THE HUMAN RELIABILITY APPROACH IN ROAD TRAFFIC SAFETY

The concept of human reliability is explained and its use in the area of traffic safety research is discussed. To use the approach a driving task analysis procedure is needed for the definition of correct driving behaviour as well as a methodology for the registration of errors as deviations from this standard. The main features of such procedures are sketched. Examples of applications are presented to show what kind of results can be gained by the approach. The discussion will focus on the possibilities of applying such results in driver assessment and training, road design and driver assistance systems. 1 THE CONCEPT OF HUMAN RELIABILITY In Human Factors Engineering the reliability of human action in man-machine systems plays a crucial role in optimising both person-task fit and safety issues. Human reliability in ergonomics is usually quantified in terms of observed occurrences of errors related to the number of opportunities for errors – some measure of exposure – at a given task. This ratio is commonly referred to as “Human Error Probability” (HEP). Typical fields of application have been industries in high-hazard operations such as aviation, chemical processing and nuclear power industries. Unfortunately, the possibility of collecting reliability data has very often remained hypothetical: the most serious problem always consisted in locating and recording error incidents, thus, a lack of data prevailed. Consequentially, a lot of error rates are based on expert judgements only. In its genuine form Human Reliability Analysis (HRA) has seldom been applied to road traffic safety measurement. This is all the more astonishing as in road traffic the likelihood of human error occurrences and the possibilities of gathering relevant data are much more promising. Moreover, the importance of errors as indicators of disturbances in traffic flow and traffic safety has often been pointed out and error research consequently advocated for many years [e.g. 1]. The statistical and economic advantages of error counting – compared to post hoc accident analysis – have also been described rather often [2]. Apart from theoretical reasoning the close connection between driver errors and road accidents – errors which are not compensated constitute the potential for accidents – has been shown in a wide variety of paradigms and explicit numerical calculations using empirical error and accident data [e.g. 3, 4]. The idea of defining and measuring traffic events with varying degrees of safety on a continuous scale is sometimes referred to as the “safety continuum”: Safe encounters, erroneous manoeuvres, conflicts and near-misses can be located on the scale and are thought of as preceding an accident, which defines the unsafe end of the continuum [5]. One would expect an accident to have been preceded by dangerous situations and erroneous behaviours, i.e. disturbances in the traffic system. The fewer disturbances occur, the more reliable the Human Centred Design for Intelligent Transport Systems 436 system will be. The number of errors introduced to the traffic system by its various elements gives an estimation of its hazard potential. In analogy to the human reliability approach from systems engineering we define the driver’s reliability by using the ratio between erroneous and correct performance, i.e. by human error probabilities (HEP). 2 DRIVING TASK ANALYSIS In human factors literature the notion of task analysis is regarded as fundamental in defining, understanding and predicting human errors [e.g. 6]. The match between the car drivers’ capabilities and the demands of the actual driving task determines the outcome in terms of a more or less safe driving behaviour [7]. To apply the human error approach to the measurement of car driver reliability the following prerequisites have to be fulfilled: a definition and taxonomy of driving tasks, a definition of correct behaviour in each of these tasks, a list of errors as deviations from the correct actions and an adequate observation method to register these events. Driving task analysis gives a frame for error definition that is less arbitrary than in previous driver observation records. A suitable procedure for driving task analysis and driver requirement assessment (SAFE: Situational analysis of behavioural requirements of driving tasks) has recently been developed. The analysis procedure, the underlying model of the drivers ́ information processing from which the requirements are derived, driver task lists and error lists are described in [8]. Fig. 1: Structure of the driving task classification and the analysis of requirements in SAFE (from [8], 962) Classific ation of driving tasks B asic driv in g tasks Ta sks in longitud inal direc tion Ta sks in intersec tions Definition of subtasks and segm entation in tim e a nd space Analysis of b ehavioural req uirem ents in subta sks Dr iver E rrors A dditional attribu tes of task s truc tu re  Perception  Expectations  Judgem e nt C om ple xity A ssessment concerning  Mem ory  Dec is ion, P lanning  Driver actio n, car -handling Risk Other driv ing tasks P otential be nefits Field Operational Tests and Naturalistic driving Studies / Green ITS 437 Once correct driving behaviour in a given task has been operationalised, deviations from that behaviour can be defined as errors. Finally, results from observations of actual driver behaviour may help to quantify driver reliability. 3 ERROR COUNTING TECHNIQUES Task analysis defines correct driver behaviour. To use the reliability approach deviations from this normative setting have to be listed in a way that is helpful to register erroneous events during a test drive. By using the SAFE-procedure it has become possible to derive error categories directly from the analysis of behavioural requirements by answering the question: Which kind of error will result if a defined requirement is not fulfilled by the driver? In a recent study [9] two sets of observation sheets for driver errors were developed. One type of observation sheet is shown in Figure 2; it comprises various categories of driver behaviour. Each driving task has its own observation sheet and each task is divided into subtasks appointed to spatial areas of the traffic site (segments S1 to S8 in the example). By marking an error in the respective column of the error list an exact appointment of the observed behaviour to the road site becomes possible. Sheets for other driving tasks are designed in an analogous fashion. To minimise observers’ confusion different sheets have the same order of errors and those cells that could be marked for the given task were highlighted. An error of a certain type is recorded only once per marked segment. Fig. 2: Error observation sheet for the driving task turning left in a traffic-light controlled intersection (C1-K1 left) (from [9], 227) S2 S1 S3 S4 S5 S6 S7 S8 Subject_Nr ____ Measuring point ___1___3____6____8__ Situation type: C1-K1 left Lis t of errors / Segments 1 2 3 4 5 6 7 8 1 Speed too fast 2 Speed oscillating 3 Inadequate acceleration 4 Inadequate deceleration 5 Headway too short (pushing) 6 Closing a narrow gap (squeezing) 7 Lateral distance too shor t to right side 8 Lateral distance too shor t to left side 9 No signals 10 Signals too late 11 Improper lane keeping at lane change 12 Inaccurate lane use at queuing space 13 Inaccurate lane use whi le turning 14 Inadequate lane choice 15 Adjustment too late 16 Adjustment wrong lane 17 Adjustment unsteady 18 Deceleration too late 19 Driving at yellow light 20 Jumping a red light 21 Not starting at green light 22 Entering junction not yet cleared 23 Persistent fo llowing 24 Does not use queuing space 25 Unasser tive clearing of junction 26 Too far into crossing traffic 27 Unflexible at lane closures/bottlenecks 28 Other signing errors 29 Too far into pedestrian/cycle crossing 30 Impedes pedestrians 31 Impedes cyclists 32 Perseverance on right of way 33 Low checking ahead 34 Low checking to left 35 Low checking to right 36 Low checking to rear 37 Low lateral checking 38 No checking (glance sequences) Human Centred Design for Intelligent Transport Systems 438 As the distribution of the drivers’ visual attention is of primary importance for a safe conduct a second set of observation sheets has been designed. For each task, the visual requirements as a result from the task analysis were translated into a list of necessary glances. An observer has to check, if all these looks (including those into the different car-mirrors) are accomplished by the subject and to mark missing ones. The logic and design of these sheets resembles the concept shown in Figure 2: a special sheet for each driving task including a list and a sketch as well as an appointment to the segmentation to locate the error in time and space is used. In addition, visual attention of the subject and the surrounding road traffic situations are registered by means of video cameras, which make it possible to verify both observation and additional technical recording data. Figure 3 gives the observation sheet for visual sampling for the same driving task as demonstrated in Figure 2. Fig. 3: Example observation sheet “visual behaviour” in C1K1left The above approach needs two observers to count driver errors during test drives. The observer on the front passenger seat gives navigation instructions and uses the observation sheet as shown in Figure 2. A second observer sits on the right-hand rear passenger seat and registers the visual behaviour of the subject. This technique of observing visual behaviour has already been used various times by our working group and can be traced back to Quenault ́s proposal [10]. The observation team, of course, has to be trained for the task in advance and interrater reliability has to be checked for. The measurement of errors applies to the same criteria as for any psychological testing, i.e. 1 Traffic signs overhead Glance ahead right/left Glance seque