Hiles, Jeffrey A. Listening to Bike Lanes. September 1996.
Car-Bike Crashes 1
Those Bothersome Bumps From Behind
The kind of separation between cars and bikes provided by bike lanes and next-to-the-road bike paths
helps keep motorists from bumping into bicyclists’ back ends. However, these facilities do little to prevent the numerous
kinds of collisions caused by the crossing and turning movements of both bicyclists and motorists. Bikeway critics, therefore,
question whether overtaking motorists are enough of a threat to justify the effort to separate bikes from cars, considering
that there may be side effects, such as hindering bicyclists’ movements and making crossing and turning more difficult,
or even more frequent. This chapter will put through the ringer what we know about bump-from-behind collisions in hopes of
squeezing out a reasoned understanding of these controversial car-bike crashes. (For convenience, I will use “car”
to refer to any kind of motor vehicle.)
John Forester (1994) argues that you can get a clear answer to the overtaking-risk question by looking
at crash statistics:
These show that many more car-bike collisions (about 95 percent) are caused by crossing
and turning maneuvers from in front of the cyclist than are caused by the car-from-behind-a-lawful-cyclist collisions that
worry cyclist-inferiority believers so much. Furthermore, car-bike collisions are only about 12 percent of all accidents to
cyclists. This combination makes the car-overtaking-a-lawful-cyclist in urban areas in daylight (which is the type of accident
used to justify transportational bikeways) only about 0.3 percent of total accidents to cyclists (pp. 10-11).
Kenneth Cross (1978), whose bicycle crash studies form the foundation for many of Forester’s
arguments, paints what seems to be a different picture when he describes what he calls “Problem Type 13,” in which
an overtaking motorist fails to see a bicyclist until it’s too late to avoid a collision:
Although seven other problem types occurred more frequently than Problem Type 13, this
problem type must be considered one of the most important because it accounted for nearly one-fourth of all fatal accidents
in the sample—three times as many as any other problem type (p. 72).
So we have on the one hand an analysis that says the overtaking risk is negligible, and on the other
hand an analysis that characterizes the overtaking collision as the most deadly of all car-bike crashes. A clearer picture
emerges when we look more closely at Type 13 crashes and, first, at the study from which these statistics came.
The Cross-Fisher study
Cross and Gary Fisher published the results of their landmark car-bike crash study in 1977. They had
gathered police reports describing 166 fatal and 753 non-fatal car-bike crashes from four areas in different parts of the
United States: Los Angeles, California; Denver-Boulder, Colorado; Tampa-Orlando, Florida; and Detroit-Flint, Michigan. After
undertaking the monumental task of visiting crash sites and interviewing participants, the researchers sorted the crashes
into 37 different problem types, grouped into seven general classes (Cross, 1978, p. 25).
Class D consists of five motorist-overtaking-bicyclist crash types. Among these, Type 13 makes up a
very high portion of bicyclist fatalities, is arguably the most feared of all accident types, and has therefore been studied
very thoroughly. Because of that, and since fear of this motorist-overtaking-unseen-bicyclist crash type is both a strong
motivation for advocates of separate bicycle facilities and a major target of those who oppose such facilities, this discussion
focuses mainly on Type 13.
Fatal versus non-fatal crash reporting
The difference between fatal and non-fatal crash reporting is an important part of the picture. As
we will see, fatalities give us a sense of how destructive different crash types tend to be. Non-fatal crash statistics show
us the relative frequencies of different crash types.
Another difference between fatal and non-fatal crashes is that we have a complete record of fatalities,
but a great many non-fatal crashes go unreported. Cross estimated that at least two thirds of all car-bike collisions are
not reported to police, even though more than half of those unreported crashes inflict injuries “severe enough to require
some form of medical treatment” (p. 16).
This raises the question of whether the one third of non-fatal crashes that get reported accurately
represent the total crash picture. Bicycles with severe injuries, for example, have more incentive to report crashes than
cyclists with slight scratches or no injuries at all. So unreported injuries may be less serious on average than reported
injuries. It is possible, then, that crash types that tend to produce more serious injuries would be over represented in the
crash reports. There is nothing wrong, though, with being more concerned about crashes that do more damage. For practical
purposes, then, we should be well served by the picture of car-bike collisions we get from looking at those that do get reported.
Perspective: 1993 U.S. car-bike crashes
To be more concrete, in 1993 car-bike collisions in the United States killed 814 bicyclists; this we
can say with confidence. Also, there were some 65,000 reported non-fatal car-bike injuries (National Highway Traffic
Safety Administration, 1994, p. 129). Perhaps more than 130,000 car-bike collisions went unreported. So fatalities made up
1.2 percent of reported car-bike collisions and perhaps less than 0.4 percent of all car-bike collisions.
The non-fatal statistics, then, represent about 99 percent of all car-bike collisions and give us the
best picture by far of the relative frequencies of different crash types. The fatality percentages, when compared with the
non-fatal distribution, give us an idea of the relative destructive capacities of the crash types. We can say, for example,
that Type 13 crashes in the Cross-Fisher study are relatively infrequent; they make up a small portion of non-fatal crashes.
But when they do happen, they are much more destructive than most; they make up a significantly larger portion of fatalities.
If the Cross-Fisher statistics were accurate for 1993 car-bike collisions, we would expect to see about
200 deaths from Type 13 crashes and about 2,600 non-fatal Type 13’s, perhaps 7,800 if you include unreported crashes.
Of all 1993 car-bike collisions, fatal and non-fatal combined, Type 13 would make up 4.3 percent
of reported crashes and perhaps 4.1 percent of all crashes including those not reported. Fatal Type 13 crashes would
make up 0.3 percent of all reported car-bike collisions and perhaps 0.1 percent of all crashes, counting those not reported.
It is important (and scary) to realize that nearly one fourth of all bicyclists killed by cars were
hit by overtaking motorists who did not see them. But it is also important to keep in mind that those overtaking fatalities
account for a fraction of a percent of all car-bike collisions. Although non-fatal injuries, especially those involving brain
injury, can be as tragic as fatal injuries—perhaps worse.
Estimated share of reported 1993 U.S. motor vehicle-bicycle
crashes for Cross-Fisher Problem Type 13: motorist overtaking, bicyclist unseen.
Total reported car-bike crashes, all types
Estimated Type 13 (4% non-fatal, 24.6% fatal)
Estimated Type 13 percentage of total reported crashes
Sources: Cross, 1978 ; National Highway Traffic Safety Administration, 1994
Other overtaking crash types
In addition to Type 13, in which the motorist did not see the cyclist, Cross and Fisher named five
other types of motorist-overtaking crashes, in which the driver did see the bike before the crash. These included Type
14, where the car was out of control; Type 15, where the cyclist swerved left to escape the overtaking car as the motorist
swerved in the same direction in an attempt to avoid the bike; Type 16, where the motorist misjudged the space required to
pass; Type 17, where the cyclist’s path was obstructed, forcing the poor pedaler to either swerve into the path of the
overtaking car or collide with the obstruction; and a category with no number labeled “motorist overtaking: type unknown.”
Collectively, these make up “Problem Class D–Motorist Overtaking/Overtaking Threat.” Table 2 shows the Cross-Fisher
statistics for Class D.
Cross-Fisher Class D Car-Bike Crashes
|Type 13: Bicyclist not observed
|Type 14: Car out of control
|Type 15: Counteractive evasive
|Type 16: Motorist misjudged
|Type 17: Path obstructed
|Total Class D
Bike crashes with and without cars
Forester may be close to the mark in saying that cars play a part in only 12 percent of cycling crashes.
Studies of emergency room-treated bicycle injuries indicate that motor vehicles are involved in 9.4 or 18 percent of these
cases (Clarke & Tracy, 1995, p. 29; Stutts, Williamson, Whitley, & Sheldon, 1990, p. 71).
It does not follow, though, that we should only devote 12 percent of our bike safety concern to car-bike
crashes. A Seattle, Washington, study found that half of all serious bike injuries involve motor vehicles. What’s
more, 82 to 96 percent of bike-related deaths involve motor vehicles (Cross, 1978, p. 22; Rogers, 1994, p. 10; Bicycle
Institute of America, 1993, p. 6). On average, then, car-bike collisions tend to inflict worse injuries than bike crashes
that don’t involve motor vehicles.
Unlucky Type 13
So, car-bike collisions are inordinately dangerous among all types of bike crashes, and Type 13 is
inordinately deadly among car-bike collisions. It would appear that to wave off these overtaking accidents because they make
up a small part of all bike crashes is like saying never mind the cobra as you walk through the snake pit because most of
the serpents in there are garter snakes.
However, Type 13 crashes, like so many bicycle transportation issues, can’t be accurately summed
up with so simple a statement. A recurring theme throughout this paper is that the more you dig below the surface of bicycle
issues, the more the picture changes—usually becoming more complex. Another part of the Type 13 story unfolds when you
look at when and where this crash type most often occurs: at night and on rural roads.
Overtaking: a rural and nighttime problem
Cross reported that this motorist-overtaking category was the only crash type in his study where nighttime
crashes out-numbered daytime ones. Nighttime crashes made up 71 percent of Type 13 fatalities, but only 30 percent of all
fatal collisions. Also, 65 percent of non-fatal Type 13 problems were in darkness. In contrast, only 10 percent of non-fatal
collisions of all types fell between dusk and dawn (p. 36). In more than 90 percent of the nighttime Type 13 crashes, the
cyclists had no lights (Williams, 1993a).
According to Cross, “about 60 percent of the Type 13 accidents occurred on a narrow, ‘rural-type’
roadway with two traffic lanes and no ridable shoulder or sidewalk” (p. 72). This problem type made up half of rural
car-bike fatalities, as opposed to just 10 percent of urban ones (Williams, 1993a).
To make a more general statement, two key factors in Type 13 crashes are poor visibility and narrow
roads. This means, for one thing, that on some roads the risk of getting hit from behind will be higher than the Cross-Fisher
average. It also suggests that a blanket statement which says that a particular crash type makes up X percent of crashes is
simplistic and may not apply to a given road.
As we have seen, overtaking collisions make up an inordinately large portion of fatalities, even though
they make up a relatively small portion of all collisions. This is easy to understand when you consider that such crashes
are apt to involve higher speeds.
In the Cross-Fisher study, more than half of all fatalities were on roads with posted speed limits
greater than 35 mph, even though less than 20 percent of all collisions occurred in that fast traffic (Cross, 1978, p. 40).
A more recent study of fatal accidents in Victoria, Australia, closely matched these findings (Hoque, 1990, p. 4).
The United Kingdom Department of Transport has provided a more dramatic illustration of the difference
speed makes. The department determined that when pedestrians are struck by cars traveling at 20 mph, only about five percent
are killed and most injuries are slight, with 30 percent of the walkers left virtually unscathed. At 30 mph, though, 45 percent
are killed and many seriously injured. Cars zipping along at 40 mph kill 85 percent of the pedestrians they strike (Bicycle
Federation of America, 1993b).
Simply put, cars that are overtaking cyclists are more likely to be at full speed than, say, cars crossing
and turning at intersections. The higher the speed the harder the impact, and the more damage done.
Where (not whether) overtaking is a problem
In a quote at the beginning of this chapter, Forester creates the impression that what he calls “bicyclist
inferiority believers” base their bicycle facilities planning on a distorted and irrational fear of a type of crash
that makes up “only about 0.3 percent of the total accidents to cyclists.” But in using this “0.3 percent”
figure, Forester himself distorts the bicycle crash picture.
First of all, Forester waters down the statistics by including bike crashes that aren’t relevant
to the bikeway discussion. Bicyclists rounding turns too fast, slipping on wet leaves, running into dogs and having other
non-motor vehicle crashes have little to do with the wisdom or folly of bikeways.
The one exception is when cyclists mix with pedestrians on trails, an environment which may foster
collisions between the two. Yet, Forester uses his “0.3” figure to argue against “bikeways” in general,
which he defines as including not only “bicycle sidewalks or side paths,” but also “bike lanes that are
part of the roadway,” a type of facility where car-bike collisions (and, perhaps to a much smaller extent, bike-bike
collisions) are virtually the only germane types of crashes.
Second, Forester’s “0.3 percent” figure gives no more weight to a crash that’s
likely to kill than to a crash that’s likely to cause a few cuts and scratches. Certainly any fall can be fatal, even
a slip in the bath tub. Nevertheless, some ways of falling from a bicycle are more likely to cause serious injuries than others.
As we have seen, it appears that the motorist-overtaking collision is on average the most destructive of all.
Third, Forester masks the overtaking problem by restricting his count to “urban” roads.
To distinguish between roads they called “urban” and those they labeled “rural,” Cross and Fisher
looked at the characteristics of the roads, not whether they were within city limits:
Accidents usually were classified as rural if they occurred in an area where (a) the posted
speed limit was 45 mph or more, (b) there were no curbs or sidewalks adjacent to the roadway, (c) street lights were not present
at the intersections, and (d) at least 50 percent of the area within one-half mile radius of the accident sites was open.
Cases that did not meet all four of these classification criteria were classified as urban.
By restricting the car-bike portion of his crash count to roads classified as “urban,”
Forester is excluding many of the kinds of roads on which overtaking crashes are most likely to occur. He is even excluding
some roads that are actually within city limits. With such an unusually dangerous crash type, it’s important to understand
the dynamics of where it happens and why. Cross describes the setting of Type 13 crashes:
About three-fifths of the rural accidents and about one-half of the urban accidents occurred
on a narrow, two-lane roadway with no ridable shoulder. Thus, about 60 percent of the Type 13 accidents occurred on a narrow,
“rural-type” roadway with two traffic lanes and no ridable shoulder or sidewalk (p. 72).
Fourth, Forester says that “practically all Americans” are “totally misinformed about
cycling in traffic” and as a result have phobic fears of the cars from behind (1994, p. 8). Assuming that is true, we
would expect practically all Americans to do everything possible to avoid cycling on narrow, fast, hilly, winding busy streets.
For the sake of argument, suppose that those streets pose a higher overtaking threat. Then we must ask this: Does the relatively
low number of overtaking collisions mean only that there is little threat of that kind of accident in the street system, or
do cyclists themselves keep the number of overtaking collisions low by staying away from streets where the threat is strongest?
Fifth, to use Forester’s own words, “there is no reasonable way to rank car-bike collision types in order of importance,
because the order depends upon what kind of cyclist you are and where you are riding…” (1993, p. 268).
The Cross-Fisher statistics are stacked by the preponderance of crashes by children, whose cognitive
skills are not as well developed as older cyclists’. The top children’s crashes are caused by kids running stop
signs and riding out of driveways (Forester, 1993, p. 269).
Moreover, few children in the Cross-Fisher study were involved in Type 13 crashes. The median age of
bicyclists in these unlucky crashes was higher than it was for any but one other crash type. “Apparently,” Cross
wrote, “bicyclists younger than 11 or 12 years of age are not permitted to ride during darkness and in types of areas
where Type 13 accidents occur” (p. 73).
The top crash types for adults, though, are not loaded with the kinds of simple, careless errors that
children commit. Arguably, adult crashes better represent the inherent hazards of the cycling environment—as opposed
to hazards of bad cycling. For adults on roads classified as “urban,” Type 13 ranked seventh out of the 37 crash
types, followed by Type 16, in which overtaking motorists saw the bicyclists, but misjudged and passed too closely.
On “rural” roads, Type 13 was the number-one crash type for adults, followed again by Type
16 (Forester, p. 269). Granted, in the lion’s share of Type 13 crashes, the bicyclists had failed to make themselves
visible with lights at night. Type 16, however, is also an overtaking problem, is number two for rural roads, and can’t
be blamed on anything as simple as lightless night riding.
So, although overtaking motorists are involved in a relatively small portion of bike crashes in general,
some stretches of roadway have characteristics that contribute to a higher than average overtaking threat. When it comes to
saying yea or nay on plans for bicycle facilities, it seems more effective to address problems of specific places than to
adopt blanket policies as if all streets were the same.
A number of factors aggravate the overtaking threat:
As we have seen, high speeds mean high impact, which means more severe injuries. In some cases speed
might also increase the likelihood of overtaking collisions by decreasing the amount of time motorists have to see, recognize,
and maneuver around bicyclists. Although this should not be a factor if motorists are traveling at speeds that suit the conditions,
small hills and bends in otherwise straight roads can create blind spots.
Cyclists are more likely to be in the path of overtaking motorists when the lanes are narrow. With
wide lanes, cyclists are more likely to be to the right of the danger zone.
Again, hills and turns limit how far motorists see down the road and decrease the amount of time motorists
have to recognize and avoid bicyclists.
This may not appear to be a design factor, since you can’t control the sun. It may seem like
more of a law enforcement problem: getting cyclists to use lights at night. But just a small red light, and in some places
just a small red reflector, can satisfy the law without making a bicyclist all that conspicuous on the roadway. Also, bicyclists
are not required to have lights on their bikes in the daytime. Few cyclists would want a law that requires them to load their
bikes with lighting equipment at all times. But this means that bicyclists who never anticipated riding at night are not likely
to have lights when the need for a night ride arises, such as when a meeting unexpectedly runs past dusk.
It would be great if every bicyclist who rode at night looked like a rolling Christmas tree. Certainly,
the requirement to have lights at night is one of the most important bike laws that police can enforce for bicyclists’
own good. But the advocate or bicycle planner whose town has a lot of nighttime cycling—a university town, for example—may
find that facilities such as bike lanes, for example, reduce serious injuries on some roadways more effectively than trying
to create a utopian society.
Alcohol was identified as a factor in a third of Cross and Fisher’s rural Type 13 fatalities
(Cross, 1978, p. 73). In a 1990s update of the Cross-Fisher study, “alcohol/drug use” was found to be an “over
representation” for crashes involving bicyclists in adult age groups (Hunter, Pein, & Stutts, 1994, p. 10). The
National Center for Statistics and Analysis (1994) also found that “alcohol involvement—either for the driver
or the cyclist—was reported in more than a third of pedalcyclist fatalities in 1993” (p. 3).
Traffic volume?—Not necessarily
Heavy traffic would seem to be an obvious risk factor. It appears reasonable to assume that the more
frequently a bicyclist gets passed, the more that bicyclist is exposed to the overtaking threat. But the relationship between
traffic volume and crashes is not that straight forward. Wachtel and Lewiston (1994), for example, found no significant relationship
between traffic volume and the risk to bicyclists crossing intersections (p. 32). Studies of motor vehicle crashes not involving
bikes have even shown that accident rates are sometimes lower with increased traffic (Hall & Pendleton, 1990). This makes
sense if you imagine a bicyclist riding urban streets at rush hour when both bicyclists and motorists are extra vigilant and
careful because of the heavy traffic. In contrast, picture a cyclist without good lights and reflectors who is pedaling in
darkness on a narrow country road. Because of the scarce traffic, a motorist would not anticipate encountering a bicyclist
there and could crest a hill and find the cyclist just ahead with no time to react.
Garder, Leden, and Thedeen (1994) point to two more studies that support the notion that the number
of “bicycle accidents at an intersection is proportional to the bike volume, but not very dependent on the motor-vehicle
volume.” They speculate that “increased vehicle volumes make the cyclists more careful. At least the risk to cyclists
would not increase in proportion to the vehicle-volume increase…” (p. 433).
Putting this all together, we might expect an unusually high overtaking-crash problem on a road with
speeds of 45 mph or faster that is narrow, two-lane, hilly, winding, and that connects university student housing with popular
night spots in a community that has a depressed economy and therefore high alcoholism.
But “unusually high” is a vague assessment. How bad of a problem would overtaking crashes
be on the rare road that fits that profile? How about on roads that fit parts of the profile? There are too many variables
and there is too little information to give anything but an intuitive impression.
It is not even possible to clearly define the overtaking risk on “rural” versus “urban”
roads. Cross-Fisher tells us that Class D accidents made up 10.5 percent of the total non-fatal sample, but 31 percent of
the rural portion of that sample. They made up 37.8 percent of the total fatal sample, but 56 percent of fatalities on roads
classified as rural (Cross, 1978, p. 71). This does not, however tell us about risk. Rural-type roads have far fewer intersections
per mile than urban roads. So there are fewer opportunities for crossing and turning movements and we would expect these non-overtaking
crashes to make up a smaller portion of the picture, even if the risk per mile of getting hit from behind was the same for
both urban and rural roads.
Given two randomly-selected car-bike crashes, one rural and one urban, we can say that the chances
of the rural one being an overtaking collision is higher than it is for the urban. But to determine the relative risks of
riding on different roads, we would have to know how much bicycle traffic there was on the road for each accident in the sample.
The data is not there to either confirm or deny differences in overtaking threat from one road to another.
We can say that motorists overtaking bicyclists accounted for 10.5 percent of the non-fatal crashes
in the Cross-Fisher study, so that about nine out of 10 car-bike collisions involved crossing and turning movements. We can
say that the dangerous Type 13, where the motorist didn’t see the cyclist, made up only four percent of the non-fatal
crashes. Furthermore, urban daytime Type 13’s accounted for only one percent of the non-fatals. But how much
does that tell us about the problems on a particular stretch of road? And how do we weigh the severity of crashes? How many
cuts and scrapes equal a death?
The purpose of this chapter is neither to disparage Forester nor to exaggerate the overtaking threat.
Rather it is to chip away at the illusion of certainty that numbers can create. We must use aggregate statistics with caution;
they may mislead us when we make decisions about specific local problems.
Previous | Contents | Next
Hiles, Jeffrey A. Listening to Bike Lanes. September 1996.
Car-Bike Crashes 2
A Broader View
Differences in Cross-Fisher fatal and non-fatal distributions
Non-fatal car-bike crash distributions from five studies, grouped
by Cross-Fisher class.
Cross-Fisher Problem Class Descriptions
- Class A: Bicycle ride-out from driveway, alley, and other mid-block location.
- Class B: Bicycle rideout at controlled intersection.
- Class C: Motorist turn, merge, drive-through, drive-out.
- Class D: Motorist overtaking, overtaking threat.
- Class E: Bicyclist unexpected turn, swerve.
- Class F: Motorist unexpected turn.
- Class G: Other.
Sources: Atkinson & Hurst, 1983; Cross, 1978; Hunter, 1994; Ross, 1992;
Cross-Fisher compared with other studies
Cross and Fisher launched their pioneering report nearly two decades ago. Fortunately, over
the years other researchers have used the same crash classification system, or customized versions of it, to study local and
regional problems. So we can see if the original study findings hold up over time and in different locations.
Figure 2 compares the Cross-Fisher non-fatal crash statistics with those of four other car-bike
crash studies. Several of the studies were too small to have a statistically significant sample of fatalities, so I have only
compared non-fatal statistics. Of course, the non-fatals give the best overall picture of crash type frequencies, so this
is not a serious shortcoming.
There are some other differences between the original study and those done since. For one
thing, not many other researchers besides Cross and Fisher were able to visit the crash sites and interview participants.
Relying on police reports alone may inject some errors. Also, most of the studies used classification systems that varied
in some ways from Cross-Fisher. In an attempt to simplify and increase the accuracy of the comparison, I have used the seven
broader categories that Cross and Fisher called “problem classes,” not the 37 more specific “problem types.”
Five other crash studies
Missoula, Montana (Williams, 1981): This study of 91 accidents from a two-and-a-half-year
period found that the median age of bicyclists involved was higher in Missoula than in the Cross-Fisher study, reflecting
the large number of adult bicyclists in this college town. Otherwise, the distribution pattern follows the Cross-Fisher study
rather closely. Only one of the 19 Missoula cyclists involved in nighttime accidents had a light.
New Zealand (Atkinson & Hurst, 1983): Of the 550 non-fatal collisions studied
in New Zealand, 8 percent were Class D overtaking crashes, not too far off from Cross-Fisher’s 10.5 percent. New Zealand’s
sample of 142 fatal crashes contained 40 percent of type D, which compares to 37.8 percent in the U.S. study. The authors
noted other similarities as well:
It can be seen that the great majority of Type 13 fatalities occur under conditions of
bad visibility or at night, often to unlit cyclists. Type 16 accidents (motorist misjudged space required to pass) do not
occur at night; the same was true in Cross and Fisher’s study. This suggests that motorists who do see a cyclist at
night leave plenty of room.
In nearly all of the Type 13 accidents that occurred in full daylight the motorist’s
attention had been diverted by some other activity such as dealing with a fly on the windscreen, picking up a dropped bottle,
attending to a misbehaving passenger, or watching something on the other side of the road. The apparently common notion that
otherwise-attentive drivers do not see cyclists from behind is not supported by the data. Probably the fact that motorists
often fail to see cyclists in other accident situations has been extended in the public imagination to include overtaking
Class E (bicyclist unexpected turn/swerve) made up an unusually high percentage of crashes
in this study. The authors speculate about a number of possible causes, including New Zealand’s narrow, poorly-paved
roads; old traffic laws requiring vehicles to wait at the side of the road before turning right (the equivalent of turning
left in the U.S., since New Zealanders drive on the left side of the road); and even the growing popularity of BMX bicycles,
which have an “image,” the authors say, that could “encourage skylarking and swerving.”
Hunter study (Hunter, Pin, & Stutts, 1994). This study classified nearly 3,000
bicyclist-motor vehicle collisions from the recent years of 1990 and 1991. The sample came from California, Florida, Maryland,
Minnesota, North Carolina and Utah. A project of the University of North Carolina Highway Safety Research Center, this study
found that adults make up a larger portion of the bike crash population now than they did during the Cross-Fisher study. Also,
Class E is a little lower and Class G (the “other” category) is a little higher than in the Cross-Fisher study.
Otherwise, the crash distribution follows the old classic very closely.
Unlike Cross and Fisher, Hunter and company did not seek out a separate statistically significant
sample of fatal crashes. Of all the crashes, just 46 (1.6 percent) were fatal. To compare the relative severity of crash types,
the researchers looked at the distribution of fatals combined with the 473 “serious” injury crashes in the sample.
Once again, the motorist-overtaking class has the highest percentage of the worst injuries, although the differences between
classes are not as dramatic as they are in the Cross-Fisher fatalities.
The two most frequent ways in which bicyclists contributed to causing crashes were “failure
to yield” at 20.7 percent and “riding against traffic” at 14.9 percent.
Madison, Wisconsin (Ross, 1992). Madison is a college town with a significant network
of bike lanes and bike paths. As one might expect, the Class D overtaking accidents among this sample of 774 bicyclist-motorist
crashes is the lowest of any of the five studies—just 4.1 percent of the entire class.
Two other classes were unusually high, though: Class C (motorist turn, merge, drive-through,
or drive out) and especially Class F (motorist unexpected turn). An on-coming motorist turning lift into the path of a straight-through
cyclist made up a whopping 23.3 percent of Madison’s crashes. In the Cross-Fisher study, this type of crash accounted
for only 7.6 percent of the sample. In Madison, bicyclists traveling in a contra-flow bike lane on University Avenue made
up 36 percent of the victims of this type of crash. A contra-flow lane runs against the direction of traffic. In this case,
it runs down the left side of a high-volume, multi-lane, one-way arterial next to the University of Wisconsin. Motorists turning
left off University Avenue cross the contra-flow lane. Motorists entering the avenue from side streets turn left across the
contra-flow lane; their attention is focused on the motor traffic, which comes from their right, while the bicyclists come
at them from the left.
One-eyed folks of either pro-bikeway or anti-bikeway persuasion may be tempted to draw unwarranted
conclusions from Madison’s unusual distribution of crash types. Pro-bikeway advocates might point to the fact that classes
A, D, and E are all quite low (see Figure 2 on page 22), and that all of these kinds of crashes might be reduced by bike lanes
and bike paths. Type A (bicyclist ride-out from driveway, alley and other mid-block location) may be reduced because bicyclists
would ride onto a bike path or bike lane instead of into a car lane. Type D, of course, would be reduced because bicyclists
would be separated from overtaking motorists. Type E crashes (bicyclist unexpected turn, swerve) would be reduced because
bikeways make cyclists ride more predictably, or give cyclists room in which to “swerve” free of threat from overtaking
traffic. Pro-bikeway advocates might say that classes C and F appear to be large, but that this is because bike facilities
have reduced other classes to relatively small portions of the total. Even if C and F did increase, proponents might add,
these two classes are the two least destructive—the fatality percentages are much lower than the non-fatal (see Figure
1). An increase in less destructive crashes may be a fair price to pay for a decrease in the more deadly ones.
Anti-bikeway advocates might point to Madison’s inordinately large Class C and Class
F and charge that these are crashes they would expect to see increase because of the bike paths and lanes. These facilities
give motorists and bicyclists a tendency to pay less attention to each other, it might be argued, and “hide” bicyclists
from view. Worse yet, they complicate crossing and turning interactions between cyclists and motorists. These critics might
argue that bike facilities have made these crashes so inordinately common that other types are dwarfed and therefore make
up a smaller than normal percentage of the whole, even though they may not be significantly reduced in number. It is not possible
to tell from the Madison study whether the city has an unusually low accident rate for classes A, D, and E, an unusually high
accident rate for classes C and F, or some combination of the two.
The one seemingly solid specific problem, the University Avenue contra-flow bike lane, is
not so cut and dried either. We might expect bicyclists riding against traffic to have a higher risk of tangling with motorists.
Motorists tend to pay most attention to the primary flow of traffic: other motorists. Bicyclists whose movements don’t
match the patterns of motorists on a roadway risk eluding motorists’ awareness. Wachtel and Lewiston (1994) found that
bicyclists crossing intersections while riding on the wrong side of the street were twice as likely to collide with cars as
right-way cyclists. They also found that wrong-way cyclists riding on sidewalk-like bike paths were about four times more
likely to clash with cars while crossing intersections than were those riding with traffic.
A contra-flow lane would seem to be a mistake. University Avenue, though, is a major route
to the university. Many bicyclists would have to expend extra time and energy if they took an alternative route. As a result,
Madison would probably see a lot of wrong-way bicycling on that road, even without the contra-flow lane. Those bicyclists
might be at an even higher risk without the bike lane. Moreover, University Avenue has a high volume of both motor and bicycle
traffic, so there are more opportunities for car-bike collisions than on most streets, and we would expect higher numbers
of crashes there than on less-traveled roads. Once again, we can't tell from Cross-Fisher-style studies whether bicyclists
have higher or lower risks per mile in different locations.
The one thing we can say with confidence is that crash types in Madison appear to differ
from the typical American city’s. Without more information, we cannot say for sure why there is a difference. It could
be from bike facilities (for better or worse), from the city’s unusually large number of adult cyclists, from peculiarities
in the street patterns of the city, or from any combination of these or other factors. We can’t even say if the difference
we see is for better or for worse. If we saw Madison-like distributions in other towns with similar networks of bike lanes
and paths, we might conclude that the facilities were a factor, but even this would not tell us if they were a good factor
or bad, only that they had made a change in the relative distribution of crash types.
Five crash studies compared
Sources: Atkinson & Hurst, 1983; Cross, 1987; Hunter, 1994; Ross, 1992; Williams, 1981.
The overall pattern
The slight differences in the results of these five studies provide material for some interesting speculation.
But it’s the similarities that are most significant. All of the studies reveal the same general crash patterns. Most
notably, overtaking crashes are relatively infrequent. One possible explanation is that the motorists who come from behind
you when you’re bicycling usually have plenty of time to see you long before they reach you, so they have plenty of
time to avoid you. What’s more, an overtaking motorist is following a path parallel to yours, several feet to the left
in most cases—and parallel lines don’t meet.
Most often, the danger lies where the bicyclist’s and motorist's paths cross suddenly, catching
both parties by surprise, and leaving too little time for either party to avoid impact.
Another possible explanation for the low number of overtaking collisions is that, as noted earlier,
bicyclists fear and avoid roads where the overtaking threat seems greatest. Given that overtaking crashes are the most destructive
types, this fear would not seem totally unfounded. If this is a factor, it may mean that bicyclists are not making full use
of the road system. To put it another way, it may mean that the road system is not fully accessible to bicyclists.
Yet another factor that could contribute to the relatively low percentage of overtaking collisions
is that the overall statistics are child-heavy. That is, two thirds of the Cross-Fisher non-fatal crashes involved cyclists
15 years old and younger (Cross, 1978, p. 28). In general, children make more mistakes than adult bicyclists. Youngsters are
especially prone to ride out of driveways or run stop signs without looking, crash types that stack the deck against overtaking
types. Children also tend to stay more within residential areas where traffic is slower, so the overtaking risk is smaller.
These neighborhood streets may also have frequent intersections, which may raise the likelihood of crossing and turning crashes.
In contrast, for adults on rural roads, Type 13 overtaking crashes are the number one crash type, and Type 13 is number six
among urban adult crashes. It should be said again, though, that this does not mean that adults on rural roads are more prone
to overtaking crashes than kids on city streets. There are just fewer intersections in the country, so fewer intersection-type
crashes. Also, keep in mind that most overtaking crashes, regardless of location, happen at night.
In any case, the crash report patterns show that it’s the motorists in front of cyclists, primarily
at intersections, who are most likely to be involved in car-bike collisions—except, of course, when the bicyclists are
riding without good lighting on narrow, high-speed roads at night. However, we cannot unequivocally conclude from this that
overtaking crashes are inconsequential.
Table 4 shows the ten most frequent crash types in the Cross-Fisher study. The top seven, which together
make up 52.9 percent of the crashes, are all crossing and turning types. But number eight out of the 37 crash types is unlucky
Type 13, the leading cause of car-bike fatalities. So although the statistics tell us that overtaking crashes make up a relatively
small part of all crashes, we can hardly tell planners and engineers to just disregard these bumps from behind.
Top 10 Cross-Fisher crash types, all age groups
||Bicyclist ride-out: intersection controlled by stop sign or yield sign
||Motorist failure to yield at stop sign or yield sign
||Bicyclist unexpected left turn, parallel paths, same direction
||Motorist unexpected left turn, parallel paths, same direction
||Bicyclist ride-out from residential driveway or alley
||Motorist unexpected right turn, parallel paths, same direction
||Motorist drive-out from commercial driveway
||Motorist overtaking, bicyclist unseen
||Bicyclist ride-out from commercial driveway
||Bicyclist unexpected left turn, parallel paths, opposite direction
Source: Cross, 1978.
Education and engineering: different needs, different outlooks
The bottom line is that a cycling instructor teaching a group of bike club members how to ride in city
traffic can confidently say that by far the most frequent collisions are crossing and turning types and that urban cyclists
have a relatively slim chance of getting rear ended as long as they don’t ride at night without lights. On the other
hand, planners and engineers can point with equal confidence to overtaking crashes as one of their main concerns. Overtaking
crashes are the number one cause of bike-related fatalities—and fatalities make the news, spur people to action, and
bring demands onto city officials far more than non-fatal crashes. Moreover, we must keep in mind that when traffic planners
and engineers are pressured to take action to help prevent bike crashes, of all their engineering choices none are as visible
to the public as bike lanes and paths, and these facilities are capable of reducing both overtaking collisions, which are
the most deadly types, and bicyclist ride-out collisions, the most common type of car-bike crash for younger children (Wilkinson,
et el., 1994b, pp. 24-25). In a later chapter we will see more clearly that crash statistics alone do not give us a complete
picture of why planners, engineers, and bicycle advocates have reason to shape environmental design with the overtaking threat
Cross-Fisher Type 13 crashes summarized
- 4.0% of non-fatal crashes
- 24.6% of fatal crashes
- 10% of urban fatal crashes
- 50% of rural fatal crashes
- Two thirds happened at night, 90% to cyclists without lights
- Nearly one third involved a drinking driver
- Half of urban Type 13’s and three fifths of rural Type 13’s were on narrow, two-lane roadway
with no ridable shoulder.
- Number six adult urban crash type, out of 37 types.
- Number one adult rural crash type
Sources: Cross, 1978, pp. 71-72; Forester 1993, p. 269; Williams, 1993a.
Previous | Contents | Next