Chapter 12. Social, Personality, and Affective Constructs in Driving
Dwight Hennessy
Buffalo State College, Buffalo, NY, USA
This chapter focuses on the person–situation transaction in driving, where personal factors in drivers (e.g., personality and affect) are impacted by contextual factors (e.g., social interactions and the physical environment) to specifically shape cognitions, attitudes, and behavior. Common personal (sensation seeking, aggression, anger, stress, and locus of control) and contextual factors (congestion, time urgency, weather, and culture) are examined, with a focus on current theories, models, measurement tools, and ultimately driving outcomes, in addition to contradictions, methodological concerns, and proposed future directions.

1. Introduction

Driving is more than the mechanical operation of a vehicle, as a means of movement between destinations. Rather, it is a complex process involving individual factors expressed within a social exchange among drivers, passengers, and pedestrians, which is ultimately impacted by contextual and environmental stimuli found inside and outside the vehicle. Rotton, Gregory, and Van Rooy (2005) argued that, traditionally, the focal point of most traffic research has been the individual in this system, often at the expense of situational determinants. This is perhaps due to the fundamental attribution error, which is the tendency to explain the actions of others in terms of personal causes even when situational factors are evident. The driver is a central component of this system, but only one component, whose thoughts, feelings, and actions are shaped and directed by the micro and macro context.
In many respects, the traffic environment is a distinct and intriguing setting. There is a degree of speed and anonymity not found in other contexts, with huge discrepancies in history, experience, or skill level among drivers; subtle forms and means of communication that often have multiple interpretations; a unique blend of written and unwritten rules that can vary across locations; and ultimately a high degree of danger. This uniqueness, in addition to the widespread application potential and relevance to the general public, has made the traffic environment an attractive context for social research. This chapter focuses on the components of this person–situation system and how they have been shown, individually and in combination, to impact driving behavior (e.g., rule violations and collision), personal outcomes (e.g., health, mood, stress, and fatigue), and interpersonal interactions (e.g., aggression and judgments of others).

2. Personal Factors and Driving Outcomes

Given that it is “people” who drive, it would seem rational that any discussion of factors that impact driving outcomes would include personal factors. It would also seem logical to assume that drivers are all ultimately unique, and that at any given moment on the roadway, there is immense variability in driving styles, learning experiences, collision history, and expectations/judgments. However, this does not preclude the search for patterns that identify categories of drivers who might be more or less at risk for negative outcomes on the roadway over time. Although there are numerous categories of individual differences that can impact driving, traffic psychology has often focused on personality variables. In fact, personality has been used to qualify many demographic and affective factors, such as gender (often associated with a masculinity trait) and driver anger (often examined as a trait disposition).
Although there are many definitions of personality, most share the notion that it involves the consistent pattern of thoughts, feelings, and actions that emerge with some level of stability across time and context. Traffic psychology has typically relied on the “trait” approach, in which the focus has been on individual characteristics that combine or cluster to determine the overall expression of personality. As a result, certain traits are believed to be inherently more dangerous than others in the traffic environment. Those who possess more of these or in a more dangerous balance are believed to be a greater risk to self and others. However, it is also a matter of “degree” in that each trait is identified along a continuum of its “strength,” where some characteristics may have a much stronger impact on the overall personality, and those that possess dangerous traits to a higher degree are most problematic.

2.1. A Case for Personality in Understanding Negative Driving Outcomes

Another reason may be that personality represents an indirect rather than direct link with collisions. Beirness (1993) argued that personality on its own is a poor predictor of collisions but instead interacts with more “proximal” factors that often involve current, state, or pressing driving-related factors. Sümer (2003) provided an excellent review of this research and proposed a model in which personality represents one of several possible distal factors (in addition to other enduring personal, cognitive, and situational factors, such as culture, vehicle condition, and attributions) that impact more immediate proximal factors composed of driving style and transitory factors (e.g., violations, errors, and safety skills) that then influence collisions. In this respect, personality is an important focal point in traffic research given its impact on the more macro, persistent, or ambient distal level (e.g., the approach to driving, personal tendencies, and beliefs about other drivers), as well as on the immediate and transitory proximal influences (e.g., altering state interpretations of actual driving behavior, state emotional experiences, and negative driving behaviors). The following sections provide a brief examination of personality factors that have been linked to negative driving outcomes. Although this list is far from comprehensive, it represents factors commonly observed in traffic psychology.

2.2. Sensation Seeking

One of the most widely studied personality predictors of negative driving behavior is sensation seeking, which is defined as a trait characterized by the pursuit of novel, diverse, and extreme experiences. To achieve these goals, high sensation seekers often display a willingness to take disproportionate physical and social risks (Zuckerman, 1994). Driving provides an excellent opportunity for high sensation seekers to satisfy the desire for sensation given the inherent potential for arousal, excitement, danger, speed, and competition. Jonah (1997) reviewed 40 studies on sensation seeking in drivers and concluded that it can increase dangerous driving in a number of areas, including impaired driving, speeding, and seat belt use. In fact, a relationship in the range of 0.3 to 0.4 was found in all but 4 studies he examined.
Sensation seeking is typically measured using Zuckerman’s Sensation Seeking Scale (SSS) Form V, which is further divided into four subscales identifying boredom susceptibility (BS), thrill and adventure seeking (TAS), disinhibition (DIS), and experience seeking (ES). However, comparisons across studies applying sensation seeking to driving behavior are difficult due to the fact that researchers often do not use the entire SSS, instead opting for selective subscales (thrill seeking being the most typical), truncation of items from subscales, combinations of items with self-generated or other unrelated items, or a total sensation seeking score that is undifferentiated across subscales.
Another tool that is gaining in popularity is the Arnett Inventory of Sensation Seeking (AISS), which was designed in response to perceived limitations in Zuckerman’s SSS. Most notably, Arnett (1994) believed that several items were culturally irrelevant and also contained risky behavior-based items that were confounded with many of the activities that were typically used as outcome variables in research, including drinking and taking drugs. The AISS contains two subscales based on a need for novelty and intensity of stimulation. Previous research has established that the AISS does measure a similar dimension of sensation seeking as that measured by the SSS and predicts dangerous and risky behavior (Andrew and Cronin, 1997 and Arnett, 1994).
Despite their differences, both the AISS and the SSS have been linked to negative driving outcomes. According to Zuckerman (2007), the SSS predicts reduced risk appraisal and elevated risk taking, including reckless driving. Jonah, Thiessen, and Au-Yeung (2001) argued that sensation seeking does appear to increase dangerous driving patterns, including aggressive (e.g., swearing, yelling, and horn honking) and high-risk activities (e.g., weaving and speeding). Interestingly, this trend is evident even among predrivers (Waylen & McKenna, 2008). Specifically, using eight items from the AISS, high sensation seeking among boys aged 11–16 years was related to favorable attitudes toward risky road use.
Other negative driving activities that have been linked to sensation seeking include tailgating and speeding (using TAS & BS subscales; Harris & Houston, 2010), lack of seat belt use and unsafe passing (using TAS & DIS subscales; Dahlen & White, 2006), and ignorance of traffic rules (using newly generated items; Iversen & Rundmo, 2002). Sensation seeking has also been found to predict elevated convictions (using TAS & BS subscales; Matthews, Tsuda, Xin, & Ozeki, 1999), self-report traffic violations (using total SSS score; Schwerdtfeger, Heims, & Heer, 2010), drinking and driving (using all four SSS factors; Greene, Krcmar, Walters, Rubin, & Hale, 2000), and driving under the influence of cannabis (using total SSS; Richer & Bergeron, 2009). Furthermore, using TAS and DIS subscales, Rimmö and Åberg (1999) determined that the violations subscale of the DBQ (which measures a dispositional tendency to willfully commit violations) mediated the relationship between sensation seeking and self-report violations and collisions. Interestingly, Wong, Chung, and Huang (2010) advocated a positive effect of sensation seeking in that high sensation seekers (using self-derived items) were involved in fewer motorcycle collisions due to the regulating effect of confidence in their riding ability. However, the collisions they experienced were more severe than those of low sensation seekers.
Some critics have argued that caution must be exercised when interpreting reported associations between sensation seeking and risky behaviors due to a high degree of conceptual overlap between the two constructs (Arnett & Balle-Jensen, 1993). A risk-taking lifestyle may naturally predispose one to seek stimulating activities; hence, the psychological phenomenon of sensation seeking may overlap with the behavioral predictor of risk taking. However, Burns and Wilde (1995) contended that sensation seekers may, in fact, seek activities that are not inherently risky. For example, sensation seekers may drive fast and experience heightened arousal yet remain within the bounds of safety by not taking excessive risks (e.g., driving on higher speed highways).
Another consideration is the fact that sensation seeking is strongly associated with age and developmental processes, which peak in late adolescence and begin to decline thereafter (Arnett & Balle-Jensen, 1993). This is especially important in traffic research because most studies that focus on dangerous or risky outcomes of sensation seeking tend to concentrate on younger drivers, for whom sensation seeking, risky tendencies, and dangerous driving are concurrently heightened. In addition, younger drivers are typically the least experienced and may engage in more dangerous activity not because they are sensation seekers but, rather, because they lack the practical knowledge, speed of processing, or sensitivity to potential danger that come with practice. In this sense, the link between sensation seeking and risky or dangerous driving may be overstated. However, the fact that sensation seeking is still linked to negative driving outcomes among older drivers may provide impetus for its continued use in identifying a category of at-risk drivers.

2.3. Trait Aggression

The issue of aggressive driving has received a great deal of attention from both scientific and public communities. However, there are differing conceptualizations of the term “aggressive,” which has made it difficult to interpret and compare conclusions in this area. Following the motivational approach to aggression (Berkowitz, 1993), many have treated traffic aggression as actions intended to physically, psychologically, or emotionally harm another within the driving environment, including drivers, passengers, and pedestrians (Hennessy and Wiesenthal, 1999 and Shinar, 1998). Examples include yelling, swearing, purposely tailgating, leaning on the horn, and roadside confrontations.
In contrast, others have approached aggressive driving as any dangerous driving behavior regardless of intent, such as speeding, weaving through traffic, and using the shoulder to pass. However, several studies have found that these latter actions, which more closely resemble highway code violations or assertiveness, are distinct compared to intentionally harmful “aggressive violations.” Drivers who intentionally cause other motorists harm are different in important ways from those who break traffic rules, even though these latter actions are sometimes selfish, illegal, or dangerous to others (Hennessy and Wiesenthal, 2005 and Lawton et al., 1997).
For the purpose of this chapter, discussions of driver aggression focus on the motivational definition emphasizing intentional harm. One important limitation to this approach is that intention is often difficult to determine, especially when self-report measures are used. This is exaggerated by the fact that several actions that are often considered as “aggressive” can also have different meanings between cultures or could be initiated for reasons other than harm. For example, Lajunen, Parker, and Summala (2004) noted that horn honking is typically considered aggressive in Scandinavia but as a message in southern Europe. Nonetheless, research has found that aggressive drivers typically show patterns of increased frequency and duration, as well as decreased latency of such actions (e.g., honk more often, for longer, and quicker in response instigation), and that they concurrently engage in a pattern of multiple forms of aggression, all of which would be more indicative of aggression than signaling behavior (Doob and Gross, 1968, Gulian et al., 1989 and Hennessy and Wiesenthal, 2005).
Although aggression is predicted by numerous personal, social, cognitive, and environmental factors (Berkowitz, 1993), there are some individuals who demonstrate a “trait”-like proclivity toward more frequent and severe acts of aggression. From this perspective, some drivers develop trait driver aggression tendencies (Hennessy & Wiesenthal, 1999), which represents a unique personal quality from those who seek to take risks or gain excitement from driving. Rather, trait driver aggression appears to involve an elevated tendency to misperceive the actions and intentions of other drivers as hostile and threatening; to become frustrated or irritated by the actions of others; and to be willing to “pay back” offending drivers through physical, emotional, and psychological harm (Gulian, Matthews, et al., 1989; Hennessy & Wiesenthal, 2001a).
One explanation for the development of trait driver aggression is grounded in learning theories. Specifically, acting in an aggressive manner, when viewed as “successful,” can be negatively reinforcing due to the removal of the source of irritation, frustration, and conflict and/or positively reinforcing through the addition of feelings of control, power, and dominance. Over time, these actions are then considered as more acceptable, beneficial, and potentially successful during similar subsequent situations. As a result, aggression becomes more customary for that driver as such responses increase in his or her response hierarchy. With repeated reinforcement, and concurrent lack of negative repercussions, driver aggression becomes acceptable or normative to that driver’s behavioral repertoire.
Trait aggression has been linked to a number of dangerous outcomes, including state aggressiveness and violations. Britt and Garrity (2006) had participants recall past driving events and indicate how they would react, and the authors found that dispositional aggression predicted angry and aggressive responses. Using self-report measures, Hennessy and Wiesenthal (2002) found that trait driver aggression was related to traffic (highway code) violations, which is consistent with the results of King and Parker (2008), who reported that trait aggressive drivers were more likely to commit traffic violations and to falsely underestimate the frequency of their violations in comparison to others. Similarly, Maxwell, Grant, and Lipkin (2005) found a relationship between trait driver aggression and traffic violations in a sample of UK drivers, whereas Kontogiannis, Kossiavelou, and Marmaras (2002) also noted that speeding convictions and general law breaking were predicted by a tendency to commit aggressive violations among Greek drivers.
The link between trait aggression and collisions may be more complicated, perhaps due to the relative rarity of collisions. Li, Li, Long, Zhan, and Hennessy (2004) found that self-reported trait driver aggression was related to active collisions, as well as traffic violations, among Chinese drivers. However, they cautioned that their sample was predominantly males, which may overestimate this relationship. Bener, Özkan, and Lajunen (2008) also revealed that driver aggression predicted collisions in Qatar, although their aggressive construct also contained elements of speeding competition. Gulliver and Begg (2007) utilized face-to-face interviews with drivers and identified a relationship between trait aggression and crashes, but only among men and after controlling for exposure. According to Sümer (2003), the link between trait aggression and collisions may, in fact, be indirect because it is mediated by an aberrant driving style (i.e., elevated errors and violations).

2.4. Negative Emotions and Trait Anger

Negative emotions experienced while driving may alter cognitions of traffic-related stimuli, narrow attentional focus or processing, and modify interpretations of other drivers and their activities, thus eventually increasing the potential for risky, harmful, or unsafe driving (Mesken, Haganzieker, Rothengatter, & de Waard, 2007). Shahar (2009) found that anxiety can have a negative impact on the number of errors, lapses, and ordinary violations, largely due to distraction and attention deficits. Similarly, Oltedal and Rundmo (2006) demonstrated that anxiety may have an indirect relationship to collisions through its link with risky driving behavior. However, others have argued that anxiety can serve a positive role in driving through increased cautiousness and alertness (Stephens & Groeger, 2009).
Other emotions, such as sadness, may have an indirect impact on driving outcomes as well. According to Pêcher, Lemercier, and Cellier (2009), drivers who listened to sad music in a simulator felt calmer but were unable to focus as much attention on the driving task. Similarly, Bulmash et al. (2006) found slower steering reaction times and a higher number of accidents among depressed participants in a simulator.
The most commonly examined emotion in driving research has been anger. Using a diary methodology, Underwood, Chapman, Wright, and Crundall (1999) revealed that 85% of their sample of UK drivers (104 participants recruited from the general driving population through media advertisements and from driving test centers) reported experiencing anger at least once during their routine daily driving excursions during a 2-week period. According to Speilberger (1988), there is a clear distinction between state and trait anger. State anger represents an emotional experience, from mild annoyance to extreme fury, in a given context. Trait anger characterizes an enduring tendency to experience state anger more frequently, intensely, and with greater duration.
Deffenbacher and colleagues have advocated a similar distinction in the driving environment where trait driver anger represents a special context-specific form of trait anger that predicts more frequent and intense anger in actual driving conditions (Deffenbacher, Huff, Lynch, Oetting, & Salvatore, 2000). The most widely used measurement tool to date has been the Driving Anger Scale (DAS; Deffenbacher, Oetting, & Lynch, 1994), in which participants are asked to rate their likely degree of anger to common anger-provoking driving situations, leading to an average score believed to differentiate drivers on a continuum of trait driver anger.
The DAS has been translated into several languages and used in a number of different countries, with a general degree of success. Despite variability in factor structure and unique predictive ability, there has been general acceptance in its ability to measure a “trait” anger characteristic. For example, Deffenbacher et al. (1994) and Yasak and Esiyok (2009) found six factors using a U.S. and Turkish sample, respectively, whereas Lajunen, Corry, Summala, and Hartley (1998) identified three factors in a UK sample, Björklund (2008) found a similar three-factor version using a Swedish translation, and Sullman (2006) uncovered four factors in a New Zealand sample. Also, Sullman found age and gender differences in DAS scores, whereas Yasak and Esiyok did not.
Despite discrepancies, trait driver anger has been linked to a number of dangerous driving behaviors, including violations (Lajunen et al., 1998), lost concentration, poor driving control, and close calls (Dahlen, Martin, Ragan, & Kuhlman, 2005). Some studies have linked driving anger to collision, but others have not. Deffenbacher et al. (2000) found that driving anger was related to greater self-reported lifetime collisions and minor collisions during the past year, as well as greater moving violations. In contrast, according to Deffenbacher, Lynch, Oetting, and Yingling (2001), although trait anger was related to low levels of concentration, poor vehicle control, and close calls, it did not predict collisions or violations. Similarly, Sullman (2006) found that the DAS was unrelated to self-reported collisions during the past 5 years. This incongruity may be due to the relative rarity of collisions as an outcome measure, difficulties in collecting collision data accurately (particularly self-reports of active or at-fault collisions), as well as numerous indirect and contextual influences on collisions.
Deffenbacher, Deffenbacher, Lynch, and Richards (2003) also identified aggression as a consequence of elevated driving anger independent of demographic variables. However, others would contend that the link between anger and state aggression is not always strong or direct. Considering the total volume of drivers and distance driven per day, and numerous potential moderating and mediating factors in the traffic environment, aggression may not be an overly common event (Parker, Lajunen, & Summala, 2002). In this respect, Neighbors, Vietor, and Knee (2002) advocated only a modest link between anger and aggression. It could be maintained that the strength and nature of such a relationship ultimately depend on a number of interacting personal and situational factors, such as the type and nature of interactions, the degree of frustration, motivation, and public self-consciousness (Lajunen and Parker, 2001 and Millar, 2007).
One consideration concerning driving anger research is the fact that some researchers neglect to differentiate state from trait anger, to the extent that some treat their trait measurement as a state outcome. Although trait anger may predict greater state anger in driving situations, it is not deterministic. Another concern is the fact that a meta-analytic review of the anger–aggression link by Bettencourt, Talley, Benjamin, and Valentine (2006) showed that trait anger predicts aggression only in hostile situations. Similarly, Wilkowski and Robinson (2010) argued that anger predicts reactive forms of aggression. This might account for the contention from Van Rooy, Rotton, and Burns (2006) that the DAS and Driving Vengeance Questionnaire (DVQ) are indistinguishable from one another based on their extremely high correlation. The DVQ was designed as a measure of attitudes toward vengeance in the driving environment, where those who have been wronged or provoked by other drivers would seek revenge through reactive types of aggression (Wiesenthal, Hennessy, & Gibson, 2000). Hence, the high correlation found by Van Rooy et al. (2006) may be due to confounding by reactive aggression. However, it could just as easily be argued that trait vengeful drivers become more easily and intensely angered and as a result are more inclined to respond to provocation from others through aggression.

2.5. Trait Driver Stress Susceptibility

Most traffic research has treated driver stress as the outcome of a negative cognitive appraisal of driving situations (Glendon et al., 1993 and Hennessy and Wiesenthal, 1997). It is only when driving is interpreted as demanding or overly taxing that stress manifests itself in psychological (e.g., anxiety and negative mood; Gulian, Matthews, et al., 1989; Wiesenthal, Hennessy, & Totten, 2000), cognitive (e.g., task-relevant cognitive interference and loss of attention; Desmond and Matthews, 2009 and Matthews, 2002), or physical symptoms (e.g., increased heart rate and blood pressure; Stokols, Novaco, Stokols, & Campbell, 1978). In this respect, trait driver stress has immediate and long-term consequences to drivers.
According to the transactional model of driver stress (Matthews, 2002), trait stress susceptibility is an important personal factor that can potentially interact with the situation to impact driving outcomes. This model holds that driver stress is the product of the dynamic interaction between personal and environmental factors that are mediated by cognitive processes (e.g., interpretation of events and selection of coping resources). Repeated stressful experiences or ineffective coping strategies can lead to feedback that dynamically alters the transactional process and eventual behavior of that driver. For example, repeated stressful outcomes may lead to a generalized trait driver stress susceptibility, which may then impact the driving activities and resulting situational factors that might be encountered, subsequently altering perceptions and interpretations of those events and ultimately impacting the state emotional experience in actual conditions.
Dislike of Driving (DIS) represents items related to anxiety, unhappiness, and lack of confidence while driving, particularly when driving conditions are difficult. DIS has been found to predict negative mood in a simulated driving task (Dorn & Matthews, 1995) as well as postcommute tension and depression (Matthews et al., 1991). High DIS drivers show greater impaired lateral control (Matthews, Sparkes, & Bygrave, 1996) and mistakes and lapses (Kontogiannis, 2006), but they also perceive themselves as less skilled, showing reduced self-driving confidence leading to greater cautiousness, slower speeds, and fewer speeding tickets (Matthews et al., 1991 and Matthews et al., 1998).
Driving Aggression (AG) items focus on reactions to irritation in the driving context and impatience shown as a result of impedance from others. Those high on the AG factor demonstrate more dangerous driving patterns, including control errors when overtaking (Matthews et al., 1998); mistakes, lapses, and speeding (Kontogiannis, 2006); and tailgating, confrontation, negative evaluation of other drivers, and minor collisions (Matthews et al., 1991).
The Alertness (AL) factor predominantly measures a tendency to monitor the driving situation for hazards. Previous research has found that AL is considerably lower in reliability than the DIS or AG factors (Glendon et al., 1993 and Lajunen and Summala, 1995) and has weak predictive relationships with behavioral and emotional outcomes (Matthews et al., 1998). This may partially account for conflicting results in research attempting to link AL with negative driving outcomes. For example, although AL has been associated with reduced collisions (Matthews, Desmond, Joyner, Carcardy, & Gilliland, 1997) and fewer speeding convictions (Matthews et al., 1991), Kontogiannis (2006) failed to find a direct relationship with either speeding convictions or collisions.
As an alternative, Matthews et al. (1997) revised and extended the item pool of the DBI to develop the Driver Stress Inventory (DSI), which consists of five factors or “dimensions” of stress vulnerability. According to Matthews (2002), Dislike of Driving (DIS), Driver Aggression (AG), and Fatigue Proneness (FAT) are reflective of subjective states of disturbance while driving. Thrill Seeking (TS) represents an enjoyment of danger, and Hazard Monitoring (HM) is consistent with a vigilance to danger while driving. The DSI is believed to be more predictive of negative driving outcomes consistent with stress than general personality constructs. Öz, Özkan, and Lajunen (2010) confirmed the five-factor solution of the DSI among Turkish drivers, and regression analysis showed that DIS and TS predicted speeding, whereas AG, DIS, and low HM were related to accident involvement. Matthews (2001) also reported that AG, TS, and low DIS were related to speeding and self-reported violations, and that AG, TS, DIS, FAT, and low HM were linked to higher rates of unintentional errors. However, some inconsistencies have been noted. Öz et al. did find negative outcomes from DIS, AG, and FAT that would be expected in comparison to the work of Matthews and colleagues, but they found that TS was related to less “dangerous” activity in the sense of slower driving and that HM was related to greater collisions. They argued that other factors, such as unique sample makeup, risk perception, and hazard detection, might account for these differences, which would be consistent with the transactional model on which the DSI is based.
One issue with using the DBQ and DSI is the fact that the factors overlap with other driving personality constructs, most notably aggression and thrill seeking of sensation seeking scales. Although these legitimately appear to represent components of driver stress within a transactional model, caution needs to be exercised when examining trait driver stress in combination with tools more narrowly designed to measure related constructs to avoid inflated relationships due to item overlap.
Another issue is that age differences have been found in both total and subfactor scores of driver stress, with older drivers typically showing lower levels of stress (Gulian, Matthews, et al., 1989; Langford & Glendon, 2002). Given that age has also been negatively linked to several driving outcomes, including aggression, speeding, riskiness, and collisions (Hennessy and Wiesenthal, 2002 and Waylen and McKenna, 2008), oversampling of young participants or failure to control for age may exaggerate the negative impact of stress on driving. A similar case could be made for gender due to the fact that men are also highly represented in dangerous driving activities and have been concurrently found to show higher levels of some components of driver stress, such as driving aggression and irritation when overtaking (Matthews et al., 1999). Hence, the disproportionate inclusion of male drivers in research could overstate the link between components of drivers stress, such as aggression, and negative outcomes, such as collisions.

2.6. Locus of Control

Locus of control (LOC) represents an enduring belief about the source of cause for, or control over, personal behavior and is typically polarized as internal (I) and external (E) (Rotter, 1966). Externals are more likely to find responsibility for their actions in other individuals, luck, chance, or situational factors beyond their control, which may contribute to lack of caution or fewer precautions to prevent negative outcomes in life. According to Montag and Comrey (1987), externals represent a danger in the traffic environment due to their passive tendencies leading to fewer personal precautions. In contrast, internals tend to attribute their activities more often to stable, internal attributes (skill or effort) and as such also take more active responsibility for their actions and steps to alter negative future outcomes. However, Arthur and Doverspike (1992) argued that internals may represent a greater danger in the driving environment due to an overconfidence and overestimation of their skills or abilities while driving.
Overall, LOC has been linked to various driving attitudes and behaviors, although there have been mixed findings. An external locus has been linked with elevated collisions (Lajunen & Summala, 1995), drinking and driving offenses (Cavaiola & DeSordi, 2000), errors (Breckenridge & Dodd, 1991), and intention to commit violations (Yagil, 2001), whereas an internal locus has been associated with increased seat belt use (Hoyt, 1973) and cautious behavior (Montag & Comrey, 1987). However, others have failed to find a link between LOC and collisions (Guastello and Guastello, 1986 and Iversen and Rundmo, 2002) or seat belt use (Riccio-Howe, 1991) and determined that different measurement approaches can lead to contrasting results (Cavaiola & DeSordi, 2000).
One reason for such inconsistency could be that Rotter’s original I/E scale may be too general to predict situation-specific LOC. In response, Montag and Comrey (1987) developed two separate scales to measure driving-specific internality (DI) and externality (DE) (MDIE), with which they identified a positive relationship between DE, as well as a negative relationship between DI, and collisions. However, using the MDIE, Arthur and Doverspike (1992) and Iversen and Rundmo (2002) failed to find any significant association with collisions. Holland, Geraghty, and Shah (2010) noted that such variation could be due to an overuse of male drivers, who are also more likely to possess an elevated self-bias and, as such, may give responses that make themselves appear less internally responsible for collisions, hence masking possible relationships between DI and collisions.
Another reason may be that the link between LOC and driving behavior is more indirect than direct. For example, although Guastello and Guastello (1986) did not find a link between Rotter’s I/E and collisions, they did note that a belief about the causes of accidents, or attributional style, may translate to specific activities such as collisions. Furthermore, Holland et al. (2010) found that externals are more likely to possess a negative driving style (e.g., either a “dissociative style” that includes distractibility or an “anxious style” that involves lack of confidence and distress) that may then indirectly impact collisions. Yagil (2001) similarly argued that externality can indirectly influence intentions to commit violations through positive attitudes toward violations.
Others have noted that LOC acts as the moderator. According to Gidron, Gal, and Desevilya (2003), the impact of hostility on collisions is moderated by increased DI. In another study, Miller and Mulligan (2002) found that driving LOC altered the outcome of mortality salience on subsequent risky driving behavior. After receiving a mortality salience treatment in which participants were given 15 true/false items designed to stimulate thoughts about their own death, internals indicated decreased, whereas externals reported increased, future risky driving behavior. This suggests that the LOC orientation was accountable for changes in the perception and personalization of mortality-related information in the driving context, in advance of future risk-taking driving behavior.
A third reason for the discrepancies could be that the concept of LOC is more varied and multidimensional than originally proposed. In this respect, a single bipolar distinction of control may be insufficient to examine the complexities of driving behavior. Levenson (1981) extended the original dimension of I/E to represent independent orientations of internal, chance, and powerful others. In a similar respect, Lajunen developed a multidimensional traffic-specific LOC scale (T-LOC; Özkan & Lajunen, 2005a), which asks questions about the source of control for driving outcomes. The four subscales of the T-LOC include the Self (similar to Internal), the Vehicle/Environment, Other Drivers, and Fate (similar to External). Using a Turkish translation, Özkan and Lajunen (2005a) found that those with an elevated “self” LOC reported elevated ordinary and aggressive violations, errors, and active accidents, which they interpreted within the framework of an elevated self-bias where self-confident drivers downplay the likelihood of negative outcomes for more dangerous driving behavior due to their own ability or skill. Using a Swedish translation of the T-LOC, Warner, Özkan, and Lajunen (2010) found a five-factor structure with similar external factors (Vehicle/Environment, Other Drivers, and Fate) and two internal factors related to the Self (Own Skill and Own Behavior). Own Behavior predicted self-reported speed on 90 km/h roads, supporting the notion that internal aspects of the T-LOC may be a useful alternative in understanding riskier driving activities.

3. The Context in Driving Outcomes

3.1. Traffic Congestion: Impedance, Time Urgency, and “Other” Drivers

Perhaps the most recognizable contextual factor in the driving environment is traffic congestion, which extends beyond the number of vehicles to represent the perception and interpretation of that volume. In line with the distinction between density and crowding in environmental psychology, the volume of traffic embodies the physical number of vehicles (or drivers) per unit of space, whereas congestion occurs when drivers believe that there are too many vehicles or too little space at that given time and location. Also, although a greater number of vehicles are likely to lead to perceptions of congestion, it is not necessary nor sufficient. For example, regular commuters, who have adjusted their expectations to fit the predictably slower moving areas during “rush hour,” may not necessarily view a specific commute as congested, especially if traffic flows faster than usual. In contrast, during periods that are typically low in volume, drivers who are forced to slow their pace or drive closer to other vehicles than anticipated for some unexpected reason may experience a sense of congestion at that particular time and location. In this respect, negative outcomes are not dependent solely on how many vehicles are present on the roadways, which would explain why congestion, and related outcomes such as stress, can be experienced in areas that have relatively small driving populations rather than solely in large city centers.
According to Stokols et al. (1978), one factor that may facilitate the perception of congestion is impedance, which represents the behavioral constraints that occur in traffic due to the distance a driver must travel and the time spent in transit during a given trip. They found that high impedance (longer distances at a slower pace) leads to greater perceptions of congestion, greater physiological arousal, and more negative evaluations of other drivers. Schaeffer, Street, Singer, and Baum (1988) subsequently argued that distance and time are so highly correlated that the average speed of a commute was a better predictor of impedance, which they also found to be related to physiological arousal and decreased performance on postcommute proofreading tasks (identifying errors in spelling, grammar, and punctuation in a written passage). Thus, travel impedance appears to create an aversive condition resulting from blocked goals (i.e., expectations of traveling at a certain speed over a certain distance in a specified time frame) due to constrained movements from other drivers (and their vehicles), which can lead to negative physical, cognitive, and behavioral outcomes.
A major consequence of traffic congestion is heightened stress and anxiety. Several studies have found that drivers report greater stress on days when levels of congestion are highest (Gulian, Matthews, et al., 1989; Hennessy & Wiesenthal, 1999). Using predetermined routes that were naturally either high or low in congestion, Van Rooy (2006) found that participants randomly assigned to the highly congested route reported greater stress and negative mood. Similarly, Hennessy and Wiesenthal (1997) had commuters drive their regular daily routes and administered state measures of driver stress while they drove in areas of both high and low congestion during the same trip. They found that state stress was significantly greater in high-congestion than in low-congestion conditions, but it was moderated by trait driver stress susceptibility. In studies examining bus drivers who routinely experience a wide spectrum of traffic conditions, Evans and colleagues determined that higher congestion was associated with elevated stress but was exaggerated by personal factors of lost control (Evans & Carrère, 1991) and learned helplessness (Evans & Stecker, 2004) in such conditions.
Traffic congestion can also increase anger and aggression in drivers. Deffenbacher et al. (2003) found that anger predicted aggression in simulated driving situations in which participants were impeded by other traffic. Using audio recorders, Underwood et al. (1999) had participants document various experiences from their commute and found that congested encounters were linked to elevated anger during that commute. Gulian, Glendon, Davies, Matthews, and Debney (1989) determined that a large proportion of highway drivers in the United Kingdom frequently experienced irritation in traffic congestion, and that the source of frustration and aggression they reported was typically “other drivers.”Hennessy and Wiesenthal (1999) confirmed this in Canadian drivers interviewed via cellular telephones while engaged in actual low- and high-congestion conditions, where state aggression toward other drivers occurred more frequently in high congestion. This is in line with the notion by Shinar (1998) that roadway aggression may be explained by the frustration aggression hypothesis, in which frustration is more typical when the actions of other drivers block or impede an intended goal. This frustration in turn increases the likelihood of aggression (depending on personal factors, past experience, attributions of others’ actions, and anticipation of outcomes). Several studies have supported the proposition that frustration and irritation from other drivers can lead to driver aggression (Björklund, 2008, Hennessy and Wiesenthal, 2001b, Lajunen and Parker, 2001 and McGarva et al., 2006).
Interestingly, traffic congestion has also been shown to have an enduring impact outside the actual driving environment. Spillover effects have been detected, where congestion-induced elevations in driver stress subsequently impact postdriving outcomes. For example, traffic congestion has been linked to performance decrements in workplace tasks, such as greater errors in proofreading (Schaeffer et al., 1988) and greater time needed to complete simple visual spatial assignments (Hennessy & Jakubowski, 2007), as well as disturbances in interpersonal factors, including increased levels of workplace aggression during that workday (specifically, obstructionism and expressed hostility among men; Hennessy, 2008) and negative evaluations of unqualified job candidates (Van Rooy, 2006). Hennessy (2008) proposed that unresolved stress from the traffic environment likely continues to unconsciously influence and intensify subsequent reactions to stressors experienced after a commute ends, even though the immediate emotional effects from driving dissipate quickly postcommute.
An important consideration in interpreting the impact of congested conditions is the confounding issue of time urgency. For many, the delays caused by other drivers can alter the economic or social experience of time, particularly during “rush hour.” Time pressure or time urgency can modify the perception of traffic events, flow, and actions of other drivers, subsequently increasing stress, irritation, frustration, negative affect, and aggression (Evans and Carrère, 1991, Hennessy et al., 2000, Koslowsky, 1997, Lucas and Heady, 2002 and Neighbors et al., 2002). Everyday hassles, such as time pressure and adverse driving conditions, typically have an additive effect, in which the influence of one event can add to the severity of another. According to Shinar and Compton (2004), driver aggression increases in frequency during periods of high time value (rush hour) even after controlling for traffic volume. In addition, Lucas and Heady (2002) discovered that regular commuters with greater flextime in their work schedule showed less perceived time urgency and resulting driver stress. Clearly, a hurried pace and lifestyle, a focus on time issues, and the escalated potential for blocked goals have the capacity to exaggerate the impact of congestion on negative driving outcomes.
Interpretations of research on traffic congestion, however, must also be qualified by methodological considerations. Like many contextual factors, it is difficult to accurately measure effects of traffic congestion outside of the actual driving experience. The use of hypothetical situations or participant recall of previous trips (even those recently completed) may be problematic because of memory issues, social desirability, experimenter bias, and expectancy effects. However, there are still concerns of control and predictability of real-time traffic events that can alter in situ evaluations of traffic congestion, such as police presence, weather effects, or construction. Also, simulated driving events are limited by the degree they are considered or experienced as “real” by participants, particularly in light of the perceptual nature of congestion in comparison to volume. Nonetheless, there is a degree of consistency across various methods that support the relevance of traffic congestion in negative driving outcomes.

3.2. Physical Environment

Previous research has established a greater risk of collisions due to adverse weather, including rain, snow, and fog (Andrey, Mills, Leahy, & Suggett, 2003), but many drivers attempt to compensate for such conditions by driving more cautiously, such as reducing speed and increasing distance between vehicles (de Waard et al., 2008 and Harris and Houston, 2010). This is in line with the zero risk theory (Näätänen & Summala, 1976), which holds that drivers adapt to risk (i.e., driving more slowly) to the point that their subjective risk approaches zero. However, Kilpeläinen and Summala (2007) argued that many are not accurate in predicting risks during poor weather and subsequently do not adjust their driving behavior properly, particularly on slippery roads.
One possible problem is that although individual motivation might be geared toward increasing personal safety, perception may be impacted by the conditions (e.g., inaccurate interpretations of distances, speed of traffic, and severity of conditions), leading to unintended risky behavior, such as speeds that are too fast for the conditions and reduced headway between vehicles (Caro, Cavallo, Marendaz, Boer, & Vienne, 2009). Cognitive workload may also be impacted during adverse weather conditions because drivers must focus on slower moving traffic and deal with reduced visibility, thus decreasing their capacity to estimate safety margins and increasing strain (Cavallo, Mestre, & Berthelon, 1997). Hill and Boyle (2007) also found that adverse weather and limited visibility can lead to elevated stress levels, which have been independently connected to increased riskiness and collisions.
Heat is another environmental factor that can impact drivers. Heat may be a source of psychological stress and physiological arousal that alters cognitions, emotions, and behavior. Using a traffic simulator, Wyon, Wyon, and Norin (1996) discovered that heat impacted vigilance, where drivers in hot conditions missed a greater number of signals to press a pedal. Heat has also been linked to increased aggressive behavior in drivers. Kenrick and MacFarlane (1986) had a confederate vehicle cause delays at a green light and found increased horn honking among those with open driver-side windows (implying the lack of air-conditioning and “hot” drivers) during periods of higher temperatures. However, it should be noted that the impact of high temperatures may be qualified by confounding factors of humidity, wind, and air pressure, which can alter the experience of “heat.” Similarly, duration of temperature needs to be considered given that prolonged heat will have different outcomes than those resulting from temporary or fluctuating heat. This is highlighted in research on the heat–aggression hypothesis that suggests a curvilinear relationship, where escape may be a more preferable behavior selection than aggression at prolonged extreme temperatures (Bell & Fusco, 1989). It is also important to note that negative outcomes from heat are typically instigated through some social contact or interaction. The target of heat-related aggression is typically others perceived as a source of irritation, frustration, or wrongdoing.
The physical environment may also have beneficial effects on driving outcomes in that pleasant sceneries may favorably impact cognitions, attitudes, affect, and behavior. Although evaluations and perceptions of scenery are subjective, consistent patterns have been found, including a greater general preference for natural as opposed to built urban scenery (Kaplan & Kaplan, 1982). This has been confirmed in the traffic environment, in which drivers report that undeveloped landscapes are less cluttered, as well as more pleasant, useful, and attractive (Evans & Wood, 1980).
Antonson, Mårdh, Wiklund, and Blomqvist (2009) reviewed existing literature related to the effect of roadside scenery on driving behavior and noted that varied and vegetated landscapes can have a beneficial impact. Such landscapes may increase curiosity, decrease boredom, and help maintain focus through anticipation of upcoming threats or challenges. Alternatively, common, predictable, or homogeneous landscapes may lead to monotony, fatigue, and risk. They found that those who traveled a vegetated route in a simulator drove slower and closer to the center of the road than those who traveled an open landscape. However, they also found greater stress, which may be explained by a greater need for vigilance due to a more rapidly changing scenery on an unfamiliar roadway. Alternatively, Parsons, Tassinary, Ulrich, Hebl, and Grossman-Alexander (1998) measured autonomic stress in participants who experienced mild stressors before and after viewing driving scenarios that varied in degree of roadside vegetation. They found that viewing the more vegetated environments did lead to decreased stress responses and increased recovery from that stress. Similarly, Cackowski and Nasar (2003) found that those who viewed routes with more roadside vegetation showed greater subsequent frustration tolerance by spending greater time attempting to solve an insolvable puzzle task. These findings highlight the relevance of the physical environment in understanding the person–situation interaction of drivers.

3.3. Driving Culture and Norms

Culture represents shared norms, values, traditions, and customs of a group that typically define and guide appropriate and inappropriate attitudes and behaviors. These can occur on a macro level (e.g., national customs and religious holidays) or a more micro level (e.g., family traditions and peer activities). Driving behavior and driving style should be influenced by these cultural processes, given that the driving environment is a social context with very distinct rules and norms that are transmitted between road users across time and generations. Also, although driving laws, licensing procedures, road types, driving styles, and actual driving behaviors will vary regionally and internationally, dangerous and risky driving practices occur universally. Attitudes about driving and personal driving styles are largely learned, which includes influence from parents, peers, media, and other drivers regarding the overall riskiness of driving, as well as the probability of experiencing negative outcomes (Hennessy et al., 2007 and Shope and Bingham, 2008). Using Ajzen’s (1985) theory of planned behavior as a framework, Elliott, Armitage, and Baughan (2007) found that subjective norms (the perceived pressure or acceptance of others toward a behavior) were associated with elevated speeding intentions, which subsequently predicted both self-reported and observed speeding behavior in a simulator. In essence, the normative belief that there is a consensus or commonality to unsafe driving behavior may serve as a justification for its personal adoption (Forward, 2009).
Parents represent one potential source of cultural and normative influence on driving behavior, particularly for young drivers. Parental influence on driving begins early in life, well before formal “training years,” through modeling of driving styles, attitudes about safety, reactions to other drivers, and respect for traffic laws (Summala, 1987). Familial models that validate recklessness often encourage riskiness of young drivers (Taubman-Ben-Ari, 2008). Bianchi and Summala (2004) further proposed that parental influence may impart from genetic dispositions that guide personal tendencies of parent drivers, such as sensation seeking or attention, which are then demonstrated to their children. Research has consistently shown that parental attitudes and activities outside the driving environment, particularly lenience of restrictions and control, can impact driving behavior and style of young drivers (Hartos, Eitel, Haynie, & Simons-Morton, 2000). Shope, Waller, Raghunathan, and Patil (2001) found that parental monitoring, nurturing, and connectedness in 10th grade of high school were subsequently linked to lower rates of serious offenses (alcohol related, speeding, and reckless driving) and crashes (single vehicle, at fault, and alcohol related), whereas lower monitoring, nurturance, connectedness, and a greater lenience toward drinking had the opposite effect. Similarly, Prato, Toledo, Lotan, and Taubman-Ben-Ari (2010) determined that risk indexes for young drivers were lower for those whose parents were actively involved in monitoring their child’s driving behavior, whereas lack of supervision exaggerated existing dangerous driving tendencies in their child, such as sensation seeking, increasing overall risk.
Another important source of cultural and normative driving influence is the media, which is prone to promote danger and risk over safety as a “normal” part of driving. Although the media has the potential to help promote a culture of driving safety, in many cultures, television and movies glorify and promote speeding, risk taking, and dangerous driving practices as acceptable or even admirable (Hennessy et al., 2007), particularly for young male drivers. Consistent with social and cognitive learning theories, one primary mechanism by which behavior is acquired is through observing and imitating others. By placing emphasis on the consequences of others’ actions, observation serves as a vicarious learning experience (Bandura, 2001). Hennessy et al. found that speeding, lane violations, and near collisions were elevated in a simulator among drivers previously exposed to a short movie scene of dangerous driving. It is possible that watching media portrayals that endorse driving that is competitive, performed at excessive speeds, or otherwise unsafe increases viewers’ perceived acceptability of such actions and reduces the expectancy of a tragic outcome, which then increases the likelihood they will engage in dangerous driving themselves.

4. Conclusions and Future Directions

This chapter highlighted the importance of personal and social influences on driving outcomes. Each driver brings a unique and rich experience base, skill set, expectation, interpretation, and reaction to the distinctive state driving context, yet research has been successful in identifying consistent patterns that can heighten the likelihood of negative driving outcomes, only a fraction of which were presented here. The purpose of such research is to concurrently understand problematic aspects within the person and the context to ultimately improve traffic safety. However, there are still many more factors in this process to be discovered.
One future focus could be on understanding aspects that influence more positive or safe driving emotions and actions. For example, Pêcher et al. (2009) found that listening to happy music decreased mean speed and hard shoulder deviations, suggesting that positive emotions could ultimately distract drivers from dangerous activities. Furthermore, Özkan and Lajunen (2005b) argued that promotion of polite driving through media sources could be a means of reducing aggressive driving. Likewise, psychology often emphasizes negative outcomes, sometimes at the expense of characteristics that can improve or increase more appropriate consequences. Anger, aggression, and revenge have been examined more frequently than their constructive counterpart, forgiveness. It is possible that understanding the process by which drivers forgive transgressions of others in the traffic context (rather than become agitated, ruminate, and harm others) may ultimately hold greater long-term benefit in helping to improve driver interactions (Moore & Dahlen, 2008).
Another direction could be increased expansion of the person–situation interaction to include the vehicle. Whereas safety issues surrounding the vehicle have been the focus of engineering and human factors, traffic psychology as a whole has been less inclined to incorporate such aspects into research. The vehicle is more than just the means of transporting individuals; rather, it is part of a driver’s life space. Issues such as comfort, layout of instruments, safety features, overall condition of the vehicle, or technology can alter the driving experience variably across situations. In many instances, the vehicle becomes an extension of the driver and his or her space, with personal meaning that can lead to protection and defense (Marsh & Collett, 1987). It can also provide a degree of anonymity from others that allows expression of emotions and actions not typical in other situations (Li et al., 2004). In this respect, a full understanding of the transactional aspects of driving may not be possible without inclusion of the vehicle.
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