Bicycle and Motor Vehicle Crashes: A Focus

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:

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:

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.

**Table 1**
Estimated share of reported 1993 U.S. motor vehicle-bicycle crashes for Cross-Fisher Problem Type 13: motorist overtaking, bicyclist unseen.
	Non-Fatal	Fatal	Total
Total reported car-bike crashes, all types	65,000	814	65,814
Estimated Type 13 (4% non-fatal, 24.6% fatal)	2,600	200	2,800
Estimated Type 13 percentage of total reported crashes	3.95%	0.3%	4.3%

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.

**Table 2**
Cross-Fisher Class D Car-Bike Crashes
	Fatal (N=166)	Non-Fatal (N=753)
Type 13: Bicyclist not observed	24.6%	4.0%
Type 14: Car out of control	4.2%	0.7%
Type 15: Counteractive evasive	2.4%	1.7%
Type 16: Motorist misjudged	1.8%	2.0%
Type 17: Path obstructed	0.6%	2.0%
Type unknown	4.2%	0.1%
Total Class D	37.8%	10.5%

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.

Speed kills

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:

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:

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.

High speeds

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.

Narrow lanes

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.

Sight obstructions

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.

Night riding

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/drug use

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).

Seat-of-the-pants profile

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.

Conclusions

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.

Chapter 3
Car-Bike Crashes 2
A Broader View

Figure 1
Differences in Cross-Fisher fatal and non-fatal distributions by class.

Figure 2
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; Williams, 1981.

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:

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.

**Table 3**
Five crash studies compared
	A	B	C	D	E	F	G
Cross-Fisher	13.9	17.0	18.7	10.5	14.2	14.5	11.2
Missoula	8.9	10.0	23.3	13.3	8.9	20.0	15.6
New Zealand	8.0	13.0	17.0	8.0	27.0	13.0	13.0
Hunter	11.8	16.0	22.3	8.5	8.4	12.1	20.9
Madison	3.8	8.3	29.8	4.1	5.1	33.4	23.3
Average	9.3	12.9	22.2	8.9	12.7	18.6	16.8

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.

**Table 4**
Top 10 Cross-Fisher crash types, all age groups
Class/Type	Description	percent	cummulative percent
B5	Bicyclist ride-out: intersection controlled by stop sign or yield sign	10.2	10.2
C9	Motorist failure to yield at stop sign or yield sign	10.2	20.4
E18	Bicyclist unexpected left turn, parallel paths, same direction	8.4	28.8
F23	Motorist unexpected left turn, parallel paths, same direction	7.6	36.4
A1	Bicyclist ride-out from residential driveway or alley	5.6	42.0
F24	Motorist unexpected right turn, parallel paths, same direction	5.6	47.6
C8	Motorist drive-out from commercial driveway	5.3	52.9
D13	Motorist overtaking, bicyclist unseen	4.0	56.9
A2	Bicyclist ride-out from commercial driveway	3.2	60.1
E19	Bicyclist unexpected left turn, parallel paths, opposite direction	3.3	63.3

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 in mind.

**Table 5**
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.

This page was last updated Friday, 27-Sep-2002 23:04:44 EDT .
Please send comments to jeffrey.hiles@wright.edu.

Chapter 2Car-Bike Crashes 1Those Bothersome Bumps From Behind