Establishing Methods to Understand Human Error at Intersections
Road deaths and injuries are preventable by understanding the causes of accidents and developing
effective countermeasures to manage these issues. Research has suggested that at least 70 per
cent, and possibly as many as 92 percent, of all traffic accidents are attributable to the human element
in traffic (Sanders and McCormick, 1992). Other research by Hakkinnen and Luoma (1991) suggests
that traffic accidents may be preceded, on average, by as many as 75,000 errors. Therefore, an
understanding of the types or errors that occur, and the underlying contributory mechanisms, is critical
for successful safety improvement.
When considering human error, a holistic approach needs to be taken that contemplates the way in
which all of the elements in the system are designed, how they interact, and the consequences for
errors. For example, the role of the driver and other road users (e.g. training, knowledge and
behaviour), the vehicle (e.g. the extent to which it is maintained), and the road environment (e.g. the
road layout) all need to be examined.
The occurrence of human-errors in complex systems such as traffic almost always involves multiple
errors and contributory factors at various levels of system operation. This has been established in
other complex systems including aviation and medicine. While human-error frameworks have been
developed to guide the incident investigation process and to develop preventative programs in various
other domains, their application in road-transport has been largely neglected. As a result, relatively
little is currently known about the different errors that road users make and their associated
This research is a practical approach being conducted to gain a deeper understanding of the
relationship between human error and safety critical events in the road traffic system. The broader
aim of the work is to develop a taxonomic structure for classifying driver errors and their associated
contributory factors, which has the potential to be used for guiding the collection of data as well as for
classifying errors and their causes for subsequent analysis.