ARSRPE Conference Paper Database

Establishing Methods to Understand Human Error at Intersections

Brace, Charlotte, Lenné, Michael, Archer, J



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 contributory factors. 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.