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Appendix B Speed and Crashes: A Controversial Topic and an Elusive Relationship David Shinar Ben-Gurion University of the Negev, Israel I was asked to write this paper on the relationship between speed and safety a few days prior to returning to the United States after a sojourn of a few months in Israel. I was returning to my home, to my car, and to my routine. On the first day back to work I drove on Washington's crash-prone I-495 beltway and had the comfortable feeling of slipping back into a familiar routine. Then I noticed I was driving at nearly 70 mph (113 km/h), way above my routine speed on the beltway. My previous typical speed on that road was 60 mph (97 km/h). I did not feel any more risk than I had felt before. In fact the feeling was one of "sameness." What I think had changed in the interim was the perceived enforced speed limit [raised from 55 to 65 221

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MANAGING SPEED 222 mph (89 to 105 km/h)--not to be confused with the posted speed limit that has remained 55 mph] and the traffic-flow speed. What governed my chosen speed--the perceived enforced speed limit? My speed correlated with it. The prevailing speed of traffic? It correlated with that too. My risk-homeostasis level? I felt as safe as ever. My need to conform? Perhaps, since I certainly moved with the herd. Once I realized what my speed was, my initial reaction was to ease off the gas. But I overcame this tendency (with ease) and decided to continue "with the traffic." Was I taking a greater risk by traveling at a higher speed? My recollection of the aggregate research in this area supported such a conclusion. But I did not feel that way. And, per- haps most important, I did not slow down. My behavior was consis- tent with my feeling of safety. My own response and probably that of most drivers is to balance safety, pleasure, and mobility. This review focuses on safety. However, statistically significant safety benefits are not always of practical sig- nificance. What makes a statistically significant effect practically significant is its magnitude relative to the societal value of mobility and the value of the pleasure the individual derives from driving. Speeding is logically related to mobility and subjectively related, for many people at least, to pleasure. Although these issues are outside the scope of this review, they are relevant to the implications of any empirical relationship between speeding and safety. This paper is therefore as much an attempt to synthesize the infor- mation on the relationship between speed and safety as an attempt to understand my own behavior--in the belief that it reflects that of many other motorists. BACKGROUND The relationship between speed and crashes is axiomatic for many people in the traffic safety community. That axiom is encapsulated in the slogan "speed kills." Speed is also listed as one of the manifesta- tions of "aggressive driving" by the National Highway Traffic Safety Administration (NHTSA) (Martinez 1997) and safety interest groups such as Advocates of Highway and Auto Safety (Snyder 1997). Grass roots movements specifically targeting speeding are

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223 Speed and Crashes emerging (e.g., Citizens Against Speed and Aggressive Driving) (Shiekh 1997). Yet recent drops in U.S. traffic fatalities despite repeal of the National Maximum Speed Limit (NMSL) serve to raise pub- lic doubts on the relevance of speed to crashes, as reflected in the national media with front page captions like "Fewer dying despite faster speed limits" (USA TODAY 1997). In the following analysis this axiom is questioned, and the causal relationship between speeding and crashes is evaluated. In referring to speed as the predictor variable and crashes as the predicted vari- able, it is assumed that speed is the independent variable of interest and that safety is the dependent variable of interest. Optimally, to demonstrate that speed is the independent variable behind changes in crashes, it should be under the experimenter's control (and it rarely is). For crashes to be a true dependent variable, a causal rela- tionship has to exist, and it can never be unequivocally justified. Finally, both variables are multidimensional and need to be specifi- cally defined. DEFINITIONS: SPEED, SAFETY, AND INTERVENING VARIABLES Safety is typically defined in terms of crashes or crash rates. At least two aspects of crashes should be considered as separate dependent measures of the effects of speed: crash probability (or incidence) and crash severity (given crash occurrence). In studying the effect of speed in particular, these two measures may not be highly correlated, since speed-related crashes are more commonly associated with severe injuries and fatalities and less with mild injuries and property damage. In contrast, the relationship between crash probability and speed is more complex, and speed-related crashes are not necessarily associated with high speeds. Speed is not a singular concept in this context. First there is a need to distinguish between speed limits (prescribed speed) and travel speeds (drivers' speed). The two overlap only in the presence of at least one of the following: intense enforcement, environmental con- straints (e.g., speed humps, reduced lane width, reduced visibility), or vehicle limitations (e.g., old cars) that force drivers to drive at or

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MANAGING SPEED 224 below the speed limit. Second, whereas speed limit is a single value, driving speed can be the speed of a single crash-involved vehicle or a statistic of the prevailing traffic speed distribution. Three such statis- tics are most often used in the context of safety: average travel speed, 85th percentile of the speed distribution, and some measure of the dispersion in travel speeds. Speed dispersion, in turn, can be quanti- fied by the speed variance (the squared deviations from the mean of the speed distribution), the speed standard deviation (the square root of the variance), and sometimes by the speed range, such as the dif- ferential between the 15th and 85th percentile speed (which corre- sponds to approximately two standard deviations). All the studies reviewed in this paper use some estimate of speed. In addition to the difficulty of integrating results obtained with the various speed measures mentioned above, there is a problem with the validity of the speed measures themselves. It is nearly impossible to obtain an objective measure of the true precrash speeds of crash- involved vehicles. This is because crashes are not planned, and con- sequently speeds must be estimated post hoc by various subjective and objective techniques, all having a limited validity. Only one study was found in which actual traffic crashes were videotaped and speed was calculated from the video frame analysis. In this study by Pasanen and Salmivaara (1993), a video camera specifically cali- brated to measure speed recorded 18 intersection collisions in Helsinki, 11 of which involved pedestrians. In another study (West and Dunn 1971), precrash speeds for approximately one-fourth of the crash-involved vehicles were determined with a high degree of certainty from data obtained from speed detectors embedded in a section of an Indiana rural state highway with a 55-mph (89-km/h) speed limit. All other studies relied on drivers' estimates, police offi- cers' estimates, or crash deformation data for calculating the speed of crash-involved vehicles. The few data that exist suggest that the rela- tionship among the different speed measures is moderate at best (F.A. Haight, unpublished data, 1994). For studies relating traffic- flow speeds to crashes, the actual speed of the traffic stream at the place and time of the crash is usually not known; instead it is extrap- olated from traffic-flow measures taken before or after the crash (e.g., Solomon 1964).

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225 Speed and Crashes The reason drivers drive at different speeds probably affects the relationship between speed and crashes. Driving slowly in congested urban traffic is associated with many fender benders and very few severe crashes, whereas driving fast on expressways is associated with very few fender benders and a small but significant number of severe crashes. On the basis of these two situations, if all crashes are counted, it appears that speed is inversely related to crashes. However, if only severe crashes are examined, the relationship between speed and crashes is direct. In addition, driving slowly in congestion is done for a different reason than driving slowly on an open freeway. The difference may stem from the situation or from individual differences among drivers (a slow driver on a freeway may be a cognitively impaired elderly driver, whereas a slow driver on a congested urban street may be a highly capable driver hampered by traffic). For example, Solomon (1964) found that drivers at precrash speeds significantly above or below the average traffic speed have a greater likelihood of overinvolvement in crashes than do those driv- ing just slightly above the average (Solomon 1964). But that rela- tionship changed when turning vehicles were removed from the total driving population (Fildes and Lee 1993). In aggregating crash data from different roads and different times to evaluate the effect of speed as a single independent variable, one assumes (at least implicitly) that "all other things remain equal." This is never the case. In real life, driving speed is highly correlated with at least the following (Bowie and Walz 1994): 1.Other crash-related driver behaviors such as drinking, not using safety belts (Evans 1991), and other types of aggressive driving (in fact, speeding is often considered as a subcategory of aggressive driv- ing); 2.Crash-related individual differences in variables such as age and sex; 3.Road design (e.g., speed-related crashes are overrepresented on curves) and road conditions, traffic conditions, and speed limits; and 4.Vehicular variables such as type of vehicle, engine power, and steering and brake performance.

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MANAGING SPEED 226 All of these factors complicate the interpretation of data. In the absence of complete data to evaluate the joint contribution of all vari- ables, the conclusions remain qualified. Finally, when speed data are not available, a speed management technique is often used to assess the relationship between speed and safety. It is then assumed that speed covaries with the speed assumed by the management technique. The most common management technique is the speed limit. Other techniques are speed enforcement and speed calming through traffic engineering (e.g., sequencing traf- fic lights) and roadway design (e.g., road humps, traffic circles, and rumble strips). It then remains to be demonstrated that these tech- niques affect speed. With these caveats in mind, the literature review will be divided into three major parts: (a) the effect of speed management/control techniques on speed and crashes, (b) the effect of speed on crash inci- dence, and (c) the effect of speed on crash severity. Finally, on the basis of the literature review, conclusions concerning speed and crashes will be drawn. SPEED REDUCTION AND SPEED MANAGEMENT TECHNIQUES An extensive review of the relationship between speed management, especially speed limits, and crashes is outside the scope of this paper. However, since these techniques are also used as surrogate measures of travel speed, a brief review of this relationship is appropriate. Speed Limits The impact of speed limits on crash risk is addressed in Appendix C. However, studies measuring both speed changes and crash experience in the context of speed limit changes and enforcement are relevant to this paper. Reviews of studies that evaluated changes in crashes and injuries in conjunction with changes (or the introduction) of speed limits have generally supported the notion that increases in speed limits without other concurrent changes are associated with increases in crashes; decreases or setting of speed limits where none existed

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227 Speed and Crashes before are associated with crash and injury reductions (NHTSA 1992; Rock 1995; Summala 1985; TRB 1984). However, all of these studies suffer from the shortcomings of poor control of potentially confounding variables such as changes in traffic patterns as a conse- quence of speed limit changes, spillover effects of crashes to adjacent roads, changes in road service levels, and the concurrent introduction of other safety-related variables including increased enforcement, increased use of safety belts, reduction in drinking and driving, and vehicle-based safety improvements. In the context of the NMSL of 55 mph (89 km/h), a Transportation Research Board (TRB) special report on the enduring effects of this statute concluded, "Nevertheless, as improvements have been made to highways, vehi- cles, and medical services, the risk associated with higher speed driv- ing has been reduced somewhat" (TRB 1984, 70). This means that comparisons across jurisdictions and over time (especially when the higher speed is the more recent) are flawed. The difficulties are so great as to yield opposite conclusions from the same data, depending on the measure of crash involvement used and the factors other than speed limits that are included in the analyses. Such disagreements have led Lave (1985; 1989; Lave and Elias 1994) to argue that, although raised speed limits increased fatalities on the affected rural roads, they have actually contributed to the observed reduction in statewide fatalities, whereas researchers of the Insurance Institute for Highway Safety (Zador and Lund 1991; Lund and Rauch 1992) have argued that the specific effects measured by Lave can be attributed to multiple other factors that have been previously linked to fatality reductions and that raising the speed limit has cost lives (NHTSA 1992). Garber and Gadiraju (1988) suggested that the difference between the design speed and the posted speed limit accounts for differences in driving speeds; widening speed dispersion, in turn, was linked to increases in crash rates. On the Virginia highways they studied, min- imum speed dispersion was obtained when the design speed was 5 to 10 mph (8 to 16 km/h) above the posted limit. This could also explain why lower speed limits sometimes increase the incidence of crashes. Parker's findings (1997) of the inconsistent effects of tempo- rary speed limit changes on short highway sections support this con-

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MANAGING SPEED 228 clusion. Other factors, such as traffic conditions, can also affect speed and speed dispersion. Thus, Vaa (1997) found that compliance with enforced speed limits is less during peak periods than during off-peak periods. Also, when speed limits are lowered, they are typically accompanied by increased enforcement and public information cam- paigns (e.g., Nilsson 1990). Parker's analysis is relevant here because it measures the effects of changes in speed limits on driving speeds and the effects of these changes on crash involvement. Parker found small but statistically significant effects of speed limit changes on travel speed. Whereas the speed limits were changed by as much as 20 mph (32 km/h), the changes in travel speed (using either the means or percentile levels) were generally less than 2 mph (3 km/h) and were unrelated to the change in the speed limit. Also, the maximum speed limit never exceeded the 55-mph (89-km/h) NMSL that was in effect at the time of the speed limit manipulation (1985 to 1992). Finally, the relationship between speed limit and crashes in Parker's study was ambiguous. Comparisons between crashes at sites where the speed limit was changed and crashes at the control sites showed a slight increase in crashes with increases in speed limits, whereas the before- after comparisons yielded a significant decrease in crashes with increases in speed limits. Parker's study had an acknowledged major shortcoming in the site selection. The sites selected for the speed limit changes were chosen by local agencies on the basis of a prede- termined need (e.g., request from the public, high incidence of crashes, compliance with local ordinances, changing land use pat- terns) rather than randomly. Thus it is likely that in many cases the changes actually reflected existing travel speeds. Given this severe constraint, the small number of sites, and short follow-up, Parker (1997) qualified his conclusions by stating that "the findings may apply to similar sites where the speed limits are changed for similar reasons. Generalizations to other roadways are not appropriate" (p. 85). Under some circumstances, changes to higher speed limits may have a greater and more consistent effect. Photo-radar surveys con- ducted by the Insurance Institute for Highway Safety revealed that the percentage of drivers exceeding 70 mph (113 km/h) increased

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229 Speed and Crashes significantly when speed limits were raised to 65 mph (105 km/h) in California and 70 mph in Texas (Retting and Greene 1997). If speeds in excess of 70 mph are well beyond the average traffic speed on these roads (admittedly an arguable assumption), then--either because of their high speed or because of their contribution to widening the range of traffic speeds--speeding drivers are at a greater crash risk. Their increased risk is consistent with both the "speed" model and the "variance" model because the more the driver exceeds the average traffic speed, the greater the range in traffic speeds and the further the driver's position from the minimum point of the U-shaped crash involvement curve. The issue of the role of speed dispersion is further complicated by the ambiguity of the term. Although the statistical definition of vari- ance as a measure of dispersion is clear, the term is often misused. Different researchers have used different statistics to represent speed dispersion. Traffic engineers typically measure speed dispersion from the speeds of free-flowing vehicles over a short period. When mea- surements of speed dispersion are based on long durations of expo- sure and many of the vehicles are not free-flowing, it is not clear what the measure reflects. For example, an exposure period that covers both peak- and non-peak-period traffic can yield a wide range of traffic speeds, whereas in a short interval the range of traffic speeds may be narrower. An intervening variable that may affect both compliance and crash involvement is the "perceived reasonableness" of the speed limit. McCoy et al. (1993) studied road sections in Nebraska and found that sites with "reasonable" speed limits were safer than those with limits 5 to 10 mph (8 to 16 km/h) below the "reasonable" levels. To ensure a good correspondence between this measure and speed choice, a recent evaluation of the relationship among safety, speed, and speed management conducted for Transport Canada suggests that the traditional rule of thumb for determination of speed limits-- to use the 85th percentile for existing roads and the design speed for new roads--is still a good one (Knowles et al. 1997). Because actual speed limits are often dictated by other considerations, and given the lack of control of these variables in most studies, the researchers con- cluded that "changing the posted speed limit does not automatically

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MANAGING SPEED 230 mean that speeds and crashes will be affected by the change and that it is not clear under what conditions changing the speed limit is likely to lead to a change in safety" (p. 2-9). Speed Enforcement Speed enforcement is probably the most common mediator between speed limit and speed choice. There is ample evidence that drivers respond to perceived enforcement by adjusting their behavior, most notably by reducing their speed (Shinar and McKnight 1985). The effect of enforcement is typically maximal at the site of the perceived enforcement, but halo effects relating to both time and place have been demonstrated. Holland and Conner (1996) obtained a time- halo effect lasting up to 9 weeks for speed enforcement coupled with signs stating "Police Speed Check Area," and Vaa (1997) demon- strated that massive enforcement, with a daily average of police pres- ence of 9 h, yielded speed reductions that lasted up to 8 weeks. This was done in a semirural area with a road section having speed limits of 37 and 50 mph (60 and 80 km/h). Interestingly, speed reductions varied by time of day, and morning peak-period speeders were the most resistant to change. This could have been due to pressure to get to work on time or the drivers' knowledge that enforcement is more difficult (and therefore perceived as less threatening) in high-density, peak-period traffic. Shinar and Stiebel (1986) showed that compli- ance was highest near police vehicles and diminished with increasing distance. The distance-halo effect was greater for a moving than a stationary police vehicle, presumably because the moving vehicle could be perceived as more threatening even when it was already out of sight. The link between enforcement and crash reduction was evaluated by Elvik (1997), who conducted a meta-analysis of studies that eval- uated automated speed enforcement in several countries including England, Germany, Sweden, Norway, Australia, and the Netherlands. He concluded that, overall, automated enforcement yielded a 17 percent reduction in injury crashes (16 to 19 percent at a confidence level of 95 percent). The difference in effectiveness at different locations suggests that it is most effective at crash "black

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231 Speed and Crashes spots" (i.e., high-crash locations). Whether the crashes migrate else- where, as has been argued by Lave and Elias (1994), is still an issue. Other Speed Management Techniques Perhaps the most cost-effective approach to speed control in the long run is through road design. This has been demonstrated with speed humps and with changes in design that are made to accommodate pedestrians in urban streets with "traffic integration." This design approach was initiated in the Netherlands (and called "woonerf ") in 1968 and has spread in various forms to Germany, Denmark, England, France, Israel, and Australia (F.A. Haight, unpublished data, 1994). The integration is achieved through making roads nar- row or winding or placing obstacles on the travel portion of the road so that vehicular traffic has to slow down to practically walking speeds [e.g., 9 mph (15 km/h)]. Although the effect on safety has not been the focus of evaluations of these changes, the effect on speed has been consistently reported (F.A. Haight, unpublished data, 1994). SPEED AND CRASH INVOLVEMENT From a very simplistic point of view it appears that as speed increases, the time to react to emerging dangers is shortened, and the likelihood of successfully coping with the imminent crash situation decreases. Also, even after a driver reacts by braking, the braking distance of the vehicle is proportional to the square of the prebraking speed. Therefore the distance traveled to a complete stop increases with speed, and the likelihood of a collision increases in a corresponding fashion. But reality is much more complicated, both theoretically and empirically. In this section an attempt is made to consider the theo- retical issues involved and the empirical data that support and refute the relationship between speed and crash probability. Some Theoretical Issues--and a Theoretical Quagmire There are at least three theoretical approaches to relate speed to crashes, each leading to a different conclusion. Each approach views

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Figure B-7 Injury (above) and property damage (below) rates by travel speed, day and night (Solomon 1964). 1 mph = 1.609 km/h.

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267 Speed and Crashes 18 to 25 percent of all human errors in property damage crashes, 20 to 29 percent of all human errors in personal injury crashes, and 24 to 40 percent of all human errors in fatal crashes. The most compelling demonstration of the combined effects of crash probability and crash cost as they relate to speed was recently provided in an analysis of crash data from 16 European countries. In that analysis Kallberg (unpublished data, 1997) demonstrated that disregarding the effects of speed on crash severity leads to serious underestimation of the effects of speed on crash costs. On the basis of a Swedish model of the relationship between crash probability and crash cost as a function of precrash speed, Kallberg's more conserva- tive estimate is that an "increase from 47 to 50 km/h increases acci- dent costs (and the number of injury accidents) by 13.2 percent, speed increases from 80 to 85 km/h by 12.9 percent, and speed increases from 90 to 100 km/h by 23.5 percent, and the effect is the same in all countries" (p. 9). In summary, these findings indicate that because speeding is a more prominent factor in more severe crashes and because severity increases as a power function of speed, it is difficult to sustain a view that excessive speed--at least relative to the median of the prevailing traffic--is not a crash risk factor with significant societal costs in terms of injuries and fatalities as well as money. CONCLUDING REMARKS There is sufficient evidence to indicate that a driver's absolute speed is a correlate of crash involvement. The indications for the positive relationship between speed and crashes are derived from empirical data of single-vehicle crashes, causal crash analysis, and theoretical frameworks related to the effects of speed on information overload and reduced vehicle-handling capacity. In addition, empirical data show unequivocally that injuries and fatality rates increase as a power function of impact speed or Delta-V. There is also ample evidence to indicate the contribution of wide disparities in speed of the traffic stream, as well as the deviation of

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MANAGING SPEED 268 crash-involved vehicles from the average traffic speed, to crash involvement. However, the support for these findings comes from correlational studies, and the argument for causality rests on the the- oretical support for this finding. The theoretical support is not suffi- cient. To suggest that speed disparities in the traffic stream contribute to potential intervehicle conflicts is not sufficient, since such conflicts appear to constitute a small portion of crashes in gen- eral and an even smaller portion of the more severe crashes. So what is the source of that relationship? Liu and Popoff (1996) suggest that the "variance effect" reflects a greater vehicle mix or a greater mix of drivers with different styles and capabilities. If that is the case, the variance effect may be a spurious finding. As long as data on driver and vehicle types (both in crash samples and traffic samples) are unavailable, this remains an interesting speculation. In a situation in which speed selection is totally at the driver's dis- cretion, the range of speeds in the traffic stream is a function of the risk levels that different drivers are willing to tolerate, different perceptions of a "safe speed" that drivers have for a given risk level, and the han- dling capabilities of different cars and drivers. All of the studies report- ing narrowing of speed disparities with increasing speed were conducted in the presence of speed limits, and, consequently, a thresh- old level of speed may have been responsible for the reduction in speed dispersion (i.e., higher speeds were due to higher speed limits on roads with higher design speeds). This is because as the speed limit is raised, fewer and fewer drivers are likely to exceed it by much, and more and more drivers tend to drive close to the limit. This also means that it is highly probable that if speed limits were strictly controlled in low- speed zones, then drivers who would otherwise exceed the limit signif- icantly (and therefore contribute to widening speed dispersion) would refrain from doing so, and both speed dispersion and crash risk would be reduced. The tendency of speed differences to narrow as average speed increases probably reflects drivers' tendencies to violate low speed limits (e.g., near schools) more than high speed limits [e.g., on Interstate highways with limits of 65 to 70 mph (105 to 113 km/h)]. The critical issue then is how speed limits are set. If they are realistic (e.g., 85th percentile or design speed), speed dispersion may be low,

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269 Speed and Crashes constant, and independent of average speed. Since the speed limit, the design speed, and prevailing conditions all contribute to speed choice, separating average speed from speed limits and enforcement is artificial. If speed limits are set at low levels and they are enforced, then speed dispersion would probably not decrease with increasing speed but rather would increase with it, in a manner similar to most measures of human psychomotor behaviors (where variance is posi- tively correlated with the mean). Then what would the relationship between speed dispersion and crashes be? That is an open question. Perhaps what is needed is systematic research into the relationships between measures of speed and speed dispersion under conditions of speed control. The importance of theory to the role of speed dispersion can be illustrated with older drivers. Older drivers are a good group to pick because the elderly are the fastest-growing age group in the popula- tion in general and on the roads in particular (Eberhard 1996). Now, if slow driving (rather than slowing down) increases crash risk, should slow drivers be advised to increase their speed? Older drivers, who tend to drive slow, do so to maintain or reduce their risk level, not to increase it. Given their slowed information processing capa- bilities, it would be foolhardy to recommend that these people drive faster so as to reduce speed disparities in the traffic stream. Also, removing them from high-speed roads may actually be detrimental to safety since (a) they already restrict their driving to safer roads and times and (b) their crash involvement may actually increase on other roads with lower design speeds (placing greater information process- ing demands on the driver) that are already associated with higher crash risks. Given the multiple factors that coexist in the real driving environment, it is interesting to speculate if it is even possible to find or create a situation in which only speed changes. The answer is probably not. Even in a simulator study, if all that is changed is the driver's speed while the traffic speed and likelihood of emergencies stay the same, then crashes will most likely increase--but so will speed differences. If the speed of all the traffic is changed and an emergency arises, then multiple-vehicle chain crashes are likely. Chain crashes on highways with restricted view (e.g., in fog)

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MANAGING SPEED 270 are suggestive of the process: the faster a vehicle travels, the greater the probability of a crash--but only in the event of an obstacle ahead. However, this situation is very artificial, because drivers adjust their speed in accordance with their expectations of obstacles ahead. Thus, it is hard to think of a realistic situation, even in a sim- ulator study, that would disaggregate the effects of the speed of a crash-involved vehicle from the disparities in speed of the traffic. In summary, the ultimate question is not whether increasing speed increases crash probability and crash severity. Instead, there are three questions: What are the mediating factors involved? What are acceptable societal costs for increased mobility? Who should decide the levels of acceptability--elected officials, safety experts, or the motoring public through their opinion or behavior (such as the 85th percentile speed)? With respect to mediating factors, it is impossible to hold all "other things equal" while varying speed. This is because the basis for speed choice--roadway design, traffic controls, enforcement, traffic flow, and perceived risk and comfort levels--all affect the relation- ship between speed and crash probability. With respect to the accept- able risk level, there is willingness at both the individual and the societal levels to accept some degree of risk to improve mobility. Thus, speed management, speed choice, crash risk, and crash severity are all intertwined and linked to the value placed on mobility. CONCLUSIONS 1. There is ample, but not unequivocal, evidence to indicate that, on a given road, crash involvement rates of individual vehicles rise with their speed of travel. 2. There are no convincing data to demonstrate that, across all roads, crash involvement rates rise with the average speed of traffic (i.e., that roads with higher average traffic speeds have higher crash rates than roads with lower average traffic speeds). This is probably because the average traffic speed is highly correlated with the design speed of different road classes (and other conditions). 3. The absolute speed deviation of crash-involved vehicles from the average traffic speed appears to be positively related to crash

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271 Speed and Crashes probability, especially for rural arterial highways and Interstate high- ways. There are insufficient data to demonstrate such a relationship for rural collector roads and urban streets. 4. The principal factor that accounts for the effects of speed devi- ation is the requirement to slow down to make turns and to enter and exit high-speed roads. Still, even when the effects of turning vehicles are removed from the data, some effects of speed deviation, especially at the extreme ends, remain. 5. The disparities in speed of the traffic stream may be positively related to crash probability, especially on Interstate highways. However, the data are not very consistent, and more data are needed. 6. On urban streets there appears to be a strong relationship between crash rates and the absolute speed of crash-involved vehi- cles. However, this conclusion is based mainly on small data sets from non-U.S. studies. 7. The data demonstrating the relevance of speed dispersion in the traffic stream and speed deviations of crash-involved vehicles are based on correlational effects and therefore cannot be used to indi- cate that if slow-moving drivers were to increase their speed, their crash probability would be reduced. 8. There are unequivocal data to indicate that the risk of injuries and fatalities increases as a function of precrash speed or Delta-V. This is true for all road types. 9. The overall cost of speed-related crashes is much greater than the relationship between speed and crash probability indicates. This is because high-speed crashes are associated with greater injury lev- els than are low-speed crashes. ACKNOWLEDGMENTS In preparing this report I was greatly assisted by the members of the TRB Committee for Guidance on Setting and Enforcing Speed Limits. All of them provided me with insightful comments and prob- ing questions on a previous draft. I have tried to be responsive to all the issues raised, and I tried to incorporate all the recommended changes and additions. The responsibility for shortcomings and lim- itations that remain is solely my own.

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MANAGING SPEED 272 I was especially assisted by the subcommittee chairperson, Forrest Council, who provided the first impetus and guidelines for this report, and by Nancy Humphrey, the TRB program manager, who provided me with most of the needed source material, acted as an intermediary between me and the committee members, spent hours on the phone with me discussing key issues, and made excellent edi- torial comments on the previous drafts. Finally, I would like to acknowledge the help of Richard Compton, who acted as a challeng- ing sounding board and spent as much time as I needed to discuss the principal speed issues. REFERENCES ABBREVIATIONS NASS National Analysis Sampling System NHTSA National Highway Traffic Safety Administration TRB Transportation Research Board Aronoff, C.J. 1971. Stannard Baker, Traffic Safety Pioneer, Retires from NU Traffic Institute. Northwestern University News, Aug. 27. Bowie, N.N., and M. Walz. 1994. Data Analysis of the Speed-Related Crash Issue. Auto and Traffic Safety, Vol. 1, No. 2, Winter, NHTSA, U.S. Department of Transportation, pp. 3138. Brown, D.B., S. Maghsoodloo, and M.E. McArdle. 1990. The Safety Impact of the 65 mph Speed Limit: A Case Study Using Alabama Accident Records. Journal of Safety Research, Vol. 21, pp. 125139. Cited by Rock (1995). Cirillo, J.A. 1968. Interstate System Accident Research: Study II, Interim Report II. Public Roads, Vol. 35, Aug. pp. 7175. Cowley, J.E. 1987. The Relationship Between Speed and Accidents: A Literature Review. J.E. Cowley and Associates, Melborne, Australia, March. Eberhard, J.W. 1996. Safe Mobility for Senior Citizens. IATSS Research, Vol. 20, pp. 2937. Elvik, R. 1997. Effects on Accidents of Automatic Speed Enforcement in Norway. Presented at 76th Annual Meeting of the Transportation Research Board, Washington, D.C. European Transport Safety Council. 1995. Reducing Traffic Injuries Resulting from Excess and Inappropriate Speed. Brussels, Belgium. Evans, L. 1991. Traffic Safety and the Driver. Van Nostrand Reinhold, New York. Fildes, B.N., and S.J. Lee. 1993. The Speed Review: Road Environment, Speed Limits, Enforcement, and Crashes. CR 127 (FORS), CR 3/93 (RSB). Road and Traffic Authority of New South Wales, Australia, Sept.

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