The effects of vehicle model and driver behavior on risk

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Abstract

We study the dependence of risk on vehicle type and especially on vehicle model. Here, risk is measured by the number of driver fatalities per year per million vehicles registered. We analyze both the risk to the drivers of each vehicle model and the risk the vehicle model imposes on drivers of other vehicles with which it crashes. The “combined risk” associated with each vehicle model is simply the sum of the risk-to-drivers in all kinds of crashes and the risk-to-drivers-of-other-vehicles in two-vehicle crashes. We find that most car models are as safe to their drivers as most sport utility vehicles (SUVs); the increased risk of a rollover in a SUV roughly balances the higher risk for cars that collide with SUVs and pickup trucks. We find that SUVs and to a greater extent pickup trucks, impose much greater risks than cars on drivers of other vehicles; and these risks increase with increasing pickup size. The higher aggressivity of SUVs and pickups makes their combined risk higher than that of almost all cars. Effects of light truck design on their risk are revealed by the analysis of specific models: new unibody (or “crossover”) SUVs appear, in preliminary analysis, to have much lower risks than the most popular truck-based SUVs. Much has been made in the past about the high risk of low-mass cars in certain kinds of collisions. We find there are other plausible explanations for this pattern of risk, which suggests that mass may not be fundamental to safety. While not conclusive, this is potentially important because improvement in fuel economy is a major goal for designers of new vehicles. We find that accounting for the most risky drivers, young males and the elderly, does not change our general results. Similarly, we find with California data that the high risk of rural driving and the high level of rural driving by pickups does not increase the risk-to-drivers of pickups relative to that for cars. However, other more subtle differences in drivers and the driving environment by vehicle type may affect our results.

Introduction

There are two general methods to analyze the effect of vehicle design on safety. The first is based on laboratory tests of the ability of a vehicle to protect its occupants, once a serious crash occurs (“crashworthiness”; e.g. the National Crash Assessment Program (NCAP) and tests by the Insurance Institute for Highway Safety (IIHS)) and the handling of a vehicle and its ability to avoid a crash (“crash avoidance”; such as Consumer Reports’ braking and handling tests). However, these tests are quite expensive and therefore are usually conducted on a single vehicle from a particular model. In addition, these tests cannot replicate the variety in the kinds of crashes (e.g. crashes at different angles with different kinds of vehicles or roadside objects) nor do they address the likelihood of different kinds of crashes (e.g. for the driver to lose control over the vehicle). The second method is to utilize data from real-world crashes. The practical limitation of this approach is that it is very difficult to separate the effect of the vehicle from the effect of the driver and driving environment in analyzing fatalities or injuries.

In this study, we use data on real-world crashes to explore the role of vehicle design in traffic fatalities in the hope of understanding the effect design differences have on safety. The fundamental problem in assessing the risks associated with vehicle designs is that both vehicle design and driver behavior (how, where and when the vehicle is driven and how it is maintained) affect risk. Various analyses approach this fundamental difficulty in different ways and none are completely satisfactory. In addition, vehicle design can influence not only its crash avoidance and crashworthiness, but also whether it endangers the occupants of other vehicles with which it may crash (“compatibility”).

The risks related to vehicle design depend on many characteristics, including specific safety technologies and features such as frontal height and stiffness, as well as gross dimensions like size and mass. These vehicle characteristics tend to be correlated with each other in historical data and they also can correlate with driver behavior. For example, higher quality vehicles may tend to be purchased by more careful drivers. And vehicle size has been strongly correlated with vehicle mass, although this relationship may be changing with the introduction of new mass reduction technologies.

Critical issue for this analysis is to evaluate vehicle design aspects of traffic deaths. Our method for addressing this emphasizes the dependence of traffic risks on individual vehicle models. The motive for this approach is to help make the analysis more transparent by bringing knowledge about individual vehicle models, including characteristics and behavior of their drivers, as well as where they are driven, to bear.

Section snippets

Data and methods

In this analysis, we use the word risk as a technical term, defining it as driver deaths per year per million registered vehicles (similar to IIHS). We focus on driver deaths, because that eliminates variations in the number of passengers among vehicle types and models that could affect our results. Following Joksch et al. (1998), we are concerned with two risks, the “risk-to-drivers” of the subject vehicle model (or vehicle type) and the “risk-to-drivers-of-other-vehicles” that crash with the

Risks by vehicle type and model

The risks to a vehicle's own driver and to drivers of other vehicles are shown by vehicle type in Fig. 1, Fig. 2. Both figures present the same information, but in a different format. Fig. 1 stacks the two risks on top of each other and allows easy comparison of the combined risk, while Fig. 2 plots the risk-to-drivers on the x-axis and the risk-to-drivers-of-other-vehicles on the y-axis. The diagonal lines in Fig. 2 showing combined risks of 110 and 130 illustrate the concept; vehicle types

Minivans

Although minivans have unibody structure rather than the stiff frames of pickup trucks (as discussed below), as a group they tend to have higher risk-to-others than most cars. This higher risk-to-others is likely due to the higher mass and front end, relative to cars.

Cars

There is a wide range in risk-to-driver of cars, with risk appearing to increase as mass decreases. However, mass alone is only a modest predictor of risk in all types of crashes. Resale value at 5 years of age or so is the best

Driver behavior

Driver behavior and vehicle design both play major roles in the risk of death in traffic crashes. (To repeat, “driver behavior” is shorthand for how, where and when the vehicle is driven and how it is maintained.)

Several behavioral characteristics have been identified as associated with unusually high risk, although the quality of data is often poor. Certain state crash databases, which contain information on both vehicle model and driver characteristics, have been used to account for driver

Conclusions

We have shown that the most popular recent car models driven in the US exhibit widely different levels of risk-to-driver, ranging over a factor of five and of risk-to-others, ranging over a factor of two. Some of the differences can be ascribed to dangerous driver behavior, three components of which we examine: rural driving, a pattern of illegal driving and driving by the young and old. Rather than quantifying the roles of these three components in detail, we show that little of the observed

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