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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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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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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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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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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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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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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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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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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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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.
OCR for page 273
273
Speed and Crashes
Fildes, B.N., G. Rumbold, and A. Leening. 1991. Speed Behavior and Drivers' Attitude
to Speeding. Report 16. Monash University Accident Research Center, Monash,
Victoria, Australia, June.
Finch, D.J., P. Kompfner, C.R. Lockwood, and G. Maycock. 1994. Speed, Speed Limits,
and Accidents. Project Report 58, S211G/RB. Transport and Road Research
Laboratory, Crowthorne, Berkshire, England.
Fowles, R., and P.D. Loeb. 1989. Speeding, Coordination, and the 55-mph Limit:
Comment. The American Economic Review, Vol. 79, pp. 916922.
Freedman, M., and A.F. Williams. 1992. Speeds Associated with 55-mph and 65-mph
Speed Limits in Northeastern States. ITE Journal, Vol. 62, No. 2,
pp. 1721.
Garber, N. J., and R. Gadiraju. 1988. Speed Variance and Its Influence on Accidents.
University of Virginia, Charlottesville.
Harkey, D.L., H. D. Robertson, and S.E. Davis. 1990. Assessment of Current Speed
Zoning Criteria. In Transportation Research Record 1281, Transportation
Research Board, National Research Council, Washington, D.C.
Hauer, E. 1971. Accidents, Overtaking, and Speed Control. Accident Analysis and
Prevention, Vol. 3, pp. 113.
Holland, C.A., and M.T. Conner. 1996. Exceeding the Speed Limit: An Evaluation of
the Effectiveness of a Police Intervention. Accident Analysis and Prevention, Vol. 28,
pp. 587597.
Joksch, H.C. 1993. Velocity Change and Fatality Risk in a Crash. Accident Analysis and
Prevention, Vol. 25, pp. 103104.
Kahneman, D. 1973. Attention and Effort. Prentice-Hall, Englewood Cliffs, N.J.
Kallberg, V.P., and J. Luoma. 1996. Speed Kills--Or Does It and Why? Proc.,
Conference of Road Safety in Europe, Birmingham, United Kingdom, Sept. 911,
pp. 129149.
Kloeden, C.N., A.J. McLean, V.M. Moore, and G. Ponte. 1997. Travelling Speed and
the Risk of Crash Involvement. Technical Report. NHMRC Road Accident
Research Unit, The University of Adelaide, Australia.
Knowles, V., B. Persaud, M. Parker, and G. Wilde. 1997. Safety, Speed and Speed
Management: A Canadian Review. Final Report, Contract T8080-5-6858.
Transport Canada, March.
Lave, C. 1985. Speeding, Coordination, and the 55-mph Limit. The American
Economic Review, Vol. 75, pp. 11591164.
Lave, C. 1989. Speeding, Coordination, and the 55-mph Limit: Reply. The American
Economic Review, Vol. 79, pp. 926931.
Lave, C., and P. Elias. 1994. Did the 65 mph Speed Limit Save Lives? Accident Analysis
and Prevention, Vol. 26, pp. 4962.
Levy, D.T., and P. Asch. 1989. Speeding, Coordination, and the 55-mph Speed Limit:
Comment. The American Economic Review, Vol. 79, pp. 913915.
Lindsay, P.H., and D.A. Norman. 1977. Human Information Processing: An Introduction
to Psychology (2nd edition). Academic Press, New York.
Liu, G.X. 1997. Identification and Analysis of Speed Related Accidents on Highways.
Proc., Canadian Multidisciplinary Road Safety Conference X, June 811, Toronto,
Ontario, Canada.
OCR for page 274
MANAGING SPEED
274
Liu, G.X., and A. Popoff. 1996. Provincial Wide Travel Speed and Traffic Safety Study
in Saskatchewan. Faculty of Engineering, University of Regina, Regina,
Saskatchewan, Canada.
Lund, A.K., and W. J. Rauch. 1992. On the Contrary! A Comment on Lave and Elias's
Question, "Did the 65 mph Speed Limit Save Lives?" Insurance Institute for Highway
Safety, Arlington, Va.
Mackay, M. 1985. Seat Belt Use Under Voluntary and Mandatory Conditions and Its
Effects on Casualties. In Human Behavior and Traffic Safety (L. Evans and R.C.
Schwing, eds.), Plenum Press, New York, pp. 259277.
Mackay, M. 1988. Crash Protection for Older Persons. In Special Report 218:
Transportation in an Aging Society, Vol. 2, Transportation Research Board, National
Research Council, Washington D.C., pp. 159193.
Martinez, F. 1997. Statement before the Subcommittee on Surface Transportation,
Committee on Transportation and Infrastructure, U.S. House of Representatives, July 17.
McCarthy, P.S. 1991. Highway Safety and the 65-mph Speed Limit. Contemporary
Policy Issues, Vol. 9, pp. 8292. Cited by Rock (1995).
McCoy, P.T., B.A. Moen, G. Pesti, and M. Mourssavi. 1993. Evaluation of Lower Speed
Limits on Urban Highways. Research Report TRP-02-26-92. University of
Nebraska, Lincoln. Cited by Knowles et al. (1997).
Moore, V.M., J. Dolinis, and A.J. Woodward. 1995. Vehicle Speed and Risk of a Severe
Crash. Epidemiology, Vol. 6, pp. 258262.
Munden, J.W. 1967. The Relation Between a Driver's Speed and His Accident Rate.
Report LR 88. Road Research Laboratory, Crowthorne, Berkshire, England. Cited
by Finch et al. (1994).
Näätänen, R., and H. Summala. 1976. Road User Behavior and Traffic Accidents. North-
Holland, New York.
NASS. 1998. Data from the 1995 Crashworthiness Data System. National Highway
Traffic Safety Administration, U.S. Department of Transportation.
NHTSA. 1992. Report to Congress on the Effects of the 65 mph Speed Limit Through
1990. U.S. Department of Transportation.
NHTSA. 1997. Traffic Safety Facts 1996. DOT HS 808 649. U.S. Department of
Transportation.
Nilsson, G. 1990. Reduction in the Speed Limit from 110 km/h to 90 km/h During Summer
1989: Effects on Personal Injury Accidents, Injured, and Speeds. VTI Report 358A.
Swedish Road and Traffic Research Institute, Sweden. Cited by Knowles et al. (1997).
O'Day, J., and J. Flora. 1982. Alternative Measures of Restraint System Effectiveness:
Interaction with Crash Severity Factors. SAE Technical Paper Series No. 820798.
Warrendale, Pa.
O'Donnell, C.J., and D.H. Connor. 1996. Predicting the Severity of Motor Vehicle
Accident Injuries Using Models of Ordered Multiple Choice. Accident Analysis and
Prevention, Vol. 28, pp. 739753.
Parker, M.R., Jr. 1997. Effects of Lowering and Raising Speed Limits on Selected Roadway
Sections. Report FHWA-RD-92-084. Federal Highway Administration, U.S.
Department of Transportation.
Pasanen, E., and H. Salmivaara. 1993. Driving Speeds and Pedestrian Safety in the
City of Helsinki. Traffic Engineering and Control, Vol. 34, No. 6, pp. 308310.
OCR for page 275
275
Speed and Crashes
Retting, R.A., and M.A. Greene. 1997. Traffic Speeds Following Repeal of the
National Maximum Speed Limit. ITE Journal, May, pp. 4246.
Rock, S.M. 1995. Impact of the 65 mph Speed Limit on Accidents, Deaths, and
Injuries in Illinois. Accident Analysis and Prevention, Vol. 27, pp. 207214.
Rodriguez, R.J. 1990. Speed, Speed Dispersion, and the Highway Fatality Rate.
Southern Economic Journal. Oct., pp. 349356.
Rumar, K., U. Bergrund, P. Jernberg, and U. Ytterbom. 1976. Driver Reaction to a
Technical Safety Measure--Studded Tires. Human Factors, Vol. 18, pp. 443454.
Sabey, B.E., and G.C. Staughton. 1975. Interacting Role of Road Environment,
Vehicle, and Road User in Accidents. Presented at the 5th International
Association for Accident and Traffic Medicine, London, Sept. 1975.
Shiekh, L. 1997. Statement before the Subcommittee on Surface Transportation,
Committee on Transportation and Infrastructure, U.S. House of Representatives,
July 17.
Shinar, D. 1978. Psychology on the Road:The Human Factor in Traffic Safety. Wiley, New York.
Shinar, D., and A. Drory. 1983. Sign Registration in Daytime and Nighttime Driving.
Human Factors, Vol. 25, pp. 117122.
Shinar, D. and A.J. McKnight. 1985. The Effects of Enforcement and Public
Information on Compliance. In Human Behavior and Traffic Safety (L. Evans and
R.C. Schwing, eds.), General Motors Research Laboratories, Plenum Press, New
York, pp. 385419.
Shinar, D., and E. Schechtman. 1998. Health and Traffic Safety Behaviors in the U.S. in
the Years 19851995. Final Report. National Highway Traffic Safety
Adminstration, U.S. Department of Transportation.
Shinar, D., and J. Stiebel. 1986. The Effectiveness of Stationary Versus Moving Police
Vehicle on Compliance with Speed Limit. Human Factors, Vol. 28, pp. 365371.
Snyder, D. 1989. Speeding, Coordination, and the 55-mph Limit. The American
Economic Review, Vol. 79, pp. 922925.
Snyder, D. 1997. Statement before the Subcommittee on Surface Transportation,
Committee on Transportation and Infrastructure, U.S. House of Representatives,
July 17.
Solomon, D. 1964. Accidents on Main Rural Highways Related to Speed, Driver, and
Vehicle. Bureau of Public Roads, U.S. Department of Commerce, July.
Stuster, J., and Z. Coffman. 1997. Synthesis of Safety Research Related to Speed and Speed
Management. Draft report. Anacapa Sciences, Inc., Santa Barbara, Calif.
Summala, H. 1985. Modeling Driver Behaviour: A Pessimistic Prediction? In Human
Behavior and Traffic Safety (L. Evans and R.C. Schwing, eds.), General Motors
Research Laboratories, Plenum Press, New York, pp. 4365.
Taieb, M., and D. Shinar. 1996. Maintaining Headway While Driving: Reality vs.
Perception. Paper presented at International Conference of Traffic and Transport
Psychology, May, Valencia, Spain.
Taylor, D.H. 1964. Drivers' Galvanic Skin Response and the Risk of Accidents.
Ergonomics, Vol. 7, pp. 439451.
TRB. 1984. Special Report 204: 55: A Decade of Experience. National Research Council,
Washington, D.C.
OCR for page 276
MANAGING SPEED
276
Treat, J.R., N.S. Tumbas, S.T. McDonald, D. Shinar, R.D. Hume, R.E. Mayer, R.L.
Stansifer, and N.J. Castellan. 1977. Tri-Level Study of the Causes of Traffic Accidents.
Volume I: Causal Factor Tabulations and Assessment. DOT-HS-805-085. NHTSA,
U.S. Department of Transportation.
USA TODAY. 1997. Fewer Dying Despite Faster Speed Limits. July 14, p. 1A.
Vaa, T. 1997. Increased Police Enforcement Effects on Speed. Accident Analysis and
Prevention, Vol. 29, pp. 373385.
Viano, D.C., and S. Ridella. 1996. Crash Causation: A Case Study of Fatal Accident
Circumstances and Configurations. No. 960458. Society of Automotive Engineers,
International Congress and Exposition, Detroit, Mich., Feb. 2629, pp. 123134.
West, L.B., Jr., and J.W. Dunn. 1971. Accidents, Speed Deviation and Speed Limits.
Traffic Engineering,Vol. 41, No. 10, pp. 5255.
Wilde, G.J.S., S. P. Claxton-Oldfield, and P.H. Platenius. 1985. Risk Homeostasis in
an Experimental Context. In Human Behavior and Traffic Safety (L. Evans and
R.C. Schwing, eds.), Plenum Press, New York, pp. 119149.
Zador, P.L. and A.K. Lund. 1991. Comments on "Did the 65 mph Speed Limit Save 3,113
Lives?" by Charles A. Lave. Insurance Institute for Highway Safety, Arlington, Va.
Representative terms from entire chapter:
speed limits
