Highway Safety: Factors Affecting Involvement in Vehicle Crashes
Released: 1994-11-29T00:00:00

Letter

Objectives, Scope, and Methodology

You have requested that we investigate these relationships more comprehensively and that we set the discussion of car size and safety into the larger context of the relative contributions to highway safety of driver attributes, vehicle characteristics, and their multiple interactions. Our response to your request involved an investigation into two distinct, and sometimes highly divergent, aspects of highway safety: crash involvement and crashworthiness. The study of crash involvement focuses attention on the factors likely to produce a crash. Crashworthiness, instead, examines the factors likely to produce serious injury, once a crash has occurred. The present report deals with crash involvement—that is, with the driver or vehicle characteristics that are related to the likelihood of a crash. The attributes we examined included driver age, gender and driving history, vehicle age and size (weight, wheelbase, and engine displacement). A companion report will examine crashworthiness: the factors that affect the likelihood of serious injury once a crash has taken place. A third report will examine the relationship between automobile crashworthiness and crash testing performed by the Department of Transportation.

In the present analysis, we have used a method known as “induced exposure” to estimate the likelihood of crash involvement. This approach assumes that not-at-fault drivers in two-vehicle accidents represent a random selection of drivers and vehicles on the road. The ratio of at-fault to not-at-fault drivers provides a measure of the relative involvement of drivers and vehicles in accident causation. We used a data base containing 340,000 records, with details on accidents reported in North Carolina in 1990, to produce ratios of at-fault to not-at-fault North Carolina drivers. Since these findings are based on data from only one state, they cannot be generalized to the nation. However, we did compare the North Carolina ratios to ratios we obtained from a Michigan data base and found the figures to be close and the trends quite similar. This finding is consistent with the logic of induced exposure—concerned with ratios of driver and vehicle characteristics rather than their absolute numbers—and suggests that the method may produce results that have more general applicability. (See appendix I for a discussion of the induced exposure approach and appendix II for descriptive statistics from North Carolina.)

Our Analysis

Any investigation of crash involvement must include more than counts of units (vehicles or drivers). In order to calculate the relative odds of being in a serious crash, it is necessary (but not sufficient) to compute, for example, how many 1989 Ford Tauruses or how many 16-year-old males are involved in serious crashes in a given time period. Without knowing how many Tauruses or 16-year-old male drivers are on the road, we cannot conclude whether these cars or these drivers are more or less likely than other cars or drivers to be involved in crashes. We must, in other words, know their exposure to crashes. For example, consider that it is generally well known that, in absolute terms, elderly drivers are involved in fewer serious crashes than younger drivers. But they also drive fewer miles, and under less hazardous conditions, than younger drivers. In absolute terms, therefore, elderly drivers pose a rather small highway safety problem. When their relative exposure is considered, however, it turns out that, for the miles they drive, elderly persons are disproportionately involved in collisions, particularly two-vehicle collisions.

Crash exposure can be estimated in a number of ways. Vehicle exposure in a given year is frequently measured by the number of vehicles registered. Thus, in our previous report, we tracked the number of fatalities per 100,000 registered vehicles for different weight classes of cars. Driver exposure can also be represented by a single count of the number of licensed drivers in various categories (for instance, age groups or geographic regions).

Such direct measures of exposure have serious limitations, however. While we may know how many vehicles of a certain type are registered, we do not know how many miles (if any) and under what conditions they are driven or by whom they are driven. If large cars are driven more miles, and under more dangerous conditions, an estimate of crash involvement based simply on the number of crashes per registered vehicle or even—if such data were available—on crashes per mile driven would underestimate their exposure and their safety.

For this reason, some researchers have turned to methods of estimating exposure indirectly. For example, some calculate crash rates from a crash data base as the ratio of at-fault to not-at-fault drivers of a certain type (say, young females), arguing that the not-at-fault drivers serve as a representative sample of drivers on the road—or “exposed”—under the conditions represented by the data base. This method has the practical advantage of allowing exposure estimates to be derived from the same data base as the count of crashes and, arguably, the strategic advantage of being more sensitive to the variations of driver and vehicle characteristics than is possible with direct measures (see appendix I).

For this study, we employed such an indirect or “induced exposure” method. We applied this method to the police-reported crash data base of North Carolina for 1990 that was provided to us by researchers at the University of North Carolina Highway Safety Research Center. This data base contains information on 183,616 crashes involving 484,258 individuals and 325,277 vehicles. We supplemented the crash data base by merging with it information on the drivers’ history of previous traffic violations.

We performed separate logistic regression analyses of crash involvement corresponding to three types of crashes (two-vehicle, single-vehicle rollover, and single-vehicle nonrollover) and two types of vehicles—(1) passenger cars and (2) light trucks and vans. Sixty-six percent of the crashes in our analysis involved two vehicles, 29 percent were single-vehicle nonrollovers, and 5 percent were rollovers. (Although rollovers accounted for only a small proportion of crashes, this type of crash is second only to frontal impacts in terms of deaths and injury severity.) Sixty-eight percent of crashes involved passenger cars, 11 percent involved light trucks and vans, and 21 percent were between cars and light trucks and vans. Appendix III presents the details of the analyses of passenger cars, appendix IV the light truck and van results. We present the main points here, first for passenger cars and, then, more briefly, for light trucks and vans.

Passenger Cars

Driver Age

We found no straight-line relationship between a driver’s age and crash involvement. In general, drivers under 25 were at greatest crash risk, followed by drivers over 65. The relationship was not the same for all crash types. A 16-year-old driver was over seven times more likely to be in a single-vehicle rollover crash, over five times more likely to be in a single-vehicle nonrollover crash, and more than twice as likely to be in a two-vehicle crash as was the safest driver overall—a 45-year-old.

Drivers least likely to be in a single-vehicle rollover crash were 62-year-olds. They were only one tenth as likely to be in such a crash as 16-year-olds. However, drivers in their mid-70s were about as likely as the 16-year-olds to become involved in a two-vehicle collision. As a driver’s age approached 80 years, the likelihood of such involvement in a two-vehicle collision increased sharply.

This was not true, however, of single-vehicle crashes. Elderly drivers were more likely to be involved in single-vehicle nonrollovers than 40-year-olds only after age 74 and in single-vehicle rollovers only after age 86. Figure 1 summarizes the effects of age by comparing each age’s odds of crash involvement in each crash type with those of a 40-year-old’s.

Violation History

Driving history was a strong predictor of crash involvement for two-car and single-car crashes, ranking second only to driver age. A history of alcohol-related convictions was a particularly powerful predictor. For example, drivers with histories of nonalcohol traffic violations were only 1.15 times as likely to be involved in a single-car nonrollover crash as drivers with a “clean” history. However, drivers with a history of drunk driving were at least 3.7 times as likely as other drivers to be involved in such a crash.

For two-car crashes, driving history was also a significant but less powerful predictor. Drivers with prior alcohol violations were 2.1 times as likely to be involved in a two-vehicle collision as drivers with no prior violations and 1.6 times as likely as drivers with nonalcohol violations.

Driver Gender

As noted earlier, driver gender affected the likelihood of involvement in single-vehicle crashes only. Males were twice as likely as females to be involved in either type of single-vehicle crash. Female drivers were indistinguishable from male drivers in their likelihood of being involved in a two-car collision.

Vehicle Age

We introduced the age of vehicles into our model as a way of correcting for the possibility that we might confuse the safety effect of a vehicle size with that of its condition. As our earlier report found, passenger cars have become, on the average, much lighter than they were in the 1970’s. Heavier cars, therefore, are more likely to be older cars and, presumably, to be in poorer condition. An analysis that did not control for this association would be in danger of overestimating the crash involvement of heavy cars.

Half of the cars in our data base were model year 1984 or newer, and 80 percent were built after 1978. We found that, regardless of size, newer cars were slightly less at risk for crash involvement. For example, if one car were 5 years older than another, the older car would have a risk 1.12 times that of the newer. We cannot tell, however, whether this difference stems from the deteriorated condition of the older car or the improved design of the newer car.

It should also be noted that (as the Department of Transportation (DOT) pointed out in its comments on a draft of this report) vehicle age may capture the effect of more than simply vehicle characteristics. Older cars may have more aggressive drivers and are more likely to be found in rural settings.

Car Weight

Car size can be expressed by different measures: wheelbase (the distance between the front and rear axles), track width (the distance between the left and right wheels), engine size, weight, and so on. Because all these variables tend to be very highly correlated with one another, it is frequently difficult to distinguish statistically their unique effects. It seems reasonable to believe that each of these factors has a differential effect on the likelihood of being involved in different types of crashes. In one research report, for example, the National Highway Traffic Safety Administration (NHTSA) found that a combination of track width and center of gravity was the best predictor of vehicle rollover.

We developed three sets of models corresponding to the three measures of car size readily available to us: wheelbase, weight, and engine displacement. The full results of these analyses are in appendix V. Here we are concerned with the relationship between car weight and crash involvement. Figure 2 summarizes this relationship by comparing the odds for cars of different weights with the odds for a 2,678-pound car (the median car weight in the sample) of being in a crash (for each of the three crash types).

For each crash type, weight had a statistically significant effect, but the effect was quite small for two-vehicle crashes and for single-vehicle crashes where a rollover did not occur. The odds ratio curves for these two crash types are almost mirror images of each other. The lightest and the heaviest cars were slightly more likely to be involved in two-vehicle crashes than were midweight cars and slightly less likely to be involved in single-vehicle nonrollover crashes.

The connection between car weight and rollover crashes, however, was substantially stronger. The lighter the car, the greater were its odds of rolling over. For example, the average 2,000-pound car was nearly three times as likely to be involved in a single-vehicle rollover crash as the average 4,500-pound car.

This finding needs some qualification. Factors other than car weight are probably more directly related to rollover propensity but, as we noted earlier, the high intercorrelation of the various measures of car size make the relationships difficult to disentangle statistically. When we used car size measures other than weight in our analyses, we found a stronger connection with rollover likelihood for wheelbase than for weight. (See appendix V.) Research by NHTSA has demonstrated that rollover propensity is related to several other vehicle factors, such as track width, weight distribution, and braking stability.

Light Trucks and Vans

Our analysis of the crash involvement probability of light trucks and vans yielded many of the same findings as our analysis of passenger cars. Full details of the analysis are presented in appendix IV. Driver age and driving history remained by far the best predictors of crash involvement. Drivers involved in single-vehicle light truck crashes were one-and-a-quarter to one-and-a-third times more likely to be male. However, whereas for passenger cars driver gender appeared to be irrelevant to involvement in two-vehicle collisions, women were slightly but significantly more likely to be involved in a light truck two-vehicle collision than were men.

As with passenger cars, the vehicle factors were much less important than the driver factors. However, older light trucks were significantly more likely to be involved in all types of crashes. The relationship between light truck weight and crash involvement was weaker than for passenger cars. We found no relationship in two-vehicle crashes and only a marginally significant relationship in single-vehicle crashes. The connection between light truck weight and crash involvement was relatively strongest for rollover crashes. As with passenger cars, the lightest of these vehicles were more likely to roll over. However, all three alternative measures of size again contributed relatively little to predictions of crash involvement, and vehicle weight ranked either second or third among the size measures in all light truck models. (See appendix V.)

Induced Exposure

The use of indirect methods to estimate the risk of being involved in a highway crash, variously referred to as “induced” or “quasi-induced” exposure, dates back at least to the 1960’s. The method is based on calculating the ratio of at-fault drivers or vehicles to not-at-fault drivers or vehicles in two-vehicle accidents contained in police accident reports. Its underlying assumption is that the not-at-fault drivers and vehicles constitute a representative sample of the drivers, vehicles, and driving conditions and their interactions for the geographical area being examined.

On the assumption that not-at-fault drivers represent the general population of drivers, the ratio of at-fault to not-at-fault drivers yields an estimate of the over- or underinvolvement of different levels of that dimension in highway crashes. R. W. Lyles et al. offer an example of estimating how much male drivers are overrepresented in interstate highway accidents. In 1988, 11,335 pairs of drivers were involved in two-vehicle accidents in which fault was assigned on Michigan interstate highways. Of the at-fault drivers, 8,366 (73.8 percent) were male, whereas only 7,528 (66.4 percent) of not-at-fault drivers were male. Males were 1.1 times (73.8/66.4) overinvolved in interstate accidents relative to their presence on these highways. Females, however, represented 26.2 percent of at-fault drivers and 33.6 percent of not-at-fault drivers. Their “involvement ratio,” therefore, was 0.78 (26.2/33.6). Lyles et al. conclude, therefore, that when the calculation is adjusted for exposure, males caused interstate highway accidents at a rate 1.4 (1.1/0.78) times that of females.

This indirect approach has a number of advantages. Foremost among them is the ability to define accident exposure in terms of any driver, roadway, or vehicle characteristic reported in the accident data base being used. For example, given a sufficiently large data base, a researcher could estimate the crash involvement risk of female drivers under 25 years of age on rural roads in the dark and could determine whether female drivers are more likely than males to become involved in accidents under such conditions.

Any attempt to measure exposure directly, at this level of detail, would be prohibitively expensive.

Two major uncertainties are associated with induced exposure measures, however. The first is common to any state or regional data base and involves whether the data being used to form estimates adequately represent other geographical areas and, hence, the universe to which they are being extrapolated. In the case of induced exposure, the concern is not that the absolute count within subcategories of drivers of vehicles may vary from state to state—that is, that there might be, for example, more light trucks or more elderly drivers in one state than another. (This is most likely the case.) Rather, we are concerned with the ratios of at-fault to not-at-fault drivers, however the absolute counts may vary geographically. It is assumed that these ratios would be less prone to substantial variation from one area to another. In other words, it is much less probable that light trucks or elderly drivers have different driving-related attributes from one state to another than that their numbers vary geographically.

Nevertheless, to test the seriousness of this concern, we compared by age and gender the accident involvement ratios we obtained from the 1990 North Carolina data base we used for our study with ratios we calculated from a data base of police-reported accidents in Michigan in 1987. In both cases, we looked strictly at two-vehicle accidents in which only one driver was considered at fault. The results are presented in table I.1.

While there is not absolute agreement between the involvement ratios derived from the two data bases (the greatest discrepancy being between the results for the oldest, male drivers), the figures are quite close and the trends are remarkably similar.

The second uncertainty associated with the use of induced exposure is potentially more serious and is less easily tested. Threats to the validity of these estimates have been suggested, in particular the possibility of systematic bias. It is possible that certain driver or vehicle types are more likely to be identified by police as being at fault in a two-vehicle accident. For example, in an ambiguous situation the police may be more inclined to place blame on a young driver. On a different plane, it is possible that the not-at-fault driver in a two-car accident is not totally without blame. This driver’s ability to avoid accidents may be less than average; hence, he or she may be more “accident-prone.” To the extent that accident-proneness exists, the not-at-fault population less than perfectly represents of the universe of drivers and vehicles.

The existence of such a bias cannot be tested directly, but indications of whether its existence effectively distorts the estimates derived from induced exposure methods can be tested both by comparisons internal to the data base and by comparing the estimates with those derived from direct exposure measurements. Lyles et al. used internal tests to determine whether discernible bias entered their estimates. They reasoned that, if not-at-fault drivers represent drivers on the road, we should find variations in their characteristics that are related to different driving conditions. For example, we know from direct observation that drivers on major freeways are less likely to be female than drivers on more local roads. Induced exposure findings should be consistent with observation and, in fact, Lyles et al. found that 63 percent of not-at-fault drivers on U.S.-numbered routes in Michigan were male, as opposed to 57 percent on local streets. Furthermore, male at-fault drivers should strike approximately the same proportion of male not-at -fault drivers as do female at-fault drivers. This turned out to be the case. On U.S.-numbered routes, male at-fault drivers struck male drivers 63 percent of the time and females 37 percent of the time. Female at-fault drivers struck male drivers 62 percent of the time and females 38 percent of the time.

Lyles et al. offer a series of similar crosschecks for the mutual independence of the at-fault and not-at-fault populations across a variety of conditions, including different roadway types, years, times of day, and driver age categories. While there were wide variations in the distribution of driver characteristics among different driving conditions, different subsets within the same condition yielded nearly identical estimates of exposure.

Comparisons with estimates formed from direct exposure methods are less straightforward. We can, for example, obtain the distribution of licensed drivers by gender from any state. However, we know that this provides a biased estimate of drivers on the road. Using the Department of Transportation’s (DOT’s) 1990 National Personal Transportation Survey (NPTS), we found that over 15 percent of all women licensed drivers over age 75 had not driven at all in the previous year (as contrasted with less than 1 percent of licensed women drivers between 25 and 34).

NPTS itself is perhaps our most comprehensive direct exposure source, and it is particularly valuable in discerning trends in travel habits in the United States over time. Yet, besides being subject to the weaknesses of human recollection, it is relatively insensitive to the quality of driving exposure. While its estimates of miles driven by respondents may be quite reliable, it cannot estimate the portion of miles driven under different conditions to the level of detail that, arguably, an induced exposure method can.

Nevertheless, comparisons of induced exposure results with those derived from more direct methods are informative. Accordingly, we compared our estimates of age and gender distribution with those from NPTS. The comparisons are presented in table I.2 in terms of the percentage of vehicle miles driven (from NPTS) and the percentage of exposure to accidents as derived from our data.

A comparison of the estimates of vehicle miles driven and of accident exposure illuminates the differences between the two measures. Put simply, not all miles are equal. It has been demonstrated that men tend to drive substantially more freeway miles than women and that freeway miles are the safest of all miles driven. These considerations are reflected in the substantial difference between the overall gender distribution estimates derived from the two measures. While men may drive nearly twice as many miles as women (65 percent versus 35 percent of all miles; see table I.2), these are more often highway miles and, thus, are substantially safer, with the result that their accident exposure is only moderately higher than women’s using the induced exposure approach. Similarly, young drivers drive fewer miles than middle-aged drivers, but their miles are considerably more dangerous both because of their timing (nights and weekends) and because of driver inexperience and risk-taking behavior. These relationships are shown in table I.2: using the induced exposure method, men age 45-54 represent 6 percent of the population at risk while men under 25 have twice the exposure (14 percent), whereas using NPTS the percentages are only slightly different.

In summary, the induced exposure method offers a means of estimating the relative risks of different types of drivers, vehicles, and driving conditions at a level of refinement that cannot be approximated in practice by any direct measurement technique. It yields summary estimates of exposure that differ from the global estimates of direct measures such as vehicle miles traveled, but the differences appear to be reasonable in view of the larger number of factors taken into consideration by the induced exposure method. Its estimates of relative risk appear to be quite stable across different geographic, driver, vehicle, and roadway conditions. In classic measurement terms, while the method’s predictive validity has not been empirically demonstrated, evidence exists to support its reliability and construct validity. Its practical utility is beyond question.

The North Carolina Data File

The data set for our analysis was created from data tapes, provided by North Carolina’s Division of Motor Vehicles, containing information on accidents in North Carolina for calendar year 1990. The information is derived from accident report forms filled out by investigating officers at accident scenes. The Highway Safety Research Center at the University of North Carolina added technical information concerning vehicles (such as vehicle weight, wheelbase, and engine size), which was obtained by decoding the vehicle identification numbers recorded on the accident forms. We also merged information, collected by the Division of Motor Vehicles, on drivers’ violation histories.

The data file contained one record for each individual unit (vehicle, pedestrian, bicyclist, and so on) involved in the accident. Table II.1 provides counts of the types of individual records that were contained in the North Carolina file. Table II.2 provides the distribution of accident types in the data base. For the purposes of the current study, an accident was considered a single- or two-vehicle accident on the basis of the count of the number of in-motion, motorized vehicles involved. We excluded the accident category labeled “Other” in table II.2, which may contain single- or two-vehicle accidents if the type of vehicle involved was not reported or was a heavy truck, bus, or farm vehicle. The “Other” category also contains accidents with three or more in-motion vehicles.

Logistic Regression Analysis for Passenger Car Crash Involvement

The outcome variable for the logistic regression equations was a dichotomous indicator of fault, coded “1” for at-fault and “0” for not-at-fault drivers. For all the equations presented here, both single- and two-vehicle accidents, the comparison group is not-at-fault drivers in two-vehicle accidents. A driver was considered at fault if the investigating police officer checked one or more violations in the checklist provided on the North Carolina accident report form. (In two-vehicle accidents, cases were excluded if no violation was reported for either driver or if both drivers had violations.) driver age, including a squared term to capture the curvilinear relationship between age and accident involvement; driver gender, with males coded “1” and females coded “0”; driver violation history, with four mutually exclusive categories: no previous traffic violations, one or more previous violations not involving alcohol, at least one alcohol violation (may also include nonalcohol violations), and violation history unknown (all out-of-state drivers and some North Carolina drivers are in this category). In the models shown, the three categories given are in contrast to the group having alcohol-related violations; vehicle age, last two digits of the vehicle model year; vehicle curb weight, expressed in hundreds of pounds and including a squared term to capture the curvilinear relationship between vehicle weight and accident involvement.

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Contrast group for violations is “Has alcohol violation.”

Vehicle weight is calibrated in hundredweights.

Logistic Regression Analysis for Light Truck and Van Crash Involvement

The outcome variable for the logistic regression equations was a dichotomous indicator of fault, coded “1” for at-fault and “0” for not-at-fault drivers. For all the equations presented here, both single- and two-vehicle accidents, the comparison group is not-at-fault drivers in two-vehicle accidents. A driver was considered at fault if the investigating police officer checked one or more violations in the checklist provided on the North Carolina accident report form. (In two-vehicle accidents, cases were excluded if no violation was reported for either driver or if both drivers had violations.) driver age, including a squared term to capture the curvilinear relationship between age and accident involvement; driver gender, with males coded “1” and females coded “0”; driver violation history, with four mutually exclusive categories: no previous traffic violations, one or more previous violations not involving alcohol, at least one alcohol violation (may also include nonalcohol violations), and violation history unknown (all out-of-state drivers and some North Carolina drivers are in this category). In the models shown, the three categories given are in contrast to the group having alcohol-related violations; vehicle age, last two digits of the vehicle model year; vehicle curb weight, expressed in hundreds of pounds and including a squared term to capture the curvilinear relationship between vehicle weight and accident involvement.

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Contrast group for violations is “Has alcohol violation.”

Vehicle weight is calibrated in hundredweights.

Alternative Definitions of Vehicle Size

Models containing three alternative definitions of vehicle size were fitted to the data. These were vehicle weight (in hundreds of pounds), engine size (displacement expressed in cubic inches), and wheelbase (the distance between the axles in inches). To allow comparisons of their relative importance in predicting crash involvement, the improvement in goodness of fit for each model over the base model (including driver age, violation history, gender, and vehicle age) is presented in tables V.1 and V.2.

As noted in the text, (1) with the exception of single-car rollovers, contributions to the model, though statistically significant in most cases, are small in comparison to most variables in the base model, and (2) different definitions are stronger depending upon the crash type being predicted.

Comments From the Department of Transportation

The following are GAO’s comments on DOT’s June 20, 1994, letter.

GAO Comments

1. We share with DOT the belief that every reasonable effort should be made to reduce the incidence of rollover crashes, which, as we noted in our report, are second only to frontal collisions in deadliness. We would be as concerned as DOT if our conclusion, that car size is substantially less predictive of rollover crashes than are driver characteristics, were misinterpreted to diminish the importance of efforts to reduce the rollover propensity of vehicles.

The analyses we performed differ significantly from NHTSA’s rollover research, but our conclusions are not in conflict. Our concern was with the relative contribution of car size to crash involvement. We examined this relationship in a general model that combined crash types and then separately, using the traditional analytic taxonomy of multiple vehicle, single-vehicle nonrollover, and single-vehicle rollover crashes. NHTSA, in contrast, attempted to identify the factors that differentiated single-vehicle rollover from nonrollover crashes. The vehicle factors it examined included a number of constructs derived from laboratory measurements, such as tilt table ratio, side pull ratio, and critical sliding velocity. The single area of overlap between the NHTSA analyses and ours was in the inclusion of wheelbase in NHTSA’s models and in one of our models.

It is not surprising, therefore, that we arrived at different conclusions regarding the importance of different vehicle characteristics relative to driver characteristics. Nevertheless, our findings also support the relatively greater importance of vehicle characteristics in rollover crashes than in other crash types. We found that lighter vehicles were more likely to be involved in single vehicle rollovers. We further found that wheelbase was a better predictor of rollover crashes than weight. 2. DOT made two suggestions for additional analyses to supplement our single-vehicle rollover model. First, agency researchers suggested that we include in our model some roadway characteristics that were beyond the scope of the research originally requested. They also suggested that we treat all single-vehicle crashes as one crash type and then perform a second-level analysis to identify the factors that distinguish between rollover and nonrollover crashes.

We performed these analyses and concluded that, while they provided important additional information about the dynamics of rollover crashes, they did not substantially alter our conclusions about the relative importance of the driver and vehicle characteristics we examined.

To respond to the first suggestion, we added two roadway variables to all our models: whether the roadway was curved or straight and whether the crash occurred in a rural or urban setting. The results of these analyses are provided in appendix VII (passenger cars) and appendix VIII (light trucks and vans). As anticipated, these roadway characteristics generally contributed significantly to the predictive power of the models. Single-vehicle crashes (rollover and nonrollover) are more likely to occur on rural and on curved roadways. Two-vehicle car crashes are less likely to occur on rural roads. The addition of these predictors, however, did not change the predominant importance of driver factors over vehicle weight in predicting crash involvement.

We also constructed a model combining both types of single-vehicle accidents. We included the results of this model in appendixes VII and VIII.

As DOT anticipated, the single-accident model yielded results consistent with our earlier findings, that driver age and violation history play the strongest roles in involvement in single vehicle crashes, with little contribution from the various vehicle characteristics. The aggregate model, however, also finds the contribution of weight to single-vehicle accidents nonsignificant. This is the net effect of the opposing influence of weight in rollover and nonrollover accidents. As our original analyses demonstrated, heavier cars are more likely to be involved in nonrollover crashes and less likely to be involved in rollovers.

We constructed a second set of models (one each for passenger cars and for light trucks and vans) to distinguish between rollover and nonrollover single-vehicle accidents. The results of these analyses are presented in appendix IX. The model produced the results anticipated by DOT—namely, that roadway characteristics and, to a lesser extent, vehicle weight are better predictors of whether a single-vehicle accident involves a rollover than the driver characteristics in our model, although the analysis did find that younger drivers were significantly more likely to be in a rollover than a nonrollover crash.

Like the analysis, the interpretation of the models must be in two stages. The findings suggest that driver characteristics predominate over vehicle characteristics in placing a vehicle in a likely single-vehicle crash situation.

Whether the resultant crash (if one does occur) involves a rollover is more determined by roadway and vehicle considerations.3. While DOT considers induced exposure an “excellent” method for measuring involvement risk in two-vehicle crashes, it expressed some cautions about its use for single-vehicle crashes. In particular, DOT suggested that the mix of light truck types (pickups, vans, and sport-utility vehicles) is different in urban and rural settings, and therefore three different light truck analyses should be performed. We agree that such analyses could provide valuable information, but we believe that they would unnecessarily expand the scope of this report. We reviewed the relative likelihood of fatal accidents in different types of light trucks and vans in a previous report. The additional analyses we performed, at DOT’s suggestion, that control for the urban and rural difference also address this concern. (See appendixes VII and VIII.) 4. DOT further suggested that we perform additional tests of the ability to generalize from the induced exposure method by comparing results from a larger number of state accident data bases. Our comparison of accident involvement ratios in North Carolina and Michigan (the two usable data bases readily available to us) was intended only to illustrate the relative consistency and reasonableness of the results obtained from applying this methodology. Many more such comparisons will need to be made before the exact parameters of the method’s applicability can be defined. Nevertheless, the results obtained by different researchers over the years from this approach to defining exposure are a strong argument for its general utility.

DOT also suggested we include some additional details concerning our analyses and references to other related work performed by NHTSA and other researchers. We have incorporated these suggestions where appropriate. 5. This statement has been changed in the text. See page 2.

Logistic Regression Analysis Including Roadway Characteristics: Passenger Cars

The outcome variable for the logistic regression equations was a dichotomous indicator of fault, coded “1” for at-fault and “0” for not-at-fault drivers. For all the equations presented here, both single- and two-vehicle accidents, the comparison group is not-at-fault drivers in two-vehicle accidents. A driver was considered at fault if the investigating police officer checked one or more violations in the checklist provided on the North Carolina accident report form. (In two-vehicle accidents, cases were excluded if no violation was reported for either driver or if both drivers had violations.) driver age, including a squared term to capture the curvilinear relationship between age and accident involvement; driver gender, with males coded “1” and females coded “0”; driver violation history, with four mutually exclusive categories: no previous traffic violations, one or more previous violations not involving alcohol, at least one alcohol violation (may also include nonalcohol violations), and violation history unknown (all out-of-state drivers and some North Carolina drivers are in this category). In the models shown, the three categories given are in contrast to the group having alcohol-related violations; vehicle age, last two digits of the vehicle model year; vehicle curb weight, expressed in hundreds of pounds and including a squared term to capture the curvilinear relationship between vehicle weight and accident involvement; rural location, coded “1” for rural locations and “0” for mixed or urban curved roadway, coded “1” if curved, “0” otherwise.

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Quadratic term removed since main effect only achieves significance without squared term.

Logistic Regression Analysis Including Roadway Characteristics: Light Trucks and Vans

The outcome variable for the logistic regression equations was a dichotomous indicator of fault, coded “1” for at-fault and “0” for not-at-fault drivers. For all the equations presented here, both single- and two-vehicle accidents, the comparison group is not-at-fault drivers in two-vehicle accidents. A driver was considered at fault if the investigating police officer checked one or more violations in the checklist provided on the North Carolina accident report form. (In two-vehicle accidents, cases were excluded if no violation was reported for either driver or if both drivers had violations.) driver age, including a squared term to capture the curvilinear relationship between age and accident involvement; driver gender, with males coded “1” and females coded “0”; driver violation history, with four mutually exclusive categories: no previous traffic violations, one or more previous violations not involving alcohol, at least one alcohol violation (may also include nonalcohol violations), and violation history unknown (all out-of-state drivers and some North Carolina drivers are in this category). In the models shown, the three categories given are in contrast to the group having alcohol-related violations; vehicle age, last two digits of the vehicle model year; vehicle curb weight, expressed in hundreds of pounds; rural location, coded “1” for rural locations and “0” for mixed or urban curved roadway, coded “1” if curved, “0” otherwise.

S.E.

Prob.

Coeff.

S.E.

Prob.

Coeff.

S.E.

Prob.

Coeff.

S.E.

Prob.

Quadratic term for weight removed since main effect only achieves significance without squared term.

Predicting Rollover Versus No Rollover Among Single-Vehicle Accidents

The outcome variable for the logistic regression equations was a dichotomous indicator of vehicle rollover, coded “1” for rollover and “0” for nonrollovers. driver age, including a squared term to capture the curvilinear relationship between age and accident involvement; driver gender, with males coded “1” and females coded “0”; driver violation history, with four mutually exclusive categories: no previous traffic violations, one or more previous violations not involving alcohol, at least one alcohol violation (may also include nonalcohol violations), and violation history unknown (all out-of-state drivers and some North Carolina drivers are in this category). In the models shown, the three categories given are in contrast to the group having alcohol-related violations; vehicle age, last two digits of the vehicle model year; vehicle curb weight, expressed in hundreds of pounds and including a squared term to capture the curvilinear relationship between vehicle weight and accident involvement. rural location, coded “1” for rural locations and “0” for mixed or urban curved roadway, coded “1” if curved, “0” otherwise.

Coeff.

S.E.

Prob.

Coeff.

S.E.

Prob.

Quadratic term removed since main effect only achieves significance without squared term.

Major Contributors to This Report

Program Evaluation and Methodology Division

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