Countermeasures are of two types: those that prevent crashes and those that reduce loss when a crash has occurred or, simply, crash prevention and crash protection measures. (By definition crash prevention measures are more beneficial than crash protection measures; when a crash is prevented the harm is zero.)

 

Statistics, however, can be hard to interpret and the questions that prompt their compilation harder to answer than might be expected. Traffic safety is largely based on measures of rates: harm—deaths, injuries or property damage—divided by some indicator of exposure to the risk of this harm. As Mr. Evans notes, “Simple counts are almost never used. The annual count of fatalities is a rate, namely, the number of fatalities per year. Rates related to driver deaths include the number of driver deaths per head of population, per registered vehicle, per licensed driver, or per same distance of travel.”

 

Mr. Evans says that the rate to be selected depends on the question being asked and the data available and that understanding comes from ensuring that the relationship between the rate and the question being asked is exactly specified. Exposure is also problematic, an appropriate measure always depending on the question being addressed.

 

The implications of rate measuring and identifying risk factors is that crashes are not “just random events,” notes Mr. Evans, “[but] in fact crashes do have important random components.” A mathematical model—the Poisson process— applied where all drivers have the same risk of crashing at all times produces an annual result in which all do not have the same number of crashes because of randomness.

 

The notion of accident-prone individuals is discredited as a possible cause, empirical studies showing that drivers with a large number of crashes in one period did not necessarily have an above average number of crashes in subsequent periods. This is not to say, however, that individual drivers or groups of drivers (20-year-old male drivers, for example) cannot be reliably identified by other methods as posing greater than average driving risks. Such groups can, and according to Mr. Evans, should be identified in the interests of greater traffic safety.

 

Quantification by observational data—statistics—provides the highest level of knowledge for resolving traffic safety problems. But it is not always attainable and indeed not always needed. Knowledge not based on observational data (the lowest level of knowledge) and knowledge only hinted at by observational data (the second level) can support reasonable and effective countermeasures. For example, most people would probably agree “Look before crossing”, is a rule without need for justification based on an underlying set of observed data.