banner of Methodology for Determining Motorcycle Operator Crash Risk and Alcohol Impairment

2. Method

Literature Search

The literature was reviewed for previous work in several areas including motorcycle fatal and injury crash statistics, alcohol involvement in crashes, population-at-risk studies, roadside sampling, crash risk studies, motorcycle riding simulators, alcohol impairment, and other factors in injury outcome (e.g., crash type, helmet use). A detailed bibliography is found in Appendices A and B of Volume II: Literature Review Report.

Expert Panel

Another important part of this project was an expert panel meeting to discuss the issues involved in satisfying the project objectives. The panel’s selection was in part driven by the literature search. That is to say, the project staff attempted to involve many of the very researchers who had previously worked in one or more of the areas in question. These included motorcycle safety, alcohol, survey technique, law enforcement, risk assessment, and related fields. Because of the sensitive nature of field data collection of a particular type of vehicle operator, panel members included representatives from motorcycle safety and motorcycle rider organizations. A suggested panel list was submitted to the Contracting Officer’s Technical Representative (COTR) for review and approval. The COTR suggestions were incorporated and a workshop was conducted with the selected panel members.

Determining Relative Risk

Prior to the introduction of the methodologies discussed by the panel, it is important for the reader to understand the concepts of crash data and comparison data and how each is necessary to understand the potential effects of alcohol impairment on motorcycle operation.

A common measure of the influence of alcohol on crash risk is that of the “relative risk” of crashing while impaired, compared to that of crashing while unimpaired. The most commonly used relative risk measures for drinking and driving show the risk of being involved in a fatal crash at a given BAC. These relative risk values are created by determining the proportion of drivers in fatal crashes at a given BAC and dividing that by the proportion of non-crash involved drivers in the population at risk who are operating at that same BAC. The result is the relative risk of being involved in a fatal crash at that BAC level.

Crash Data Variable over Population-at-risk data variable equals Relative Risk

Table 1 shows the relative risk of being involved in a crash as reported by Compton et al. (2002).  The relative risk of crash for automobile drivers begins to increase at low BAC levels and increases more than two-fold at BACs ≥ .07 g/dL.

Table 1. Relative Crash Risk by BAC

BAC Level
Crash Risk
BAC Level
Crash Risk

.00

1.00

.13

12.60

.01

1.03

.14

16.36

.02

1.03

.15

22.10

.03

1.06

.16

29.48

.04

1.18

.17

39.05

.05

1.38

.18

50.99

.06

1.63

.19

65.32

.07

2.09

.20

81.79

.08

2.69

.21

99.78

.09

3.54

.22

117.72

.10

4.79

.23

134.26

.11

6.41

.24

146.90

.12

8.90

.25+

153.68

By plotting the relative risk for a range of BAC levels, the increasing effects of alcohol on crash risk can be observed as BAC increases. Figure 1 shows a relative risk curve from Compton et al.

Relative Risk Estimate line graph

1. Relative Risk Estimate

The same basic concept could also be used to create curves showing relative risk of other potential consequences of alcohol impairment on motorcycle operation, such as injury crashes. 

It would also be possible to develop risk curves for simulated crashes using a motorcycle simulator, or for performance errors (e.g., lane exceedance) on a simulator or closed course.  However, due to differences between these settings and real-world operation, data from simulators and closed-course operation are generally considered more indicative of impairment than true crash risk.

As will be summarized below, the methodologies for understanding the effects of alcohol impairment on motorcycle operation involve collecting both crash data and population-at-risk data. Where potential methodologies do not result in the collection of both types of data, methodologies for collecting crash data must be matched with methodologies for collecting data on the population-at-risk. Because population-at-risk data is used for comparison purposes, it will be referred to in this report as “comparison data.”

Table 2 shows methodologies identified as ways to collect data necessary to understand the effects of alcohol impairment on motorcycle operation. The table begins with methodologies that would provide laboratory data on impairment, followed by studies which would provide new crash and comparison data, methodologies that would provide new crash data, methodologies that would provide new comparison data, and finally a methodology that could be done entirely using existing data.

Table 2. Brief Description of Methodologies

Method

Description

Studies Providing Data on the Impairing Effects of Alcohol

Simulation Study

Using a motorcycle simulator with alcohol-dosed subjects. Rider impairment would be measured by comparing performance within rider at various BAC levels.

Closed-Course Study

Alcohol-dosed subjects would ride a motorcycle on a closed course. Rider impairment would be measured by comparing performance within rider at various BAC levels.

Field Studies Providing Both Crash and Comparison Data

Contemporary Case Control

BAC and other information concerning crashes are recorded at the scene (as much as possible) and afterward. Later, similar data is recorded for non-crash-involved riders at or near the same location, time of day, and day of week.

Cohort Study

A sample of riders would be selected. Alcohol use (e.g., BACs while riding) would be recorded over time, along with data on any crashes that occur. Data would be collected using various methods, which could include surveys, diaries, and use of an instrumented motorcycle.

Emergency Department

Interviews conducted with crash-involved riders and BAC testing takes place at a hospital. This data would then be compared to BACs and interviews (if available) to on-road population-at-risk data taken from a different source.

Survey Study

Traditional survey techniques (e.g., phone, mail, or in-person surveys) would be used to collect self-reported data from riders concerning alcohol use and crash histories. Height, weight, and number of drinks would be used to estimate BACs of riders when they were not involved in crashes versus when they were involved in crashes.

Studies Providing Crash Case  Data

Fatal Crash Records

Crash data for motorcyclists would come from FARS. This data would then be compared to BACs and other information (if available) to on-road population-at-risk data taken from a different source.

Injury Crash Records

Crash data would come from records of motorcycle injury crashes. This data would then be compared to BACs and other information (if available) to on-road population-at-risk data taken from a different source.

Studies Providing Comparison Data

Geo-General Comparison Data

Population-at-risk data would come from general roadside surveys, not from specific sites of previous crashes. Oversampling of population-at-risk data makes it possible to control statistically for factors such as age. Crash data would come from elsewhere (e.g., FARS).

Geo-Specific Comparison Data

Population-at-risk data would come from visits to specific sites of previous crashes found in archival data. Data is then compared to data from another source (e.g., FARS).

Fuel Station Survey

Similar to roadside collection of BACs and other data except that survey takes place when riders stop to refuel. Data is then compared to data from another source (e.g., FARS).

Study Using Existing Data for Crash and Comparison Cases

Induced Exposure

Using archival records (e.g., FARS) BAC data from crash-involved at-fault riders is compared to crash-involved riders deemed not to be at fault (population-at-risk).  The at-fault riders are then compared to the non-at-fault riders to yield a measure of risk.

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