. In the first case, the signal change from green to amber is on a fixed schedule; in the second case, the signal change is 3

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of roadway, and using modern statistical techniques (Empirical Bayes), a number of surprising conclusions were reached. Placing RPMs, which improve driver preview, on sharp curves (greater than 3.5 ° of curvature) on low volume roadways (less than 5000 Average Annual Daily Traffic [AADT]) was associated with a 43% increase in crashes as compared to similar roads without pavement markers. We do not have the human factors studies to explain why, but given Kallberg (1993) findings that speed increased at night when PMDs were installed, and older research (Allen et al., 1977) showing that, both in driving simulators and on real roads, higher contrast lane markings lead to higher speeds, a possible explanation is higher speed when the road is better delineated. On roads with little room for error, where design standards are lower (i.e. 80 km/h vs 100 km/h roads), even small increases in speed greatly increase the risk of a crash. The lead article (Hauer, 2019) in this issue on “Road Safety Research and Practice: Problems of Co-existence” discusses the effects of delineation on speed, reporting in Appendix B, for example, “that edgelining does affect speed, that speed is affected both during the day and night, and that the magnitude of the change depends on circumstances (straight road or curve, amount of traffic, width of lane etc.)”. In some circumstances speed is found to decrease with the application of edgelining and in other cases, to increase. Clearly, it is important to consider various roadway characteristics when assessing whether and how adaptation occurs. For example, the NCHRP study findings on RPMs are not all negative. The better the roadway, and the gentler the curves, the more likely there is to be an improvement in crash experience associated with the presence of RPMs – the best effect is found on freeways with traffic volumes of over 60,000 AADT where crashes on RPM-equipped roads are 67% of similar roads that are not so equipped. Therefore, if roads designed for a given speed are given treatments (e.g. high visibility delineation) that create a mismatch with critical design features (e.g. sharp curves, unforgiving roadside), then improving driver guidance or comfort will not necessarily lead to lower crash rates because driver expectancies regarding appropriate speed will be violated. Instead, we need to be selective in how and where we apply improved delineation or repaving so that it is aligned with drivers’ expectancies. Also, it is important to measure speed as well as crashes to have a better understanding of how adaptation is occurring. Guidance that is not visible, and so does not tempt drivers to increase speed, may be more effective in reducing crashes than guidance that is obvious. Distracted or fatigued drivers who cross longitudinal rumble strips, whether they lie along the shoulder edge or the centerline, are alerted by the sound that indicates they are about to leave the roadway. Crash studies show that shoulder edge rumble strips result in a 21% reduction in run-off-road crashes on rural highways (Fitzpatrick et al., 2000). Center line rumble strips have similar effects; they are associated with a 25% reduction in target crashes (Persaud et al., 2003). Perhaps it would be better to give drivers an auditory warning on sharp curves rather than improving their preview and tempting them to increase speed. A study of driver performance and safety impacts comparing rumble strips and improved path delineation on sharp curves has not been done to the authors’ knowledge, but would be most useful. It may very well show that rumble strips do not lead to increased speed at night and are therefore more effective in improving safety. Another countermeasure that may be effective without leading to negative changes in behavior is a vehicle-based warning system activated when the driver approaches a sharp curve at an unsafe speed (Davis et al., 2018). Adaptation is likely a continuous process and can be seen with the use of a perceptual countermeasure, namely progressively closer lateral lane markings. The theory was that, if elements in the peripheral field are placed at decreasing distances apart, then drivers should have the sensation of increased speed and slow down. Immediately after the lines were painted at roundabout entries, a 30 percent reduction in 85th percentile speed, and a 23 percent reduction in average speed, were found. However, reductions were only partially sustained after a one

dependent on traffic conditions. If there are vehicles within the dilemma zone (where the choice of stopping or proceeding on a caution light is difficult), then the amber phase is delayed and the green phase extended for a short period until the vehicle triggering the extended green is able to clear the intersection legally. With this scheme there is a large reduction in the number of run-the-red-light offenses (Zegeer Ch and en Deen, 1978, in van der Horst, 2013). The reason is that most run-the-red-light offenses occur when drivers are caught in the dilemma zone, but with long distance detection, fewer drivers are caught in the dilemma zone and as a result there is less red-light running. Thus, safety is improved. That being the case, what happens to drivers who are caught in the dilemma zone despite the green light being extended? Familiar drivers have come to expect a high probability of a green light, and so make the decision to go through the intersection (i.e. accelerate or continue at the same speed) at an earlier stage of the approach process (by about 1 s) than is the case for drivers approaching a fixed schedule traffic signal. As a result, the level of compliance to the red light at vehicle-actuated (long distance detection) intersections for those caught in the dilemma zone is lower than is the case at fixed-time signals. It is very important to note, however, that overall safety is greater in the case of vehicle-actuated traffic signals because of the large reduction in drivers caught in the dilemma zone, resulting in fewer run-the-red light offenses. 5.2. Vehicle path delineation Based on the assumption that drivers are running off the road on curves because of a lack of “adequate guidance” around the curve path, traffic engineers have applied, among other devices, edgelines, postmounted delineators (PMDs) and raised pavement markers (RPMs) (sometimes referred to as “cat’s eyes”) to provide better information about the path ahead. If one assumes that decreased driver workload and increased driver comfort improves performance, one would expect that more visual guidance should reduce run-off-road crashes, and that this improvement would be especially noticeable at night. What do the crash studies tell us? Kallberg (1993) in Finland looked at the impact of PMDs on roads of various designs: those with posted speed limits of 100 km/h with gentle curves and wide clear zones, and those with posted limits of 80 km/h with sharp curves and no room for error once drivers leave the road (Kallberg, 1993). Twenty pairs of similar road sections were selected and one of each pair was randomly assigned to have PMDs installed. This selection method avoided the regression to the mean effect, whereby only recent high-crash sites are the ones selected for treatment. A peak in crashes is generally followed by a decline, countermeasure or no countermeasure. This decline, due to statistical variability, can be incorrectly interpreted as a positive effect of the treatment. Using this strong statistical approach involving random site selection, Kallberg (1993) found that improving guidance by using PMDs was associated with minimal effects on nighttime speed or on crashes on 100 km/h roads, but it was associated with higher speeds at night and a highly significant 40–60% increase in nighttime injury crashes on 80 km/h roads. Driver preview was improved and drivers sped up, with disastrous results on those (80 km/h) roads not able to accommodate higher speeds. Perhaps because it is difficult to accept counterintuitive results, this study did not result in a ban on PMDs on roads with sharp curves. Similarly, the Human Factors Guidelines to Roadway Systems (Campbell et al., 2012), in its section on PMDs, makes no reference to the cautionary findings described in the Kallberg (1993) paper above or a more recent 2004 study with similar findings (Bahar et al., 2004) discussed below. Kallberg (1993) findings were very strongly confirmed in 2004 in an extensive National Co-operative Highway Research Program (NCHRP) study that looked at the effects on crashes of another device intended to improve visual guidance for drivers: RPMs on undivided highways and freeways (Bahar et al., 2004). Based on data from several hundred miles 4

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to a zero net effect on fatal crash involvements. Fatal run-off-road crashes of passenger cars increased but this was offset by decreases in fatal collisions with pedestrians and in collisions with other vehicles on wet roads. (ABS was quite effective in decreasing non-fatal crashes however). A number of studies found this same pattern of increased run-off-road crashes but decreased crashes with other vehicles. One of the potential explanations offered was that drivers unfamiliar with ABS did not know how to use it properly. Despite expectations that crash outcomes would be improved after the population had more experience with ABS, the pattern of increased run-off-road crashes especially on wet roads remained. The authors state they are “still unable to provide a convincing explanation or empirical evidence for this increase” (Kahane and Dang, 2009). Earlier behavioral studies pointing to higher speeds and shorter headways in ABS vehicles may provide an explanation. A small fleet study of taxicabs in Germany found no differences in the crash rates of taxicabs with and without ABS (Aschenbrenner et al., 1992). However, in-vehicle observers posing as passengers rated those drivers of ABS vehicles as driving more aggressively and performing more dangerous maneuvers than drivers of non-ABS vehicles. The drivers themselves reported that ABS made them more likely to take risks. Another taxi fleet study (Sagberg et al., 1997) found that drivers using antilock brakes kept significantly shorter headway distances to lead vehicles than drivers without antilock brakes. Some have proposed that behavioral adaptation to in-vehicle safety devices is more likely to occur if a driver is able to experience direct feedback from the device (Rudin-Brown, 2010; Evans, 1991; Lund and O’Neill, 1986 If this is the case, then behavioral adaptation to antilock brakes would be more likely to occur in drivers who had experienced their activation vs. those who did not. Grant and Smiley (1993) tested this hypothesis using an instrumented vehicle on a test track. Drivers who were shown the increased control available with antilock brakes drove faster and applied higher brake pedal forces than those drivers who did not experience the antilock brakes. A telephone survey study (Lee-Gosselin et al., 2001) found that many people mistakenly believed their own vehicle to be equipped with antilock brakes when, in fact, it was not, and that antilock brakes permitted faster speeds. Of most concern, 75% of drivers incorrectly believed that antilock brakes allow better vehicle control and ability to stop faster on deformable (snow, slush, gravel) surfaces. In fact, stopping distances on deformable surfaces are increased significantly with antilock brakes (Battista, 2001; Forkenbrock et al., 1999). Many drivers in the 1990′s incorrectly believed that they were supposed to pump the brake pedal with antilock brakes, which in fact actually defeats their purpose (Williams and Wells, 1994). Collectively, research indicates that the development of behavioral adaptation to antilock brakes, at least among those drivers who hold inaccurate beliefs regarding the technology, is likely.

year period. (Denton, 1973). Other studies also show this trending effect (Maroney and Dewar, 1987). These findings suggest that the effect of such markings fade over time and are most effective for unfamiliar drivers. This is supported by a study showing greater effects on roads expected to have the greatest proportion of unfamiliar drivers (Katz, Duke, & Rakha, 2006). Not all behavioral adaptation is negative. For example, it has been shown that drivers turning right concentrate their visual search on vehicles coming from the left, and fail to detect a bicyclist or pedestrian crossing from the right. This is especially likely to occur if drivers do not stop before turning right on red and, as a result, give themselves less time to search both to the left and the right. At intersections with high pedestrian and bicycle volumes, this can result in crashes, as drivers do not detect pedestrians and bicycles coming from the right. At uncontrolled intersections the use of speed humps, elevated crossings, and warning signs were tested (Summala, 1981). Signs were ineffective in changing driver search behavior. However, speed humps and elevated crossings caused drivers to slow, and at the same time resulted in their making more searches to the right, lessening the potential for conflict between right-turn drivers and bicycles from the right. Another example of a road safety countermeasure which did not lead to negative changes in behaviour is delineation used to reduce driving speed without significantly reducing driving comfort on 80 km/ h roads. The delineation countermeasure was developed and studied using a process described by Van der Horst (2013), which explicitly considered adaptation at every stage. Passenger car speeds were reduced by narrowing the lane width of ‘smooth’ asphalt by means of a centre line rumble strip together with a rumble strip edge that provided heavy vehicles with enough lateral space for lateral control. Because of concerns that drivers might adapt by moving closer to the road center, the center line delineation was widened to increase conspicuity and the white lane stripe on the edge of the road was removed. Measures were first studied in a simulator to ensure the desired effect was achieved. Then speed reductions and lateral position measures were studied on the road. A concern was that drivers might adapt in a negative manner by moving closer to the center, increasing the risk of head-on collisions. On road measures showed that this did not happen. Drivers controlled lane position by referring to the center line strip (the marker closest to their eye position) and maintained as much separation with oncoming vehicles as in the conventional marking design, even moving 0.1 m further away. Speed and crash measures were collected over a two-year period and showed positive effects. 6. Vehicle countermeasures and behavioral adaptation 6.1. Vehicle countermeasures that affect vehicle handling and stability 6.1.1. Antilock brakes Antilock brakes act by modulating the pressure in each wheel’s brake line so that, if a wheel lock-up is anticipated or occurs, the brake line pressure is released, allowing the wheel to continue to turn. Because the wheels continue to turn during braking, combined steering and braking continue to be possible. Without ABS, the wheels lock-up and cause the vehicle to lose traction and the driver to lose directional control (Grant and Smiley, 1993). Antilock brakes have been required equipment on European vehicles since 2007. While there are no specific North American or Australian safety standards, their presence on all new vehicles since 2011 is inferred through the respective safety standard requiring electronic stability control, or ESC, a traction control system (see next section). While early studies prompted some authors to estimate potential safety benefits of antilock brakes of up to a 10–15 % reduction in crashes (OECD, 1990; Rompe et al., 1987), the full expected benefit has not been realized. A National Highway Safety Traffic Administration report, based on large databases and 12 years of data, found that ABS had close

6.2. Electronic Stability Control (ESC) ESC is a vehicle system that continually monitors a driver’s steering and brake inputs, as well as the vehicle’s lateral acceleration and yaw rate. If the system determines that the vehicle is becoming unstable by beginning to spin or skid, ESC automatically applies braking at individual wheels and/or engine power to bring it back under the driver’s control. Because it intervenes before a loss of control occurs, ESC has the potential to prevent certain types of crashes more than others. In particular, ESC reliably reduces the number of vehicle crashes that involve loss of control and running off the road, including rollovers and crashes with fixed objects (Chouinard and Lécuyer, 2011; Erke, 2008; Scully and Newstead, 2008) and especially those occurring on snow- or slush-covered roads (Chouinard and Lécuyer, 2011; Erke, 2008). There is indirect evidence that ESC may result in behavioral adaptation. Surveys of Canadian (Rudin-Brown et al., 2009a, 2009b) and Swedish (Vadeby et al., 2011) drivers have investigated the possibility of behavioral adaptation to ESC. Both surveys independently found that 5

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begins to move out of its lane. Typically, LDW systems are programmed not to issue a warning when a lane exceedance is detected at the same time that the corresponding turn signal is activated. The sensitivity of the warning threshold criteria may be adjusted by the driver; for example, allowing more or less deviation from lane center before a warning is triggered. Rudin-Brown and Noy (2002) investigated whether an LDW system would induce behavioral adaptation in drivers required to perform a secondary number-entry task. It was hypothesized that behavioral adaptation would manifest by increasing the drivers’ reliance on the warnings to keep them oriented to the driving task. Licensed drivers aged 21–44 years participated in two separate studies: one in a simulated driving environment, the other on a test track. Results showed that the presence of reliable warnings in both settings improved lanekeeping performance compared to no warnings. However, drivers reported a high degree of trust in both accurate and inaccurate LDW systems, despite the intentional infidelity of the latter. (The “inaccurate” system was designed to produce false positive and false negative warnings on a pre-determined schedule). Performance on the number-entry task improved (became faster) over time in the driving simulator regardless of warning accuracy condition, while there was no change in number-entry speed in the drivers on the test-track. The collective results from both studies indicate that, because of the propensity of some people to trust unreliable or faulty devices, behavioral adaptation to LDW can result, and caution should be used in attempting to predict the aggregate safety benefits of these systems.

drivers reported driving faster, and more carelessly, when they believed they were driving a vehicle equipped with ESC compared to a vehicle that was not so equipped. The Canadian drivers also reported that, with ESC, they would be more likely to drive in adverse weather conditions, to drive faster in adverse weather conditions, and to drive more aggressively than if they were driving a vehicle without ESC. It is important to note that, regardless of its potential to induce behavioral adaptation, ESC’s proven effectiveness in reducing certain kinds of serious crashes outweighs any potential increases in unsafe driving. Evaluation studies to-date have reliably and conclusively proven its effectiveness, with crash savings estimates currently ranging between approximately 25–50 percent (Høye, 2011; Papelis et al., 2010). 6.3. Vehicle countermeasures that warn the driver of undesirable states 6.3.1. Backing aids and reversing cameras Backing aids are sensor- or video-based systems that assist drivers in performing low-speed backing maneuvers by providing some form (usually auditory) of warning to communicate the presence of, and distance to, obstacles located behind a vehicle (Green and Deering, 2006). Some systems (e.g., Bosch ‘Rear cross traffic alert’; Bosch, 2019) are also able to detect and warn drivers of approaching cross traffic (Cicchino, 2019). These systems have the ability to reduce low-speed collisions between vehicles and objects and pedestrians so long as they detect them reliably and are used appropriately by drivers. Reversing cameras mounted in the rear bumper area project to a small monitor mounted in the instrument cluster or within the rear view mirror. Interestingly, as of May 2018, reversing cameras are required equipment on new vehicles in the U.S., as they expand the driver’s rear field of view to enable the driver to detect the area behind the vehicle (IIHS (Insurance Institute of Highway Safety), 2014). Reversing cameras may allow drivers to detect unexpected and unseen obstacles while backing; however, caution must be used when estimating safety benefits as these systems require direct glances to an in-vehicle display that may be located outside a driver’s line of sight. Regardless of backing aid type, system warnings and information require attention and an appropriate response in a timely manner to be effective (Mazzae and Garrott, 2007). Backing aids are effective at reducing crashes under experimental conditions (Mazzae et al., 2008; Mazzae, 2010). However, their use may result in behavioral adaptation. Experimental evaluation of extended (i.e., 2-month) use of backing aids, where participants’ own vehicles were equipped with commercially available backing aids (Rudin-Brown et al., 2012), explored the possibility. Although parking accuracy improved when drivers used the systems, objective measures of their visual search behavior showed that they made fewer glances to mirrors and to the rear of the vehicle when using the system, a finding that persisted even after the system was subsequently turned off. Backover crash risk associated with use of the systems was estimated to be very high, as between 80 and 93 per cent of participants subsequently collided with an unexpected obstacle that had been surreptitiously positioned behind their vehicle by a confederate researcher, despite the presence of an active backing aid that issued an auditory warning or displayed video showing the rear of the vehicle. Subjective survey studies also suggest the potential for behavioral adaptation to backing aids. Drivers of vehicles equipped with backing aids report over-relying on the systems, so that they make fewer glances to rear view and side mirrors, and fewer direct glances, while backing (Llaneras, 2006; Jenness et al., 2011).

6.5. Vehicle countermeasures that automate part(s) of the driving task 6.5.1. Adaptive cruise control (ACC) Adaptive Cruise Control (ACC) allows a vehicle to follow a lead vehicle at an appropriate distance by controlling the engine and/or power train and/or the brake. A vehicle equipped with ACC will reduce speed automatically, within limits, to match the speed of a slower vehicle that it is following. In a driving simulator study, Hoedemaeker and Brookhuis (1998) tested the ability of ACC to induce behavioral adaptation. Regardless of driving style (speed choice and the ability to ignore distractions), results showed behavioral adaptation in terms of higher speed, smaller minimum time headway, and larger brake pedal forces. Although most drivers evaluated the system very positively, the potential safety effects of ACC are challenged by such results. Test-track research (Rudin-Brown and Parker, 2004) examined whether drivers would devote more attention to a secondary numberentry task while their vehicle’s headway time and distance to a lead vehicle was under control of ACC. Results showed that ACC could induce behavioral adaptation in drivers in safety critical ways. Compared to when driving without ACC, participants were able to identify significantly more items on a secondary task when using ACC while, at the same time, their hazard detection time to a lead vehicle’s brake lights increased. Furthermore, ACC was associated with significantly more lane position variability, and drivers’ trust in ACC increased significantly after using the system, despite a simulated failure of the ACC system during testing. All drivers reported relying on the ACC system to keep the vehicle at a safe distance from the lead vehicle. 6.6. Lane keeping assistance systems Unlike LDW systems, which only warn the driver of a lane departure, lane keeping assistance systems (LKAS) automatically intervene by activating a vehicle’s electric power steering system to guide the vehicle back into the lane if a driver fails to signal an intent to change lanes (i.e., via turn signal) or fails to prevent a lane departure. While there have been few evaluations of LKAS in terms of their propensity to result in behavioral adaptation, recent subjective data from interviews with early adopters of automated vehicles (Lin et al., 2018)

6.4. Lane departure warnings Lane departure warning (LDW) systems provide audible, visual and/ or haptic (i.e., creating a sense of touch through application of force or vibration) warnings to drivers when their vehicle unintentionally 6

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suggest that drivers may adapt to the presence of LKAS by devoting more time to in-vehicle secondary tasks. Twenty drivers who owned and operated a Tesla partially automated vehicle (PAV) for between one and five months completed a semi-structured telephone interview, which was designed to assess drivers’ level of trust in the system, their mental model of the driving task, and the likelihood for behavioral adaptation. Results showed that most drivers had a very positive view of the system, and that all drivers engaged in secondary tasks when the system was active. Eighteen of the 20 participants reported that these secondary tasks resulted in them looking away from the forward roadway for periods of between 3 and 5 s, and even longer when the participants travelled on familiar roads. One participant said that he had “spent almost half an hour playing on my mobile phone without looking up to check the road conditions” (p.41).

(Decina et al., 2007; Vanlaar et al., 2012). This detour behavior can consequently be accompanied by negative impacts on the alternate route(s), including excessive speeding and increased crash rates (i.e., collision migration) (ARRB (Australian Road Research Board), 2005). Drivers also speed up in locations adjacent to fixed speed cameras, possibly to make up for any perceived time lost from slowing down in the presence of speed cameras (ARRB (Australian Road Research Board), 2005). These findings strongly suggest that, in order to appropriately evaluate all effects of fixed speed cameras, locations outside the immediate camera treatment zone(s) must also be monitored. Nonetheless, it must be emphasized, as with other countermeasures discussed above, that the overall impact of speed cameras, despite the evidence of some negative adaptation, is very positive (Elvik, 1998).

7. Enforcement countermeasures and behavioral adaptation

7.3. Cell phone prohibition

7.1. Red light cameras

Research has conclusively demonstrated that the use of mobile phones while driving can be distracting and can increase the risk of collision (Caird et al., 2008; Klauer et al., 2006). Banning the use of all mobile phones and devices while driving would be expected to effectively minimize driver distraction due to their use. However, due to social, political and economic pressures, legislation is often enacted that prohibits only the use of handheld mobile phones, while permitting the use of hands-free devices (what is known as a ‘partial ban’). Previous research has shown that the cognitive component of a mobile phone conversation can be distracting (Harbluk et al., 2007; Engström et al., 2005). An argument against banning handheld mobile phones while permitting the use of hands-free devices is that it may convey the (incorrect) message that it is safe to drive while talking on a hands-free device (Kircher et al., 2011; Caird et al., 2008). Another reason against partial bans is that they might encourage drivers to engage in other forms of more easily concealed, but much higher risk, electronic communication like text-messaging when they might otherwise refrain. Because it requires frequent glances away from the forward roadway, text-messaging is associated with driving impairments (Drews et al., 2009) and increased crash risk (Olson et al., 2009). Concealed text-messaging is a form of behavioral adaptation to phone bans, the likelihood of which would be even greater if enforcement efforts were not taken seriously, and/or if public opinion was not in support of the legislation. Research on mobile phone usage rates in Australia (Rudin-Brown et al., 2009a, 2009b) and the U.S. (NOPUS (National Occupant Protection Use Survey), 2010) suggests that partial bans may be associated with proportional increases in text-messaging. The state of Victoria, Australia has had legislation against driving while using handheld mobile phones since at least 19661; however, it does allow drivers to use commercially available hands-free devices. A 2009 observational survey of drivers’ mobile phone use in Victoria (Melbourne) showed that, while only 1.4% of drivers were observed to be communicating via a hands-free device, a significant proportion of drivers (3.4%) engaged in handheld mobile phone use, including textmessaging (1.5%). Whether this rate of text-messaging was indicative of behavioral adaptation, through a migration of phone use from handheld to text-messaging, was explored by comparing it to the rate of driver text-messaging observed using a similar data collection method in a jurisdiction where handheld phone use was not banned. In 2009, the percentage of drivers observed “text-messaging or visibly manipulating other handheld devices” or talking on a handheld phone in the U.S. Midwest (where all forms of mobile phone use have always been permitted) (NOPUS (National Occupant Protection Use Survey), 2010) was 0.6 percent (compared to 1.1% in Melbourne), and

First introduced as enforcement tools in Europe in the early 1970s, red light cameras are designed to photograph vehicles entering an intersection after the signal has turned red, and detect an offence through a combination of pavement sensors and a timing system linked to the traffic signals (Retting et al., 2003). In addition to improved enforcement, the installation of red light cameras and associated signage and education efforts is intended to act as a general deterrent to all drivers with respect to disobeying red traffic signals. While they reduce the number of red light violations and collisions involving right-angle impacts, red light cameras are consistently associated with increases in rear-end crashes (Decina et al., 2007; Garber et al., 2005; Council et al., 2005; Burkey and Obeng, 2004; Retting et al., 2003; Butler, 2001). The increase in rear-end crashes is likely caused by following drivers not reacting in time to avoid a lead vehicle’s abrupt deceleration (Retting et al., 2003). A following driver’s late detection of a lead vehicle’s abrupt deceleration can be considered a form of behavioral adaptation. This appears to have resulted in a secondary stage of adaptation. Research from the Canadian city of Winnipeg, Manitoba, where red light cameras were first introduced in 2002, tells of this secondary stage. That research found that, in addition to a consistent 46% decrease of right-angle crashes, red light cameras were associated with an initial 42% increase in rear-end crashes, which subsequently fell by 19% (Vanlaar et al., 2012). This time-series analysis suggests that, over time, drivers following the abruptly slowing lead vehicles may better adjust, or ‘calibrate’, their stopping behavior in the presence of red light cameras, attenuating the incidence of rear-end crashes. 7.2. Fixed speed cameras Fixed speed cameras capture instances of vehicles exceeding the posted speed limit by taking single shots, or by averaging a vehicle’s speed over several measurements. Fixed speed cameras are usually accompanied by visible enforcement efforts, as well as signage, to maximize their potential for general deterrence. Apps for smart phones and in-vehicle navigation systems can provide a driver with information regarding the locations of fixed speed cameras, and can be programmed to provide alerts (Decina et al., 2007). These efforts to increase the cameras’ conspicuity decrease the number of speed violations at the location of the camera installation; however, research (ARRB (Australian Road Research Board), 2005) shows that they may inadvertently increase the likelihood of behavioral adaptation, in terms of increased speeds at areas adjacent to the camera. In fact, research has shown that approximately 5% of the approximate 25% reduction in injury crashes results from changes in traffic flow away from the treated road segments as drivers who normally use routes passing the speed camera locations take detours to avoid them

1 Regulation 153(1) of Victoria’s Motor Car Regulations (1966) states ‘Except with the approval of the Chief Commissioner the driver of a motor car shall not while the motor car is in motion use any telephone microphone or any other similar instrument or apparatus in such motor car’

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training being involved in more accidents than drivers who had completed their training before the requirement for a second-phase had been introduced. The effect was found for males only. Jones (1993) findings on the impact of skid control training with U.S. teenaged drivers were particularly interesting. The trained teenagers showed (marginally significant) worse driver records overall, but better performance in the slippery conditions at which the training had been aimed. An advanced car handling driver-training program in Germany was evaluated by Siegrist and Ramseier (1992). Self-reported subsequent collision involvement was no different overall for the course participants than for a control group. However, drivers who participated in two or more courses were involved in more collisions than those who only participated in one. Of particular interest, those who reported feeling that the course had a positive effect on safety had a higher subsequent collision rate than those participants who noted no safety benefits. The 1990 OECD report on behavioral adaptation interpreted the negative safety effect of car handling skills training as consistent with other European findings of behavioral adaptation, such as those found for studded tires and anti-lock brake systems (OECD, 1990). The OECD committee stated that: “These results suggest that a high level of driving skill is associated with a high crash risk. This apparent contradiction could be explained as follows: the belief of being more skilled than fellow drivers increases confidence in one's abilities more than it increases actual abilities. A high confidence in one's abilities could lead to an aggressive style of driving that could lead to more critical situations. If the driver's increased skill is not in proportion to the increased number of critical situations, then there will be more accidents.” (p. 79) Lonero and colleagues reviewed evaluations of earlier advanced driver training programs (Lonero et al., 1995), concluding that practical safety benefits will only occur if these programs are coordinated with motivational influences, such as ‘insight training’ showing drivers their skill limitations. Otherwise, as Naatanen and Summala (1974) suggest, there is a clear danger that “…increased skills raise the level of aspiration in driving (higher speed, more frequent overtaking, smaller margins of safety, etc.)” (p. 243) (Naatanen and Summala, 1974). This danger is clearly illustrated by a study that found that licensed amateur race drivers, as a group, had rather poor on-road driving records, based on crashes per kilometer driven on public roads, despite their presumably superior car-handling skills (Williams and O’Neill, 1974).

the proportion of drivers using a hands free mobile phone was 0.6 percent (compared to 1.4% in Melbourne). These results suggest that at least some drivers in Melbourne may have exhibited behavioral adaptation by choosing to use other (handheld) phone options that are easy to conceal. Interestingly, an evaluation of insurance claims in the U.S. revealed an unexpected increase in the frequency of insurance claims in those states that had enacted text-messaging bans compared to neighboring states (IIHS (Insurance Institute for Highway Safety), 2010). The study authors suggest that texting drivers may have responded to the bans by attempting to avoid fines by hiding their phones from view, on their laps. Because this would require drivers to look further away from the road ahead, and for longer periods of time, the bans may have inadvertently made the text-messaging behavior among a portion of drivers more dangerous. Results of 2006–2017 observational surveys of Canadian drivers stopped at intersections (CCMTA (Canadian Council of Motor Transport Administrators), 2016) also suggest changes in use patterns over time and the potential migration towards handheld (e.g., typing, texting) vs. talking functions in the years following the implementation of partial bans. 8. Driver education countermeasures and behavioral adaptation Driver education is generally seen as a positive influence on driver safety. From time to time, after a particular egregious crash involving a teenager, a commercial vehicle driver, or a senior, there will be calls for more – and more frequent – driver education for the target group. How effective a countermeasure would that likely be? The best place to look for an answer would be in the experience of learner drivers, as one would anticipate that the greatest impact of driver education would be on such drivers. 8.1. School-based driver education The U.S. DeKalb County Driver Education Project was the most comprehensive experiment in beginner driver education, based in the then-typical delivery of U.S. driver education in public secondary schools. Volunteer high school students (18,000) were randomly assigned to one of three groups: no formal driver education, standard driver education or special intensive driver education, called the Safe Performance Curriculum (SPC). SPC was developed to represent the “state-of-the-art” in driver education, both in terms of content and methods. The SPC was much longer and more carefully developed than typical driver education curricula available at the time, and was strongly oriented toward improvement of drivers’ hazard perception skills. Detailed reanalysis of the study data showed that SPC drivers were more likely than control drivers (those with no formal training) to obtain licenses, have violations, and experience collisions. Students assigned to the standard driver education were also more likely than control students to obtain licenses, but the increases in collisions and violations were smaller and statistically insignificant. Students in the standard course took longer to obtain their licenses than SPC drivers, suggesting that the greater availability of driver education resulted in drivers being licensed earlier. As a result of this earlier exposure, crashes and violations occurred at an earlier age than otherwise (Lund et al., 1986).

9. Driver impairment and behavioral adaptation 9.1. Impaired driving policies Various strategies have been implemented in attempts to address alcohol-related crash fatalities and serious injuries. These include, for example: legislating per se limits of maximum driver BAC limits, including zero BAC limits for novice drivers; targeted and random breath testing (RBT); and mandatory drunk driving sanctions, including interlocks for repeat drunk driving offenders and education/treatment program attendance. Evaluations of these strategies have supported their ability to reduce drunk driving and associated crashes (Voas and Lacey, 2011); however, behavioral adaptation may offset or reduce their effectiveness. Legislating a per se legal driver BAC limit may introduce challenges similar to those faced with the adoption of partial bans of mobile phones. Legal alcohol limits can present an ambiguous and inaccurate message to drivers that an alcohol intake below the legal limit (e.g., < 0.05 to 0.08% BAC in North America) is safe and does not impair driving skills. Another potential behavioral adaptation in response to BAC requirements is the potential for migration of substance use from alcohol to other drugs (Davey et al., 2005; Patton et al., 2005). Speculation exists that, with the passage of tougher drinking and

8.2. Skid control training Similar to the DeKalb results, evaluations of some advanced driver training programs suggest the counterintuitive conclusion that raising levels of driving skill does not necessarily reduce crashes. Indeed, in some cases car handling training is actually associated with a higher crash risk. Glad (1988), in Norway, found a negative safety effect of a mandatory second-phase of driver training involving a slippery-road training session, with those drivers who received the second phase 8

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and sustained attention is required (see Smiley, 1999 for a review). Table 1 provides a summary of the countermeasures considered, and the behavioral adaptation and safety consequences that have been observed, to-date, in the research literature. What is evident, is that behavioral adaptation is common – almost a given – consequence of any and all road safety countermeasures. It is consequentially imperative that this – essentially ubiquitous – road safety phenomenon be given due attention and respect by safety researchers and change agents alike in order to minimize its potentially deleterious and negating effects. Without this attention and due consideration, behavioral adaptation will prevent many (if not all) innovative, well-designed (and well-intentioned) future road safety countermeasures from achieving their full potential in terms of crash reduction and safety benefits.

driving laws and the increased enforcement of these laws, some drivers, particularly young ones, may move away from alcohol and instead choose to use psychoactive drugs that are less likely to be tested, such as cannabis or the combination of alcohol and cannabis. It is important to note, here, that there is currently no empirical research to support either of the above possibilities regarding younger drivers, and research is therefore needed in this area. Many countries’ driving under the influence (DUI) laws are modelled on a system that allows a driver to be stopped both on suspicion of impaired driving and at random without any requirement for suspicion of impairment. The use of ‘booze buses’ (as well as drug buses) in Australasia is an application of this system in terms of enforcement practices, and the majority of drunk driving detection and enforcement activities in those countries occurs through their deployment. These highly visible buses allow for the random testing of high proportions of drivers in an environment that is safe, both for the police and for drivers, with minimal disruption to traffic. These buses were designed to be highly conspicuous to maximize their “general deterrence” effects. In this manner, booze buses not only enable the breath testing of a large volume of passing motorists, but they can also deter other drivers who pass the buses but are not stopped. The use of booze buses is associated with significant decreases in the elevated crash rates associated with higher alcohol sales venues (Chikritzhs and Stockwell, 2006), which illustrates how the deterrent effect of these highly visible, drunk driving enforcement practices can be used as a crash prevention strategy. While their effectiveness in deterring drivers from drinking before driving is documented (Delaney et al., 2006), behavioral adaptation to the presence of RBT enforcement practices has been observed. For example, research from New Zealand has identified a pattern among drunk drivers to detour off main roads and on to local residential streets in attempts to avoid detection (Keall and Frith, 1997). This behavior may be associated with an increase in crashes on this road type and/or with increases in the number of drunk driving crashes involving vulnerable road users, such as pedestrians and cyclists. It may also undermine the effectiveness of the current RBT system, which aims to increase drivers’ perception that alcohol use by drivers will be detected.

10. Inventory of characteristics to consider when estimating the likelihood for behavioral adaptation The effectiveness of road safety countermeasures could likely be more accurately estimated if the potential for behavioral adaptation was considered during planning. Several authors (Cacciabue and Saad, 2008; Elvik, 2002; Rudin-Brown and Noy, 2002; Hedlund, 2000; Bjørnskau, 1994; OECD, 1990) have identified factors, or qualities, of intended countermeasures that likely affect whether behavioral adaptation will develop as a consequence. Rudin-Brown and Noy’s “Qualitative” model of behavioral adaptation predicts that the quality of feedback from a given countermeasure (the Qualitative model describes the process of behavioral adaptation to in-vehicle driver assistance systems), in terms of timing (immediate vs. delayed), amount (exposure) and persistence, influences the likelihood and nature of behavioral adaptation (Rudin-Brown and Noy, 2002; Rudin-Brown, 2010). An adapted version of this model is presented here (Fig. 2) to illustrate more specifically how certain characteristics of a road safety countermeasure can increase (or decrease) the likelihood of driver behavioral adaptation. The model shows how a road safety countermeasure can target driver behavior directly (e.g., policies to limit certain behaviors by drivers; changes in driver training), or it can target the object of driver behavior, be it the vehicle (e.g., ABS; LDW), the road (e.g., improved path delineation; rumble strips), or the environment (e.g., countermeasures that increase a vehicle’s conspicuity; improved roadway lighting). An important concept included in this version of the model is the intended safety outcome of a given road safety countermeasure: which can be either crashworthiness, or crash avoidance. Within the model (in black text) are seven characteristics of road safety countermeasures that can be used to predict whether, and to what extent, behavioral adaptation will occur. Consideration of these factors previously by Rudin-Brown et al., 2013 resulted in the generation of a comprehensive feature inventory. This inventory is expanded and presented here in the hopes of answering Hauer’s question of when can we anticipate the full safety effect of a treatment and when can we not (Hauer, 2019). Positioning these countermeasure features, or characteristics, within the context of the adapted Qualitative model can help identify where, in the development process, to aim strategies to limit the expression of behavioral adaptation. Doing this, it is hoped, will lead to reductions in crash risk and improvements in overall road safety.

9.1.1. Alcohol vs. Cannabis impairment The negative effects of alcohol on driver behavior are well-known (Moskowitz, 2015). Like alcohol, cannabis impairs cognitive functions that are involved in the safe operation of motor vehicles (Bondallaz et al., 2017). A meta-analysis review of experimental studies found that a THC2 concentration in the blood of between about 3.5 and 5 ng per milliliter (ng/ml)3 correlates with driving impairment comparable to that associated with a BAC of 0.05%, and has been proposed as a suitable legal driving limit (Grotenhermen et al., 2007). Studies show a low to moderate increase in risk of being involved in a motor-vehicle accident (Brubacher et al., 2019; Rogeborg and Elvik, 2016). Although over-confidence in one’s driving skills may underlie alcohol’s negative effects on driving performance, the consumption of cannabis is associated with very different effects on driving. For example, cannabis is associated with slower speeds and longer headways while following a lead vehicle (Smiley, 1999). Cannabis’ impairment is often mitigated, in that drivers are more likely to be aware of their impairment than are drivers who are impaired by alcohol. In experimental studies, participant drivers who are under cannabis treatment appear to perceive that they are indeed impaired. Where they can compensate, they do, for example by staying in their lane and not overtaking slower-moving lead vehicles, by slowing down and by focusing their attention in situations where they have a strong expectation that a response will be required. Such compensation is not always possible, however, when events are unexpected or where continuous 2 3

10.1. Transparency of feedback The ‘transparency’, or ‘noticeability’, of feedback provided by a road safety countermeasure to road users is likely to have a significant impact on the expression of behavioral adaptation. Hfedlund (2000) refers to this factor as ‘visibility’ of the safety measure, while the OECD described it as road users’ interaction with a safety measure, and the immediacy of the feedback they receive from it (OECD, 1990). In an earlier effort to define the conditions influencing road user behavioral adaptation, Bjørnskau (1994) described this feature as how easily a

Tetrahydrocannabinol - the principal psychoactive element in cannabis. Equivalent to 7 to 10 ng/ml in serum. 9

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Table 1 Research Evidence for Driver Behavioral Adaptation. Behavioral Change

Highway and TCD Design

Vehicle Design

Enforcement

↑ speed ↓ speed

Long stopping distance to roundabouts, PMDs, high contrast lane markings, raised crosswalks, narrowed comfortable lane width Road re-surfacing, PRPMs, narrowed comfortable lane width

ACC ABS

Speed cameras

↑ crashes ↓ crashes ↑ confidence / risk taking ↑ inattention ↑ texting ↑ changes in exposure

Red light cameras ESC ACC, backing aids LKAS Speed cameras

Driver Education

Alcohol/Drug/Cell Policies

Skid training

Cell Phone RBT alcohol

TCD – Traffic Control Device ACC – Adaptive Cruise Control RBT – Random Breath Testing. PMD – Post Mounted Delineators ABS – Antilock Brakes. PRPM – Permanent Raised Pavement Markers ESC – Electronic Stability Control. LKAS – Lane Keeping Assistance Systems.

Fig. 2. Modified Qualitative Model of Behavioral Adaptation (Rudin-Brown and Noy, 2002; Rudin-Brown, 2010), and seven characteristics likely to contribute to its expression.

installed as original equipment on all new vehicles with little associated awareness raising activities regarding its effectiveness on crash avoidance, essentially leaving drivers to drive as they always had and so maximizing the benefits of the countermeasure.

countermeasure is noticed. Feedback from a countermeasure, whether it is directed at the vehicle, roadway, or user (red elements in Fig. 2), can be either direct, through experience, or inferred, through information received from others, educational campaigns, or the media. If, for example, a new driver assistance system (e.g., ESC) is hailed by friends and the media as having only positive effects on safety, a driver may be more likely to trust that it will do what it was designed to do and demonstrate behavioral adaptation by, for example, driving faster in adverse weather conditions. On the other hand, if a device is publicized as having negative effects, behavioral adaptation would be less likely. The quality of feedback will also influence the extent of any behavioral adaptation, including timing (whether it is immediate or delayed), the amount, and its persistence. For example, through rapid automatic application, ABS provide feedback to drivers and are associated with behavioral adaptation. On the other hand, air bags do not provide feedback, and are not associated with behavioral adaptation (Sagberg et al., 1997). Based on the predictions of the model (Fig. 2), transparency of feedback between the object or the effect of a safety countermeasure could be targeted to limit the development of behavioral adaptation. A potentially effective strategy would be to limit the amount and conspicuity of feedback provided to drivers. For example, an in-vehicle crash avoidance system that operates with limited additional driver input (e.g., ESC) could be

10.2. Type of safety effect The goal, or effect (Hedlund, 2000), of a road safety countermeasure (e.g., crash mitigation (including changes in exposure [Kulmala, 2010]) or avoidance vs. crashworthiness) will determine, to an extent, the degree to which behavioral adaptation is expressed. In proposing preconditions that may influence road user behavioral adaptation, Bjørnskau (1994) suggested that whether or not a countermeasure primarily reduces injury severity (in instances where a collision had already occurred) would be predictive of future behavioral adaptation. If a countermeasure targeted injury severity, then it would be less likely to result in behavioral adaptation than one which decreases the likelihood of having a collision in the first place (Elvik, 2002). This is because road users see crashes as the unwanted events (rather than the injuries sustained in crashes), and are therefore less likely to perceive crashworthiness countermeasures as providing an increase in safety margin. The requirement to wear seat belts, which reduce injury but do not reduce crash risk, can be considered as an example of this situation. 10

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driver’s motivation to compensate for any changes in safety. If one is motivated to compensate for a safety change, then it will be more likely to happen, compared to if one is not motivated to compensate. The OECD called this feature “superimposing of the driving goals by extra motives” (OECD, 1990), while Bjørnskau (1994) described it as whether or not additional “utility” of driving could be gained as a consequence of a countermeasure. Cacciabue and Saad (2008), in referring to driver assistance systems, label this factor as a user’s intentions, or goals. An example of a user need influencing the extent of behavioral adaptation would be some drivers’ perceived need to speed, and to avoid potential speeding tickets, by taking detours around fixed speed camera locations and consequently travelling (and potentially crashing) more on neighboring roads (ARRB (Australian Road Research Board), 2005; Mountain et al., 2004). Following on from these assumptions, it should be possible to influence drivers’ needs and motivations, and consequently to increase or decrease the likelihood of behavioral adaptation, through the application of social marketing principles that modify social norms and practices.

While the type of a countermeasure’s safety effect cannot be changed to limit the development of behavioral adaptation, it is possible to moderate it by, for example, providing driver education and/or awareness training. This could target several aspects of driver behavior: 1) improving the “calibration” of drivers’ reliance on a given countermeasure (e.g.., adjusting drivers’ perception of the trustworthiness of an in-vehicle warning or automated system), 2) emphasizing the importance of drivers remaining in control of their vehicle at all times, 3) increasing student drivers’ awareness and understanding of their interaction tendencies (e.g., individual differences in sensation seeking – see below) with new equipment or countermeasures, and 4) providing information on how to best approach novel road safety countermeasures to maximize their effectiveness. 10.3. Magnitude of effect The extent to which a road safety countermeasure is associated with changes in its target risk factor will influence the likelihood of behavioral adaptation. That is, the more noticeable, or larger, an effect, the more likely it is that road users will use any perceived benefits for other goals. Elvik (2002) and Bjørnskau (1994) call this the size of the ‘engineering effect’, and conclude that large changes will be more easily noticed than small changes, and are therefore more likely to be perceived by road users as major additions to safety margins. The OECD suggested that a road safety countermeasure that provides “an increase in subjective safety” is more likely to result in behavioral adaptation than one that does not (OECD, 1990). An example of an increase in subjective safety would be when drivers are allowed to experience the effects of ABS, and subsequently drive a vehicle equipped with ABS more recklessly than a non-equipped vehicle (Grant and Smiley, 1993). As per the model (Fig. 2), a strategy to limit feedback to drivers regarding the magnitude of effect of a given countermeasure could be implemented to minimize the potential development of behavioral adaptation.

10.6. User autonomy The degree to which a driver is free to change their behavior, or the degree to which they ‘control’ a situation (Hedlund, 2000), will influence the expression of behavioral adaptation. The OECD (1990) conceptualized this factor as “extending the (road user’s) freedom of action” (p.65). They use the example of the requirement for daylight running lights (DRL), which improve the noticeability of a vehicle to other drivers but have no effects on an equipped vehicle driver’s “freedom of action”, to illustrate a countermeasure that would be less likely to be associated with behavioral adaptation on the part of the driver. On the other hand, ABS, because it extends the driver’s freedom of action by improving vehicle stopping (at least on most surface types), would be more likely to be associated with behavioral adaptation. 10.7. Individual differences

10.4. Trustworthiness / perceived effectiveness Individual differences are often not considered in models of road user behavior or behavioral adaptation; however, several (e.g., a driver’s sensation-seeking tendencies) have been demonstrated to contribute to the expression of behavioral adaptation (Rudin-Brown and Parker, 2004) and have been included in the Qualitative Model of Behavioral Adaptation (Rudin-Brown and Noy, 2002; Rudin-Brown, 2010). Drivers who seek high sensation experiences tend to drive more recklessly given another unique or risky optional (secondary) task to perform (Rudin-Brown and Parker, 2004; Jonah, 1997; Burns and Wilde, 1985), and thus may be more likely to demonstrate behavioral adaptation, especially where a countermeasure is perceived as reducing the level of risk. In fact when asked, high sensation-seekers are more likely that low sensation-seekers to say that they would speed and drive while impaired if they were driving a vehicle that was equipped with ABS (Jonah et al., 2001). Two potential means to limit the expression of behavioral adaptation in groups of individuals that are at an elevated risk of developing it (i.e., high sensation seekers) include: raising awareness of the potential for (and teaching alternatives to) behavioral adaptation through training or education programs, and fully automating the driving task to limit the effects of individual driver behavior. Full automation (as opposed to only limited, or partial, automation, which relies on a driver’s ability to monitor driving constantly and being prepared to take over the driving task at potentially unexpected moments) will remove entirely the influence of driver individual differences and the resulting behavior from the driving task, and so is expected to be associated with clear and precise predictions of safety effects. There are, no doubt, other individual characteristics beside sensation-seeking that affect behavioral adaptation. Knowledge of these characteristics could similarly assist in designing better

To be effective, a road safety countermeasure must be perceived by drivers as being ‘trustworthy’ (Rudin-Brown, 2010; Rudin-Brown and Noy, 2002), in the sense that they feel that its effects are both significantly positive and reliable over time. Without this degree of trust in a system, drivers would be less likely to experience the countermeasure’s positive effects, resulting in a reduced likelihood of behavioral adaptation. An example of a countermeasure that may not be trusted, and therefore would be less likely to be associated with behavioral adaptation, would be mobile phone bans, especially if drivers perceive them as un-credible or if they do not believe that a ban will contribute to improved road safety. Similarly, if a countermeasure is not trusted by law enforcement — for example if police perceive penalties for mobile phone use to be too harsh — they may be less likely to formally charge a driver, consequently reducing the likelihood of behavioral adaptation among that group. Following this logic, providing feedback that includes clear and realistic expectations of a given countermeasure as a strategy to prevent behavioral adaptation (e.g., advertising that a collision warning system may not identify every crash situation) may be effective. 10.5. User need(s) / motivation The extent to which a road safety countermeasure matches drivers’ need(s), including mobility, workload, risk and pleasure, will influence the degree of behavioral adaptation. Hedlund describes this construct as “motivation” (Hedlund, 2000), and sees drivers as being motivated by many factors, including economic and behavioral, as well as habit and the desire to simplify decisions (preferring to put many daily operations on “automatic pilot”). These factors all serve to influence a 11

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countermeasures.

original draft, Writing - review & editing.

11. Conclusions and recommendations

Declaration of Competing Interest

This paper considered how drivers change the way they drive in response to various well-intentioned road safety countermeasures, and the ubiquitous nature of behavioral adaptation. The effects of behavioral adaptation were described for a variety of countermeasures: highway design, traffic engineering, vehicle, enforcement, driver education, and impaired driving policies. Characteristics to consider when estimating the likelihood for, and extent of, behavioral adaptation in response to a road safety countermeasure were presented in the hopes of addressing the question “When can we anticipate the safety effect of a treatment and when not?”. A model (Fig. 2) was presented to help identify where in the development process to aim strategies to limit the expression of behavioral adaptation. This inventory of characteristics and model of behavioral adaptation are provided in the hopes that they be considered when estimating the indicators of a countermeasure’s potential to trigger and/or sustain behavioral adaptation in drivers, and where to target any limiting strategies, with the ultimate goal being improvements in countermeasure effectiveness and therefore overall road safety. Based on this review of the unexpected results following implementation of safety countermeasures, the following conclusions and recommendations can be made:

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Allen, R.W., O’Hanlon, J.F., McRuer, D.T., 1977. Drivers' visibility requirements for roadway delineation, Vol. I and II: Effects of contrast and configuration on driver performance and behavior. Report No. FHWA RD-77-165 & 166. Federal Highway Administration, Washington, D.C. American Association of State Highway & Transportation Officials (AASHTO), 2010. Highway Safety Manual, 1st ed. Atlanta, Georgia. Angelastro, M., 2010. The influence of driver sight distance on crash rates and driver speed at modern roundabouts in the United States. ITE J. 80 (7), 28–39. ARRB (Australian Road Research Board), 2005. Evaluation of the Fixed Digital Speed Camera Program in New South Wales (No. RC2416). Roads and Traffic Authority, NSW, Australia. Aschenbrenner, K.M., Biehl, B., Wurm, G.W., 1992. Is Traffic Safety Improved Through Better Engineering?: Investigation of Risk Compensation With the Example of Antilock Brake Systems. Bundesanstalt für Straßenwesen (BASt) Report No. R246. Bahar, G., Mollett, C., Persaud, B., Lyon, C., Smiley, A., Smahel, T., McGee, H.W., 2004. Safety Evaluation of Permanent Raised Pavement Markers. NCHRP Report 518. Transportation Research Board, Washington, D.C. Battista, V., 2001. Performance of ABS-equipped vehicles on deformable surfaces. In: Proceedings of the 12th Annual Canadian Multidisciplinary Road Safety Conference. London, ON, CD-ROM, June 10-13. Bhise, V.D., Rockwell, T.H., 1973. Development of a driver-information-acquisition based operational tool for the evaluation of highway signs. Report No. 440, highway research board. In: Presented at the 1973 Annual Meeting of the Highway Research Board. Washington, D.C. Bjørnskau, T., 1994. Hypotheses on risk compensation. In: Proceedings of the Conference on Road Safety in Europe and the Strategic Highway Research Program (SHRP). Lille, France. 26-28 September, 81-98. Bondallaz, P., Chtioui, H., Favrat, B., Fornari, E., Giroud, C., Maeder, P., 2017. Chapter 39: assessment of cannabis acute effects on driving skills: laboratory, simulator, and on-road studies. In: Preedy, V.R. (Ed.), Handbook of Cannabis and Related Pathologies. Academic Press, 2017, pp. 379–390. Bosch (Robert Bosch GmbH), 2019. Rear Cross Traffic Alert. Available at: https://www. bosch-mobility-solutions.com/en/products-and-services/passenger-cars-and-lightcommercial-vehicles/driver-assistance-systems/rear-cross-traffic-alert/. Last accessed: 2 February. Brubacher, J.R., Chan, H., Erdelyi, S., Macdonald, S., Asbridge, M., Mann, R.E., Eppler, J., Lund, A., MacPherson, A., Martz, W., Schreiber, W.E., Brant, R., Purssell, R.A., 2019. Cannabis use as a risk factor for causing motor vehicle crashes: a prospective study. Addiction 20 (May). https://doi.org/10.1111/add.14663. Epub ahead of print. Burkey, M., Obeng, K., 2004. A Detailed Investigation of Crash Risk Reduction Resulting From Red Light Cameras in Small Urban Areas. U.S. Department of Transportation, Research and Special Programs Administration, Washington, DC. Burns, P.C., Wilde, G.J.S., 1985. Risk taking in male taxi drivers: relationships among personality, observational data, and driver records. Pers. Individ. Dif. 18, 267–278. Butler, P.C., 2001. A Quantifiable Measure of Effectiveness of Red Light Running Cameras at Treatment and Non-treatment Sites. Howard University, Washington, DC. Cacciabue, P.C., Saad, F., 2008. Behavioural adaptations to driver support systems: a modelling and road safety perspective. Cogn. Technol. Work. 10, 31–39. Caird, J.K., Willness, C.R., Steel, P., Scialfa, C., 2008. A meta-analysis of the effects of cell phones on driver performance. Hum. Factors 40, 1282–1293. Campbell, J.L., Lichty, M.G., Brown, J.L., Richard, C.M., Graving, J.S., Graham, J., O’Laughlin, M., Torbic, D., Harwood, D.W., 2012. Human Factors Guidelines for Road Systems. 2nd ed. Transportation Research Board. NCHRP Report 600, Washington, D.C. CCMTA (Canadian Council of Motor Transport Administrators), 2016. Use of Electronic Communication Devices by Canadian Drivers: Combined urban/rural Sites. 2017. Summary report. Charlton, S.G., 2003. Restricting intersection visibility to reduce approach speeds. Accid. Anal. Prev. 35 (5), 817–823. Chikritzhs, T., Stockwell, T., 2006. The impact of later trading hours for hotels on levels of impaired driver road crashes and driver breath alcohol levels. Addiction 101, 1254–1264. Chouinard, A., Lécuyer, J.-F., 2011. A study of the effectiveness of electronic stability control in canada. Accid. Anal. Prev. 43, 451–460. Cicchino, J.B., 2019. Real-world effects of rear cross-traffic alert on police-reported backing crashes. Accid. Anal. Prev. 123, 350–355. Council, F.M., Persaud, B., Eccles, K., Lyon, C., Griffith, M.S., 2005. Safety Evaluation of Red-light Cameras (No. FHWA-HRT-05-048). Federal Highway Administration, Washington, DC. Davis, B., Morris, N., Achtemeier, J., Patzer, B., 2018. In-vehicle Dynamic Curve-speed Warnings at High-risk Rural Curves. Department of Mechanical Engineering, University of Minnesota, Minneapolis, Minnesota. Davey, J.D., Davies, A., French, N., Williams, C.K., Lang, C.P., 2005. Drug driving from a

1) The phenomenon of behavioral adaptation is ubiquitous (see Table 1) and can be seen for a wide variety of countermeasures including highway design, traffic control device design, vehicle design, enforcement, driver education, and driver impairment policies. 2) Behavioral changes following an intervention that increases driver confidence or comfort or reduces driver workload or perceived risk, should be anticipated by designers as a matter of course. These changes may include increased speed, increased confidence, increased risk-taking, changes in exposure to risky driving scenarios and/or decreased attention. The more obvious the impact of a countermeasure (e.g. adaptive cruise control [ACC], raised pavement markers [RPMs]) on the driver’s task, the more likely it is that behavioral adaptation will occur. Conversely, it can be expected that, if the safety benefit is hidden (e.g., air bags, seatbelts), behavioral adaptation is less likely to occur. Behavioral adaptation can have both positive and negative safety consequences. 3) More research is needed to determine the optimum conditions that are necessary for the effective use of a countermeasure (e.g., high geometric standard roads for effective implementation of RPMs) and the best context in which to introduce them (e.g., during a publicity campaign). 4) Typically, but not in all circumstances, the negative consequences of behavioral adaptation are outweighed by the overall positive effects of countermeasures on safety. However, this should not preclude investigation and consideration of behavioral adaptation for all potential road safety interventions, as well as targeted strategies to limit the expression of behavioral adaptation. 5) Adaptation takes place over time and may be less evident after a countermeasure has been in place for some time. 6) Both behavior and crash risk studies are needed to understand the behavioral changes that are likely to occur in response to an intervention, and how these changes may affect crash risk, with the aim of developing more effective countermeasures by preventing negative safety behaviors. CRediT authorship contribution statement Alison Smiley: Conceptualization, Writing - original draft, Writing review & editing. Christina Rudin-Brown: Methodology, Writing 12

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Kircher, K., Patten, C., Ahlström, C., 2011. Mobile Telephones and Other Communication Devices and Their Impact on Traffic Safety - a Review of the Literature (No. VTI Rapport 729A). Linköping, Sweden: VTI. Klauer, S.G., Dingus, T.A., Neale, V.L., Sudweeks, J., Ramsey, D., 2006. The Impact of Driver Inattention on near-crash/crash Risk: an Analysis Using the 100-car Naturalistic Driving Study Data. Report No. DOT HS 810 594. National Highway Traffic Safety Administration, Washington, DC. Kulmala, R., 2010. Ex-ante assessment of the safety effects of intelligent transport system. Accid. Anal. Prev. 42, 1359–1369. Kulmala, R., Rämä, P., 2013. Definition of behavioural adaptation. Chapter 2. In: RudinBrown, C., Jamson, S. (Eds.), Behavioural Adaptation and Road Safety: Theory, Evidence, and Action. Taylor & Francis (CRC Press), Boca Raton, FL, pp. 11–21. Lee-Gosselin, M., Fournier, P.-S., Béchard, I., 2001. Driver knowledge and beliefs about antilock brake systems. Transp. Res. Rec. 1779, 62–67. Lerner, N., Williams, A., Sedney, C., 1988. Risk perception in highway driving: executive summary. Rep. No. FHWA Project No. DTFH61-85-C-00143, U.S. Department of Transportation. Federal Highway Administration, Washington, D.C. Lin, R., Ma, L., Zhang, W., 2018. An interview study exploring Tesla drivers’ behavioural adaptation. Appl. Ergon. 72, 37–47. Llaneras, R.E., 2006. Exploratory Study of Early Adopter, Safety-related Driving With Advanced Technologies. NHTSA report No. DOT HS 809 972. Washington, DC. . Lonero, L.P., Clinton, K.M., Brock, J., Wilde, G., Laurie, I., Black, D., 1995. Novice Driver Education Model Curriculum Outine. AAA Foundation for Traffic Safety, Washington, D.C. Lund, A.K., O’Neill, B., 1986. Perceived risks and driving behavior. Accid. Anal. Prev. 18, 367–370. Lund, A.K., Williams, A.F., Zador, P., 1986. High school driver education: further evaluation of the Dekalb County study. Accid. Anal. Prev. 18 (4), 349–357. Maroney, S., Dewar, R.E., 1987. Alternatives to enforcement in modifying speeding behavior of drivers. Transp. Res. Rec. 111, 121–125. Mazzae, E.N., Barickman, F., Baldwin, G.H.S., Ranney, T., 2008. On-road Study of Drivers’ Use of Rearview Video Systems (ORSDURVS). NHTSA report No. DOT HS 811 024, Washington, DC. . Mazzae, E.N., 2010. Drivers’ use of rearview video and sensor-based backing aid systems in a non-laboratory setting. NHTSA Research Report in Docket Number NHTSA-20100162. December. Mazzae, E.N., Garrott, W.R., 2007. Experimental Evaluation of the Performance of Available Backover Prevention Technologies for Medium Straight Trucks. NHTSA report No. DOT HS 810 865. Washington, DC. . Moskowitz, H., 2015. Alcohol and Drugs. In: Smiley, A. (Ed.), Human Factors in Traffic Safety, 3rd ed. (Chapter 9). Mountain, L., Hirst, W., Maher, M., 2004. Costing lives or saving lives? A detailed evaluation of the impact of speed cameras on safety. Traffic Eng. Control 45, 280–288. Mourant, R.R., Rockwell, T.H., 1970. Mapping eye-movement patterns to the visual scene in driving: an exploratory study. Hum. Factors 12 (1), 81–87. Naatanen, R., Summala, H., 1974. A model for the role of motivational factors in drivers’ decision-making. Accid. Anal. Prev. 6, 243–261. NOPUS (National Occupant Protection Use Survey), 2010. Driver Electronic Device Use in 2009. Traffic Safety Facts: Research Note. Retrieved from www.distraction.gov/ research/PDF-Files/Driver-Electronic-Device-Use-2008.pdf. OECD, 1990. Behavioural Adaptations to Changes in the Road Transport System. Organization for Economic Co-operation and Development, Road Transport Research, Scientific Expert Group, Paris, France. Olson, R.L., Hanowski, R.J., Hickman, J.S., Bocanegra, J., 2009. Driver Distraction in Commercial Vehicle Operations. FMCSA-RRR-09-042. Federal Motor Carrier Safety Administration., Washington, DC. Papelis, Y.E., Watson, G.S., Brown, T.L., 2010. An empirical study of the effectiveness of electronic stability control system in reducing loss of vehicle control. Accid. Anal. Prev. 42 (3), 929–934. Patton, D., Mackay, T.-L., Broszeit, B., 2005. Alcohol and Other Drug Use by Manitoba Students. Addictions Foundation of Manitoba (May). Available at www.afm.mb.ca. Persaud, B., Retting, R., Lyon, C., 2003. Crash Reduction Following Installation of Centerline Rumble Strips on Rural Two-lane Roads. Insurance Institute for Highway Safety, Arlington, Virginia. Retting, R.A., Ferguson, S.A., Hakkert, A.S., 2003. Effects of red light cameras on violations and crashes: a review of the international literature. Traffic Inj. Prev. 4, 17–23. Rogeborg, O., Elvik, R., 2016. The effects of cannabis intoxication on motor vehicle collision revisited and revised. Addiction 111 (8), 1348–1359. Rompe, K., Schindler, A., Wallrich, M., 1987. Advantages of an anti wheel lock system (ABS) for the average driver in difficult driving situations. In: Proceedings of the 11th International Technical Conference on Experimental Safety Vehicles. Washington, DC, 442-448. Rudin-Brown, C.M., Burns, P., Hagen, L., Roberts, S., Scipione, A., 2012. Behavioural adaptation as a consequence of extended use of low-speed backing aids. In: Sullman, M., Dorn, L. (Eds.), Advances in Traffic Psychology. Ashgate: Farnham, UK, pp. 285–294. Rudin-Brown, C.M., Jonah, B., Boase, P., Rudin-Brown, C.M., 2013. Behavioural adaptation to road safety policy. In: Jamson, S.L. (Ed.), Behavioural Adaptation and Road Safety: Theory, Evidence, and Action. Taylor & Francis (CRC Press), Boca Raton, FL, pp. 177–206. Rudin-Brown, C.M., Noy, Y.I., 2002. Investigation of behavioural adaptation to lane departure warnings. Transp. Res. Rec. 1803, 30–37. Rudin-Brown, C.M., Parker, H.A., 2004. Behavioural adaptation to adapative cruise control (ACC): implications for preventive strategies. Trans. Res., Part F7 (2), 59–276. Rudin-Brown, C.M., Jenkins, R.W., Whitehead, T., Burns, P.C., 2009a. Could ESC (Electronic Stability Control) change the way we drive? Traffic Inj. Prev. 10 (4),

user’s perspective. Drugs Educ. Prev. Policy 12, 61–70. Decina, L.E., Thomas, L., Srinivasan, R., Staplin, L., 2007. Automated Enforcement: a Compendium of Worldwide Evaluations of Results (No. DOT HS 810 763). National Highway Traffic Safety Administration, Washington, DC. Delaney, A., Diamantopoulou, K., Cameron, M., 2006. Strategic Principles of Drinkdriving Enforcement (No. 249). Monash University Accident Research Centre, Melbourne. Denton, G.G., 1973. The Influence of Visual Pattern on Perceived Speed at Newbridge M8, Midlothian. Rep. No. LR534. Transport and Road Research Laboratory, Crowthorne, England. Drews, F.A., Yazdani, H., Godfrey, C.N., Cooper, J.M., Strayer, D.L., 2009. Text messaging during simulated driving. Hum. Factors 51, 762–770. Edquist, J.E., Rudin-Brown, C.M., Lenné, M.G., 2012. The effects of on-street parking and road environment visual complexity on travel speed and reaction time. Accid. Anal. Prev. 45, 759–765. Elvik, R., 1998. Effects on accidents of automatic speed enforcement in Norway. Transp. Res. Rec. 1595, 14–19. Elvik, R., 2002. To what extent can theory account for the findings of road safety evaluation studies? In: Proceedings of the 15th ICTCT Workshop on Speed Management and Implementation. Brno, Czech Republic, 24-25 October. Elvik, R., Vaa, T., 2004. The Handbook of Road Safety Measures. Engström, J., Johansson, E., Ostlund, J., 2005. Effects of visual and cognitive load in real and simulated motorway driving. Transp. Res. Part F Traffic Psychol. Behav. 8 (2), 97–120. Erke, A., 2008. Effects of electronic stability control (ESC) on accidents: a review of empirical evidence. Accid. Anal. Prev. 40 (1), 167–173. Evans, L., 1991. Traffic Safety and the Driver. Van Nostrand Reinhold., New York. Fitzpatrick, K., Carlson, P.J., Wooldridge, M.D., Brewer, M.A., 2000. Design Factors That Affect Driver Speed on Suburban Arterials. Rep. No. FHWA Report No. FHWA/TX001/1769-3. Federal Highway Administration, Washington, D.C. Forkenbrock, G.J., Flick, M., Garrott, M.R., 1999. NHTSA Light Vehicle Antilock Brake System Research Program Task 4: a Test Track Study of Light Vehicle ABS Performance Over a Broad Range of Surfaces and Maneuvers (No. NHTSA Final Report No. HS-808 875). National Highway Traffic Safety Administration, Washington DC. Fuller, R., 2011. Driver control theory: from task difficulty homeostasis to risk allostasis. In: Porter, B. (Ed.), Handbook of Traffic Psychology. Academic Press, London. Garber, N.J., Miller, J.S., Eslambolchi, S., Khandelwal, R., Mattingly, K.M., Sprinkle, K.M., Wachendorf, P.L., 2005. An Evaluation of Red Light Camera (photo-red) Enforcement Programs in Virginia: a Report in Response to a Request by Virginia's secretary of Transportation (No. VTRC 05-R21). Virginia Transportation Research Council, Charlottesville, VA. Glad, A., 1988. Phase 2 Driver Education: Effect on the Risk of Accident. Norwegian Center for Research, Department of Transportation, Oslo, Norway. Grant, B.A., Smiley, A., 1993. Driver response to antilock brakes: a demonstration of behavioural adaptation. In: Proceedings of the Canadian Multidisciplinary Road Safety Conference. Saskatoon, SK, 14-16 June. Green, C.A., Deering, R.K., 2006. Driver Performance Research Regarding Systems for Use While Backing. Paper No. 2006-01-1982. Society of Automotive Engineers., Warrendale, PA. Grotenhermen, F., Leson, G., Berghaus, G., Drummer, O.H., Krüger, H.P., Longo, M., Moskowitz, H., Perrine, B., Ramaekers, J.G., Smiley, A., Tunbridge, R., 2007. Developing limits for driving under cannabis. Addiction 1–8. Harbluk, J.L., Noy, Y.I., Trbovich, P.L., Eizenman, M., 2007. An on-road assessment of cognitive distraction: impacts on drivers’ visual behavior and braking performance. Accid. Anal. Prev. 39, 372–379. Hauer, E., 1997. Observational before-after studies in road safety. Pergamon. Copyright 1997 Elsevier Science Ltd. ISBN 0-08-043 053 8, 1st ed. . Hauer, E. Road safety research and practice; problems of coexistence. Accident Analysis and Prevention, Special Issue: From Research to Practice. In press. Hedlund, J., 2000. Risky business: safety regulations, risk compensation, and individual behavior. Inj. Prev. 6, 82–90. Hoedemaeker, M., Brookhuis, K.A., 1998. Behavioural adaptation to driving with an adaptive cruise control (ACC). Transp. Res. Rec. Part F 1, 95–106. Høye, A., 2011. The effects of Electronic Stability Control (ESC) on crashes—an update. Accid. Anal. Prev. 43, 1148–1159. IIHS (Insurance Institute for Highway Safety), 2010. Highway Loss Data Institute Bulletin: Texting Laws and Collision Claim Frequencies. Retrieved 14 June 2012, from www. iihs.org/research/topics/pdf/HLDI_Bulletin_27_11.pdf. IIHS (Insurance Institute of Highway Safety), 2014. Status Report, vol. 49 4 | May 29. Jenness, J.W., Jenkins, F., Zador, P., 2011. Motorcycle Conspicuity and the Effect of Fleet DRL: Analysis of Two-Vehicle Fatal Crashes in Canada and the United States 20012007. Washington, DC: NHTSA Report No. DOT HS 811 505. Jonah, B., 1997. Sensation seeking and risky driving: a review and synthesis of the literature. Accid. Anal. Prev. 29 (5), 651–665. Jonah, B., Thiessen, R., Au-Yeung, E., 2001. Sensation seeking, risky driving and behavioral adaptation. Accid. Anal. Prev. 33 (5), 679–684. Jones, B., 1993. The Effectiveness of Skid-car Training for Teenage Novice Drivers in Oregon. Oregon Department of Motor Vehicles, Salem, Oregon. Kahane, C.J., Dang, J.N., 2009. The long-term effect of ABS in passenger cars and LTVs. National Highway Traffic Safety Administration - Report No. DOT HS 811 182. Washington, D.C. Kallberg, V.P., 1993. Reflector posts - signs of danger? Transp. Res. Rec. 1403, 57–66. Keall, M.D., Frith, W.J., 1997. Drink driving behavior and its strategic implications in New Zealand. In: Proceedings of the 14th International Conference on Alcohol, Drugs, and Traffic Safety. Annecy, France, 401-408.

13

Accident Analysis and Prevention 135 (2020) 105370

A. Smiley and C. Rudin-Brown

road traffic accidents: state of the art, IET Intell. Transp. Syst. 1 (2), 81–88. Vadeby, A., Wiklund, M., Forward, S., 2011. Car drivers’ perceptions of electronic stability control (ESC) systems. Accid. Anal. Prev. 43, 706–713. van der Horst, R.A., 2013. Behavioural adaptation to roadway engineering countermeasures. In: Rudin-Brown, C.M., Jamson, S.L. (Eds.), Behavioural Adaptation and Road Safety: Theory, Evidence, and Action. Taylor & Francis (CRC Press), Boca Raton, FL, pp. 113–134 (Chapter 7). Van der Zwan, J.T., 2011. Developing porous asphalt for freeways in the Netherlands: reducing noise. Improving Safety, Increasing Service Life. TR News, Number 272, January-February. Vanlaar, W., Robertson, R., Marcoux, K., 2012. An evaluation of Winnipeg’s photo Enforcement Safety Program: results of time series analyses and an intersection camera experiment. In: Proceedings of the 22nd Canadian Multidisciplinary Road Safety Conference. Banff, Alberta, June 10-13. Voas, R.V., Lacey, J.C., 2011. Alcohol and Highway Safety 2006: A Review of the State of Knowledge. Available from www.nhtsa.gov/staticfiles/nti/pdf/811374.pdf. Wilde, G.J.S., 1982. The theory of risk homeostasis: implications for safety and health. Risk Anal. 2, 209–225. Williams, A.F., O’Neill, B., 1974. On-the-road driving records of licensed race drivers. Accid. Anal. Prev. 6, 263–270. Williams, A.F., Wells, J.K., 1994. Driver experience with antilock brake systems. Accid. Anal. Prev. 26, 807–811. WHO (World Health Organization), 2008. Speed management: a road safety manual for decision-makers and practitioners. Chapter 3: What Are the Tools for Managing Speed? pp. 47–90. Available at: https://www.who.int/roadsafety/projects/manuals/ speed_manual/en/. Zegeer Ch, V., en Deen, R.C., 1978. Green-extension systems at high-speed intersections. ITE J. (November), 19–24.

340–347. Rudin-Brown, C.M., Young, K., Lenné, M., 2009b. Behavioural adaptation to mobile phone legislation: could there be unintended consequences of partial bans. In: Proceedings of the First International Conference on Driver Distraction and Inattention. Gothenburg, Sweden, 28-29 September. Rudin-Brown, C.M., 2010. “Intelligent” in-vehicle ITS: limiting behavioural adaptation through adaptive design. IET Intell. Transp. Syst. 4 (4), 252–261. Sagberg, F., Fosser, S., Saetermo, I.F., 1997. An investigation of behavioural adaptation to airbags and antilock brakes among taxi drivers. Accid. Anal. Prev. 29, 293–302. Scully, J., Newstead, S., 2008. Evaluation of electronic stability control effectiveness in Australasia. Accid. Anal. Prev. 40, 2050–2057. Siegrist, S., Ramseier, E., 1992. Evaluation of Advanced Driving Courses. BFU, Bern, Switzerland. Smiley, A., 1999. Marijuana: on-road and driving simulator studies. In: Kalant, J., Corrigall, W., Hall, W., Smart, R. (Eds.), Chapter 5 in The Health Effects of Cannabis. The Centre for Addiction and Mental Health, Toronto, Canada. Smiley, A., 2000. Behavioural adaptation, safety and ITS. In: Presented at the 79th Annual Meeting of the Transportation Research Board. Washington, D.C. January. Sternberg, R.J., 2017. Human Intelligence. Online Britannica article available at: https://www.britannica.com/science/human-intelligence-psychology Encyclopedia Britannica. . Summala, H., 1981. Driver/vehicle steering response latencies. Human Factors 23 (6), 683–692. Summala, H., 1988. Risk control is not risk adjustment: the zero-risk theory of driver behavior and its implications. Ergonomics 31, 491–506. Teh, E., Jamson, S., Carsten, O., 2018. Mind the gap: drivers underestimate the impact of the behavior of other traffic on their workload. Appl. Ergon. 67, 125–132. Vaa, T., Penttinen, M., Spyropoulou, I., 2007. Intelligent transport systems and effects on

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