Prevention of Accidents in Transportation Research Paper

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Abstract

Transportation accidents carry great personal, organizational, societal, and economic costs. Applied psychology can derive research inspiration from, and can contribute much to the understanding and eventual prevention of, accidents in all kinds of transportation modes. The applied psychological questions surrounding accidents can vary widely, from issues of perception in an individual driver, to trade-offs made in complex organizations under pressures of scarcity and competition, to biases and heuristics in our understanding of culpability, cause, and control. Further progress on safety in transportation systems hinges in part on contributions from applied psychology.

Outline

Why Is It Important to Understand Accidents?
Applied Psychology and Accidents
Why Do Accidents Happen?
Regulation of Transportation Systems and Incident Reporting
Interventions, New Technology, and Future Developments

1. Why Is It Important To Understand Accidents?

Transportation accidents carry tremendous social, environmental, and economic costs. Traffic accidents are among the leading causes of death for people under 34 years of age. In 2002 alone, 42,815 people died in highway accidents in the United States; that equates to more than 117 fatalities per day or 1.51 fatalities per 100 million vehicle miles traveled. The number of injuries hovers around 3 million per year. The economic costs of these accidents are enormous—more than $230 billion per year for the United States alone. Survivors of transportation accidents often require treatment and rehabilitation and also suffer psychological consequences that may carry over into work and social settings.

The nature and frequency of accidents differ widely among the various transportation modes. For example, the year 1999 witnessed 51 major accidents in commercial aviation and 2768 train-related accidents that were responsible for 932 casualties. At the same time, safety of the various transportation modes varies with geographical location. For example, in terms of casualties, passenger shipping may be less safe than driving in parts of Southeast Asia, and flying may be more dangerous in certain parts of Africa. Indeed, it would seem that the phrase ‘‘richer is safer’’ is true except when considering road traffic accidents in heavily populated, affluent parts of world. All the same, a transportation accident is one of the most common risks to which any member of society is exposed.

2. Applied Psychology And Accidents

Transportation accidents are interesting from an applied psychology perspective because they are, most basically, about human behavior in applied settings. In fact, accidents are typically about human cognition as much as they are about technical problems and issues. The applied psychological questions surrounding accidents can vary widely, from issues of sensation and perception in an individual driver to shortcuts made by organizational members under pressures of scarcity and competition. Applied psychology as a field of scientific inquiry can use its insights for, and derive much empirical inspiration from, transportation accidents. Indeed, applied psychology in the broadest, most inclusive sense is relevant to transportation accidents.

In studying whether a car driver could have seen a pedestrian, applied psychology may be called on to assess the effect of atmospheric modulation (e.g., rain, mist, dust) on stimulus perceptibility (e.g., the pedestrian). Models exist to calculate so-called atmospheric modulation transfer that can help applied psychologists to reconstruct the existing visibility during a particular occurrence. Additional questions may arise when substance abuse is suspected on the part of the driver; in such a case, applied psychology relies more on models of psychopathology. Much research leverage exists in gauging the (sub)cultural acceptability of consuming alcohol before engaging in transportation activities. Other questions with relevance to applied psychology arise when notable mismatches occur in people’s risk perceptions. For example, people might not want to use the railroads due to one highly publicized recent fatal accident. If they prefer to drive their cars, they actually expose themselves to the risk of an accident (perhaps even a fatal accident) several orders of magnitude larger than that which would exist while riding the train. Such individual risk assessments are deeply confounded, mixing personal histories, notions of control and destiny, and various kinds of biases and rationalizations (e.g., the availability heuristic) into a rich trove for applied psychology.

Perhaps even more complex are questions surrounding the trade-offs made by professionals who work in transportation systems, leading to issues such as ‘‘safety cultures’’ in transportation systems. Does the commander of the aircraft continue with the approach in bad weather or not? Does the master of the ship sail in the storm or not? Such trade-offs are more than the risk assessments of individual decision makers; they must also be understood as expressions of the preferences and priorities of entire sociotechnical systems that operate in environments of scarcity (e.g., only so many passengers, only so much money to be had) and competition (e.g., somebody else is always ready to take over one’s routes). In efforts to investigate these kinds of trade-offs and other applied problems, psychology has seen a methodological shift accelerate over the past decade or so, with an increasing emphasis on fieldwork and the study of applied settings. It is believed that to understand decision making in actual (transportation) work, where real decisions have real outcomes for real people, psychological researchers must get out of the laboratory and investigate applied settings directly. Issues of confounding factors are dealt with through intensive analysis and interpretation of research results, leading to findings (e.g., on naturalistic decision making) that are both internally valid and exportable to other applied settings.

There is also a role for applied psychology in understanding issues of culpability and control. To what extent, and why, do we judge participants in transportation (e.g., drivers, pilots) to be culpable for the accidents that they ‘‘cause’’? Such questions are deeply complex and touch on much of what people hope and believe about the world in which they live. The questions are interconnected with yet another set of biases, including the ‘‘hindsight bias’’ that describes how knowledge of outcome profoundly alters one’s perception of the behavior and intentions that led up to that outcome. The hindsight bias also allows one to convert a complex tangled history leading up to an accident into a simple series of binary decisions, where participants had clear choices to do the right thing or the wrong thing. These questions also inevitably coincide with assumptions about causation, whether justified or not. These tend to be quite problematic and affect applied psychological reasoning around accidents and human intention and behavior.

3. Why Do Accidents Happen?

One of the more compelling questions for those involved in transportation is why accidents happen. Indeed, a pressing issue after an accident is often to resolve the question, ‘‘What was the cause?’’ There is, of course, no one or unequivocal answer, and not just because the various modes of transportation may differ widely in their respective etiologies of failure. Models of accident causation develop continually, reflecting not only new insights or access to accident data but also the general scientific spirit of the times. In transportation, the so-called ‘‘chain of events’’ model is popular. In this model, one failure somewhere in the system can be seen to lead to (or trigger) the next, and so on, until this cascade of individually insignificant faults pushes the entire system over the edge of breakdown. For example, a piece of debris on the runway, left there by a preceding aircraft, manages to puncture the tire of a subsequent aircraft. Tire fragments slam into the wing, puncturing the fuel tank and triggering a fire that, not much later, brings down the entire aircraft. The chain of events model is also credited with distributing or refocusing the search for accident causes away from frontline operators (i.e., away from ‘‘operator error’’), instead identifying higher up supervisory and organizational shortcomings that may have contributed.

However, recent insight into the sociotechnical nature of large transportation accidents, such as disasters involving the Space Shuttles Challenger (in 1986) and Columbia (in 2003), has exposed the limits of the chain of events model. Most critically, the model presupposes (or even requires) failures so as to cause a failure. This contradicts characterizations of organizational and operational practice preceding accidents as normal everyday routine. Accidents can still happen even if everybody follows the rules and there are no ‘‘failures’’ or ‘‘shortcomings’’ as seen from the perspective of those running and regulating the transportation system. As Perrow suggested in 1984, accidents in these systems may be quite ‘‘normal’’ given their structural properties of interactive complexity and tight coupling. Nothing extraordinary (i.e., no obvious breakdowns or failures) as seen

from inside the system is necessary to produce an accident. For example, aviation is a tightly coupled system, as are railroads. This means that there is little slack to recover if things do start to go wrong. The workings of a railroad system, however, are generally more linear and transparent than those of aviation, with the former allowing for better insight into how to manage the system.

These insights have put pressure on the old label ‘human error’’ as an explanation for accidents. Indeed, human error today can no longer be legitimately seen as the cause of accidents; rather, it is seen as an effect, as a symptom, or as a sign of trouble deeper inside the system. Human error is no longer an explanation; instead, it demands one. Human error, to the extent that it exists as a separable category of human performance (which many researchers doubt or deny), is systematically connected to features of people’s tools and tasks. This is consistent with the ecological commitment in much of applied psychology, where an analysis of agent–environment mutuality, or person–context couplings, is critical to producing useful insights into the success and failure of transportation (and other) systems. The latest accident models deemphasize ‘‘cause’’ altogether, noting that it is a deeply Newtonian concept that might not carry over well into an understanding of why complex transportation systems fail, with the Columbia accident being a case in point. These models see transportation accidents rather as control problems; as the gradual erosion and eventual loss of control over a safety-critical process, where safety constraints on design or operation are violated. Such models can be reconciled better with increasing evidence that large transportation accidents are nearly invariably preceded by some kind of slow but gradual drift (e.g., away from procedures, away from design specifications) that is hard to notice or characterize as deviant when seen from the inside, that is, from the perspective of participants themselves. The consistent, if slight, speeding that most male drivers exhibit (e.g., nearly always 5 miles per hour over the speed limit) is another example of this. The deviance is normalized; the nonroutine becomes routine, and there is consistent noncompliance. Production pressures (e.g., pressure to be on time, pressure to maximize capacity use) form the major engine behind such drift toward failure in nearly all transportation systems because virtually no transportation system is immune to the pressures of competition and resource scarcity. Recent research debates are not necessarily about the existence or reality of such pressures; rather, they discuss to what extent these pressures consciously affect people’s trade-offs and to what extent people deliberately gamble (and lose if they have an accident). Much research points to the prerational insidious nature of production pressures on people’s trade-offs rather than to conscious immoral or risky calculation, although male drivers under 25 years of age might be an exception.

Acknowledging that such sociotechnical complexity underlies most transportation accidents means that safety and risk are really social constructs rather than objective engineering measurements. Risk is constructed at the intersection of social forces and technical knowledge (or the lack thereof). Slogans such as ‘‘safety comes first’’ are mere posturing. They are disconnected from reality where risk is negotiated as subjective social activity and where it will be different in different cultures, different among organizations within a single culture, and even different among subcultures within one organization.

4. Regulation Of Transportation Systems And Incident Reporting

Most, if not all, transportation systems are regulated to some extent. Regulation means that rules are created to govern practice and that the rules are enforced to generate compliance. This often requires large organizations—nearly always public or government agencies—tasked with issuing rules, overseeing practice, and certifying and checking systems, operators, and organizations. The extent of regulation of various transportation modes varies greatly from country to country. In some countries, regulators are criticized for having too close a relationship with the transportation industries they are supposed to regulate and for having a dual and supposedly incompatible mandate (i.e., promoting the use and growth of the transportation system as well as overseeing and monitoring its compliance).

The success of safety regulation depends on the safety level that the transportation system has already achieved. In relatively unsafe transportation modes (e.g., private flying), regulation can pay great and rather immediate dividends. It can help to standardize practice; it can issue, broadcast, and enforce rules and remind operators of them; and it can help to build a corpus of cases on routes to accidents that can be shared in the community. There is often leverage in changing designs (e.g., operator interfaces) to make them more error resistant and error tolerant. In safer systems (e.g., charter flights), such error-resistant designs have typically evolved further. Accidents are often preceded by socalled ‘‘dress rehearsals’’—sequences of events similar to real accidents but without fatal outcomes. Learning from those dress rehearsals is encouraged, particularly through incident-reporting systems. However, in ultrasafe transportation systems (e.g., European railroads), overregulation becomes a problem. More rules and more monitoring are no longer accompanied by safety gains; they serve only to increase system complexity and potentially decrease transparency as well as rule compliance. Incident-reporting systems in ultra-safe transportation systems may be of limited value. The typical accident there emerges from routine, everyday banal factors that combine and align in ways that are hard to foresee. Incident reports would neither notice the relevance of those factors nor be able to subsequently project their interplay. This, in fact, is a problem allied with all incident-reporting systems. Reporting is not the same as analysis, which in turn is not the same as learning from potential failure. Just having an incident reporting system in place guarantees little in terms of progress on safety.

5. Interventions, New Technology, And Future Developments

Prevention and improvement strategies vary considerably from one transportation mode to another because many of the factors that affect accident likelihood are mode specific. In general, prevention strategies seek either to minimize the likelihood of a particular kind of harmful event (e.g., a crash) or to minimize its impact (in terms of property damage, injury, or environmental pollution). New technology often plays a dominant role here. Automatic seat belt systems, side impact protection systems, collision detection and avoidance systems, anti-lock breaking systems, double-hull vessels, automated cocoons to keep an aircraft within its prescribed envelope—all of these systems intervene, prevent, and protect in one sense or another. Even though enormous progress is made through such interventions, technology is sometimes a mixed blessing. Airbags may protect mostly drivers who do not wear seat belts (forcing a large majority of other people, who do wear seat belts, to pay a premium on their cars), and automation in virtually all transportation modes has been associated with the emergence of new human–machine coordination problems (e.g., mode errors, ‘‘automation surprises,’’ people getting lost in display page architectures). Indeed, automation has been associated with the emergence of new types of accidents in virtually all transportation modes, where it has introduced new capabilities and new complexities. One typical signature of accidents in automated transportation systems is the ‘‘going sour’’ scenario, where a small trigger event (i.e., an unusual occurrence or a small failure in a system somewhere), itself innocuous, leads to a series of misassessments and miscommunications between humans and computers. The system is managed into greater hazard due to coordination breakdowns among the various players (both humans and machines). The end result is often an automation surprise, where humans discover that what they thought they had instructed the automation to do was not quite what the automation was doing. More research, much of it in applied psychology, will be necessary to use new technology and other interventions to their full potential in helping to reduce transportation accidents.

References:

Amalberti, R. (2002). The paradoxes of almost totally safe transportation systems. Safety Science, 37, 109–126.
Billings, C. E. (1996). Aviation automation: The search for a human centered approach. Mahwah, NJ: Lawrence Erlbaum.
Dekker, W. A. (2002). The field guide to human error investigations. Bedford, UK: Cranfield University Press.
Perrow, (1984). Normal accidents: Living with high-risk technologies. New York: Basic Books.
Vaughan, D. (1996). The Challenger launch decision: Risky technology, culture, and deviance at NASA. Chicago: University of Chicago Press.