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The Influence of Traffic Congestion, Daily Hassles, and Trait Stress Susceptibility on State Driver Stress: An Interactive Perspective1


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State driver stress was measured in both low and high traffic congestion using cellular telephones. The contributions of time urgency, trait driver stress, and hassles were also examined. Drivers showed substantially more state driver stress under high than low congestion. Time urgency made a significant positive contribution to state driver stress at both congestion levels. Trait driver stress also contributed positively under low congestion. There was a significant hassles X trait stress interaction under high congestion. Hassles exposure moderately increased state driver stress for high trait stress drivers, but reduced state driver stress for medium and low trait stress drivers. These findings indicate that state driver stress is influenced by a combination of situational and personal factors, including factors external to the driving context.
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Journal of Applied Biobehavioral Research, 2000, 5, 2, pp. 162-179.
Copyright 2000 by Bellwether Publishing, Ltd. All rights reserved.
The Influence of Traffic Congestion, Daily Hassles, and
Trait Stress Susceptibility on State Driver Stress:
An Interactive Perspective1
York University
Toronto, Ontario, Canada
State driver stress was measured in both low and high traffic congestion using cellular
telephones. The contributions of time urgency, trait driver stress, and hassles were also
examined. Drivers showed substantially more state driver stress under high than low con-
gestion. Time urgency made a significant positive contribution to state driver stress at both
congestion levels. Trait driver stress also contributed positively under low congestion.
There was a significant hassles X trait stress interaction under high congestion. Hassles
exposure moderately increased state driver stress for high trait stress drivers, but reduced
state driver stress for medium and low trait stress drivers. These findings indicate that state
driver stress is influenced by a combination of situational and personal factors, including
factors external to the driving context.
Most driver stress research has viewed stress as the outcome of a negative
cognitive appraisal of driving situations (Glendon et al., 1993; Gulian, 1987;
Hennessy & Wiesenthal, 1997). It is only when driving is interpreted as demand-
ing or dangerous that stress manifests itself in negative affect, such as anxiety
and worry (Gulian, Matthews, Glendon, Davies, & Debney, 1989), or physiolog-
ical responses, such as increased heart rate and blood pressure (Robertson, 1988).
Individuals who describe driving as highly stressful have been found to report a
higher incidence of speeding violations (Matthews, Dorn, & Glendon, 1991) and
minor traffic accidents (Gulian, Glendon, Matthews, Davies, & Debney, 1990;
Selzer & Vinoker, 1974).
1This manuscript is based, in part, on research conducted by Dwight Hennessy, in partial fulfil-
ment of the Ph.D. degree requirements of York University. The research was supervised by David
Wiesenthal. The authors wish to thank Professors Esther Greenglass and James Check who served on
Dwight Hennessy’s dissertation committee, as well as Bob Lamble of the Ontario Ministry of Trans-
portation. Further, valuable assistance was provided by Alex Shanahan, Suzan Krepostman, Amy
Harris, and Mike Foundos. This research was partially supported by a grant from the Ministry of
Transportation of Ontario. Opi nions exp ressed in this report are those o f the authors and do not neces-
sarily reflect the views and policies of the Ministry.
2Correspondence concerning this article should be addressed to Dwight A. Hennessy, who is now
at the Department of Psychology, State University of New York College at Buffalo, 1300 Elmwood
Avenue, Buffalo, NY 14222.
Although no single factor will necessarily be interpreted as stressful by all
individuals, some factors have been identified that frequently lead to negative
interpretations and stress. One of the most common contributors to driver stress
is traffic congestion (Gulian, Debney, Glendon, Davies, & Matthews, 1989;
Hennessy & Wiesenthal, 1999; Stokols, Novaco, Stokols, & Campbell, 1978).
According to Novaco, Stokols, and Milanesi (1990), most regular commuters
experience some level of daily traffic congestion. Congested traffic is often inter-
preted as a negative event in that it tends to slow or block the attainment of goals,
such as driving at a certain speed or getting to a destination at a scheduled time
(Novaco, Stokols, Campbell, & Stokols, 1979). Those who are forced to drive
below a desired speed, especially for long distances, tend to report greater levels
of driver stress. Gulian, Debney, et al. (1989) found that 50% of highway drivers
in the United Kingdom frequently experience irritation in traffic congestion,
regardless of time demands. Other driving scenarios that are often perceived as
undesirable include merging with fast moving traffic, failing to overtake other
drivers, bad weather, and poor road conditions, such as those found in narrow
construction lanes (Gulian, Debney, et al., 1989).
According to Gulian, Matthews, et al. (1989), interpretations of driving situa-
tions may also be influenced by factors unrelated to driving, such as problems
experienced within the work or home environments. They have advocated the use
of the term “driver stress” rather than “driving stress,” because stress is influenced
by the whole life experiences of the driver rather than factors exclusively within
the driving situation (Gulian, Glendon, Matthews, Davies, & Debney, 1988).
Individuals experience a wide range of daily hassles, or minor daily pres-
sures, that can accumulate and lead to the experience of stress (Flannery, 1986;
Johnson & Stone, 1987). Frequently, new stressors must be dealt with before old
issues are resolved (Cohen, 1980). The influence of hassles that are not effec-
tively dealt with can persist, even when no longer in conscious awareness, and
add to the pressures of subsequent hassles (Kohn & Macdonald, 1992b; Lazarus,
1981; Taylor, 1991). These “after-effects” can continue to do psychological and
physiological damage, and may intensify over time as they accumulate with other
previously unresolved stress reactions (Glass & Singer, 1972). Gulian et al.
(1990) found that participants who reported a difficult day at work subsequently
reported greater levels of fatigue and stress during their commute home. When
unresolved “nondriving” hassles carry forward into the driving situation, events
are more likely to be interpreted as negative, increasing the potential for driver
stress. As a further complication and danger, driver stress can also carry over
from the driving situation and create difficulties within the work or home envi-
ronments which may then, in turn, influence further driver stress interpretations
(Novaco et al., 1990).
Acute driver stress has been found to have a cumulative effect, producing
a lasting general propensity or personality disposition toward driver stress
(Glendon et al., 1993; Gulian et al., 1990). Those who repeatedly experience
driving as stressful may develop an overall negative view of driving, which
heightens the probability of experiencing driver stress (Matthews et al., 1991).
Hennessy and Wiesenthal (1997) found that certain driving situations were more
likely to be experienced as stressful, but individuals with a greater propensity
toward driver stress were more likely to report stress reactions within actual driv-
ing situations. Individuals who possess this “trait driver stress” susceptibility
have been found to show heightened levels of arousal, and unpleasant mood
when driving (Gulian, Matthews, et al., 1989; Matthews et al., 1998). The ulti-
mate consequence lies in the fact that the negative interpretations that are influ-
enced by this disposition, heighten the occurrence of driver stress which, in turn,
serves to maintain the disposition. As the cycle continues, the deleterious
sequelae of driver stress tend to intensify.
Previous research has established that situational and personal factors are
necessary to accurately determine driver stress levels (Matthews et al., 1998).
State driver stress is generally greater in high than in low congestion, although
this effect is much more pronounced for high trait stress drivers than for low trait
stress drivers (Hennessy & Wiesenthal, 1997). In addition, Gulian, Matthews,
et al. (1989) found that problems experienced in nondriving situations can also
magnify the perception of driver stress. Therefore, it was hypothesized that state
driver stress would be greater among high trait stress drivers in both low and high
congestion. However, in high congestion, where frustration and irritation are typ-
ically greatest, state driver stress would be predicted by the interaction of trait
driver stress susceptibility and daily hassles. High trait stress drivers, under the
added influence of elevated daily hassles, will report greater state driver stress.
Previous research has also demonstrated that time urgency can magnify nega-
tive interpretations of driving events (Hennessy, 1995; Koslowsky, 1997). Time
urgent drivers may be more likely to perceive others as an obstacle to reaching a
destination on time; thus, it was hypothesized that time urgency would lead to
greater state driver stress.
Hypothesis 1. State driver stress will be greater in high than in low
traffic congestion.
Hypothesis 2. Time urgency will lead to greater state driver stress.
Hypothesis 3. In high congestion only, state driver stress will be
predicted by the interaction of daily hassles and trait driver stress
susceptibility. Specifically, state stress will be greater among high
trait stress drivers, but exaggerated among those with elevated
daily hassles.
Hypothesis 4. In low congestion, state driver stress will be greater
among high trait stress drivers.
Participants consisted of 28 females and 28 males who commuted regularly
along Highway 401 in Metropolitan Toronto, between home and work/school in
the North York region. The age range was from 19 to 55 years, with an average of
26.5 years. Participants were obtained through course recruitment, word of
mouth, or campus advertisements at York University.
Nokia Cellular telephones (Model LX12/C15) were equipped with a cigarette
lighter power adapter for continuous power access, and a stationary antenna. A
visor mounted microphone provided hands free capability.
1. Driving Behaviour Inventory—General (DBI-Gen). “Trait” driver stress
was measured using the Driving Behaviour Inventory-General Driver Stress
scale (DBI-Gen; Gulian, Matthews, et al., 1989). The DBI-Gen consists of 16
items that tap a general disposition, or “trait” susceptibility, to driver stress. Pre-
vious research has established the DBI-Gen as a valid, robust, and reliable mea-
sure of trait driver stress (Glendon et al., 1993; Hennessy & Wiesenthal, 1997,
1999; Matthews et al., 1991). In the present study, responses were made on a Lik-
ert scale ranging from 0 to 100, indicating agreement or disagreement with each
statement, rather than on the original 0 to 4 Likert scale. Previous research has
shown this scaling revision to maintain high reliability (α = .90; Hennessy &
Wiesenthal, 1997). Scoring consisted of the mean response to the 16 items, with a
possible range of 0 to 100. Higher scores indicated greater trait driver stress sus-
2. State Driver Stress Questionnaire. The State Driver Stress Questionnaire
was intended to assess the state experience of driver stress; thus, it was designed
to be administered verbally in actual driving situations (Hennessy & Wiesenthal,
1997). It consisted of 11 items similar to those from the DBI-Gen and 10 items
from the Stress Arousal Checklist (Mackay, Cox, Burrows, & Lazzerini, 1978).
Half of the Stress Arousal Checklist items indicated positive mood (relaxed, con-
tented, peaceful, comfortable, and calm) and the other half indicated negative
mood (tense, bothered, nervous, uneasy, and distressed). All items were worded
to the present tense, in order to represent “state” measures of stress. For example,
“Trying but failing to overtake is frustrating me” was used rather than “When I
try but fail to overtake, I am usually frustrated.” Stress Arousal Checklist items
were transformed from an adjective, to a statement regarding present feelings
(e.g. “I am feeling nervous” rather than “Nervous”). For the present study,
responses were provided verbally, using a Likert type scale ranging from
0(strongly disagree) to 100 (strongly agree), indicating the extent to which each
item pertained to the driver’s experience in the immediate driving situation. Pre-
vious research has found the State Driver Stress Questionnaire to demonstrate
high reliability in low (α = .92 to .94) and high (α = .95 to .97) traffic congestion
conditions (Hennessy & Wiesenthal, 1997, 1999; Wiesenthal, Hennessy, &
Totten, 2000). For scoring purposes, the positive mood items were reverse keyed.
Scoring consisted of calculating the mean response of the 21 items, with a possi-
ble range of 0 to 100. Higher scores indicated greater state driver stress.
A manipulation check item was added to determine whether low and high
traffic congestion conditions were perceived as differing in congestion level. Par-
ticipants were asked to rate the level of congestion, from 0 to 100, in both low
and high congestion as it was experienced. A higher rating indicated greater con-
gestion. Also, since time urgency has been linked to driver stress (Evans &
Carrere, 1991; Koslowsky, 1997), three items were added to evaluate its influ-
ence (Hennessy & Wiesenthal, 1997).
3. Survey of Recent Life Experiences (SRLE). The Survey of Recent Life Ex-
periences (Kohn & Macdonald, 1992a) is a self-report measure of exposure to
daily hassles, that has been developed as an alternative to the Daily Hassles Scale
(Kanner, Coyne, Schaefer, & Lazarus, 1981). Critics have argued that the Daily
Hassles Scale is contaminated by items pertaining to psychological and physical
distress, and by a response format that may reflect such distress rather than pre-
dict it (e.g., Dohrenwend, Dohrenwend, Dodson, & Shrout, 1984; Dohrenwend
& Shrout, 1985; Green, 1986; Kohn & Macdonald, 1992a). A great deal of stress
and hassles research has been focussed on determining potential links between
stress and psychological or physiological symptomology, therefore conceptual
overlap and common items found in the Daily Hassles Scale may lead to inflated
relationships. Also, response options to the Daily Hassles Scale provide no alter-
native for those who have not recently experienced a particular item as distress-
ing (Kohn & Macdonald, 1992b). Judgements of the severity of individual
hassles may actually reflect, rather than predict, subjective distress; accordingly,
Kohn and Macdonald (1992a) based their response format on the degree of expo-
sure to each hassle rather than its judged severity.
The present study used the shortened version of the SRLE (Kohn &
Macdonald, 1992a), which consists of 41 items intended as a measure of accumu-
lated hassles over the course of a given time period. Participants were required to
indicate the extent that each item had been part of their lives over the past month.
Responses were scored on a Likert type scale, from 1 (not at all) to 4 (very
much). Scoring consisted of the sum of ratings across items with a possible range
of 41 to 164. Higher scores indicated greater experience of hassles over the past
month. The SRLE has been found to have high internal consistency (α = .91), and
to correlate significantly with trait anxiety, perceived stress, psychiatric sympto-
mology, and minor physical ailments (Kohn, Gurevich, Pickering, & Macdonald,
1994; Kohn, Hay, & Legere, 1994; Kohn & Macdonald, 1992a). deJong,
Timmerman, and Emmelkamp (1996) also found that a translated version of the
SRLE displayed strong reliability and construct validity within a Dutch sample.
The present study was designed to measure driver stress in actual low and
high congested conditions through the use of cellular telephones. Research par-
ticipants were enlisted through course recruitment or personal contact. During an
initial appointment, informed consent was obtained, and instructions regarding
the experimental procedure and cellular telephone operation were given. Follow-
ing the instruction period, participants completed the DBI-Gen in order to assess
their trait susceptibility toward driver stress, and the SRLE to evaluate hassles
exposure over the previous month. Participants then provided information
regarding their regular travel route along Highway 401, because all measures
were administered during their usual daily commute. Highway 401 was chosen
because it is the major east-west traffic artery for Metropolitan Toronto, with as
many as 14 lanes, divided into a series of express (core) and collector lanes. The
average daily traffic on this highway for the Metropolitan Toronto area in 1991
was over 255,000 vehicles (Ontario Ministry of Transportation, 1992). For each
participant, two areas along their regular commuting route were chosen: one that
is typically low and one that is typically high in traffic congestion. A landmark
unique to each chosen area was then selected, that would be subsequently used
during the participant’s actual journey as a cue to initiate a cellular telephone call
to the experimenter. Both the low and high congestion telephone interviews were
scheduled during a single journey.
An equal number of participants were randomly assigned to either a morning
or an evening measurement group, in which they would be measured during
either their regular morning or evening commute, respectively. This was done in
order to control for the potential impact of the time of day on driver stress levels.
Previous research has found that driver stress is slightly higher during evening
commutes compared to morning commutes (Gulian et al., 1990). Within these
groups, drivers were further divided into those who typically encountered high
prior to low traffic volumes in the course of their daily commute (n = 28), and
those who encountered low prior to high volumes (n = 28). This process elimi-
nated possible confounding effects due to fatigue or practice on state driver stress
by counterbalancing order of exposure to low and high congestion. Prior to initi-
ating their commute, participants were instructed to make a practice cellular tele-
phone call to the experimenter in order to ensure that the telephone was
functioning properly and to avoid any confusion regarding its use while actually
driving. No measurement took place during the pretest telephone call. Partici-
pants were reminded of the response scale to the State Driver Stress Question-
naire and instructed to commence their journey as usual. Upon approaching their
first designated landmark (i.e., for the low or high congestion area), participants
telephoned the researcher, through a single-button speed-dial operation. When
successful telephone contact was made, the State Driver Stress Questionnaire
was administered verbally, while drivers were engaged in the actual driving pro-
cess. Upon completion of the first telephone interview, the cellular telephone call
was terminated and the participants continued driving until their second land-
mark was reached, which prompted the second telephone call. The State Driver
Stress Questionnaire was, again, administered verbally. Termination of the sec-
ond telephone call concluded participation.
All measures were obtained between February and March of 1998. Partici-
pants were tested once, either on a Tuesdays, Wednesdays, or Thursdays. Week-
ends were excluded because most participants were not available, and Mondays
and Fridays were excluded because driving-induced stress and stress elicited by
extrinsic factors have been found to be most consistent between Tuesdays and
Thursdays (Gulian et al., 1990). In order to eliminate the possibility of poor
weather increasing stress, participants were tested only on partly cloudy to sunny
Intercorrelations, means, standard deviations, and alpha reliabilities for the
State Driver Stress Questionnaire, DBI-Gen, SRLE, and Time Urgency appear in
Table 1. Alpha reliabilities for all measures were high, ranging from .80 to .92.
Separate state driver stress scores were obtained from the State Driver Stress
Questionnaire for both low and high congestion. Scores were calculated as the
mean response across individual items in the particular congestion condition.
Higher scores indicated greater state stress in that congestion condition.
In order to examine the influences of congestion level and driver sex on state
driver stress, a split plot factorial analysis was performed with the two levels of
congestion as the within groups variable and driver sex as the between groups
variable. Consistent with Hypothesis 1, Table 2 demonstrates that state driver
stress was greater in high than in low congestion, F(1, 54) = 94.62, p < .01. In
fact all 56 participants showed greater state driver stress in high congestion, and
according to η2, congestion level accounted for 64% of the observed variablity in
state driver stress. In contrast, driver sex and the Congestion Level × Driver Sex
Table 1
Intercorrelations, Means, Standard Deviations, and Reliabilities of the State Driver Stress Questionnaire, DBI-Gen, SRLE,
and Time Urgency
1. State driver stress questionnaire: Low
congestion —————
2. State driver stress questionnaire: High
congestion .78** ————
3. Driving behaviour inventory-general (DBI-Gen) .50** .65** — — —
4. Survey of recent life experiences (SRLE) .06 .18 .29* — —
5. Time urgency: Low congestion .44** .09 .18 — —
6. Time urgency: High congestion .31* .09 .16 .55**
M28.41 43.08 43.30 77.62 47.09 48.09
Mdn 24.00 41.25 44.90 77.00 50.00 50.00
SD 15.72 17.53 16.41 17.56 17.09 17.00
Minimum 1.00 8.00 2.00 47.00 0.00 3.00
Maximum 62.00 84.00 78.00 115.00 83.00 100.00
α.92 .90 .80 .92 — —
Note. n = 56.
*p < .05. **p < .01.
interaction had no significant impact on state driver stress, F(1, 54) = 1.08, ns,
and F(1, 54) = 0.78, ns, respectively.
In order to determine predictors of state driver stress within low and high con-
gestion conditions, separate multiple regressions were computed for each. The
main effect predictors were daily hassles, trait driver stress, time urgency, and
driver sex. In addition, all possible product terms for two way interactions were
included in each initial model.
A hierarchical entry stepwise procedure was used to produce final models,
which included only two classes of effects: those that were statistically signifi-
cant and those that, significant or not on their own, were implicated in a signifi-
cant interaction. The procedure was to enter all main effects forcibly and add the
interactions stepwise on the first run. If any interactions proved significant, they
would be entered forcibly on the second run along with the implicated main
effects. All other significant main effects would be added stepwise on the second
run. However, in the event that no interactions proved significant on the first run,
the main effects would be entered stepwise on the second run. This strategy has
been reported in greater detail elsewhere (e.g., Kohn & Macdonald, 1992b; Kohn
et al., 1994).
High Congestion
The final regression model for high congestion appears in Table 3. Hassles
and time urgency both made significant contributions to state driver stress, as did
the Hassles × Trait Driver Stress interaction. Consistent with Hypothesis 2,
highly time urgent drivers demonstrated greater state stress than did those under
less time urgency. Also, Hypothesis 3 was confirmed in that state driver stress
was predicted by the interaction of daily hassles and trait drivers stress suscepti-
bility (Figure 1).
Table 2
Mean State Driver Stress Levels Between Congestion Conditions and Driver Sex
Congestion level
Low 28.41 15.72 56
High 43.08 17.53 56
Driver sex
Female 33.58 17.57 28
Male 37.92 15.61 28
To generate Figure 1, which shows the line of best fit for state drivers stress
as a function of hassles and trait driver stress, the regression equation in Table 3
was applied to 24 idealized cases generated as follows: a hassles score that is a
multiples of 10 in the range of 50 to 120 (where the observed range was 47 to
115); a trait driver stress score either 1 SD below the mean, at the mean, or 1 SD
above the mean; and the mean value for time urgency. The 24 idealized cases
thus represent 8 levels of hassles × 3 levels of trait driver stress. The mean value
Table 3
Predictors of State Driver Stress Within High Congestion
Criterion Predictor btR
2 change
State driver stress:
High congestion Hassles × Trait Stress 0.010 2.04* .362
Hassles -0.531 -2.09* .121
Time urgency 0.283 2.80** .05
Trait stress -0.090 -0.023 .001
Intercept 40.232 2.05
Note. R2 = .533, F(4, 51) = 14.56, p < .01.
*p < .05. **p < .01.
Figure 1. State driver stress as a function of daily hassles and trait driver stress suscep-
tibility under conditions of high traffic congestion.
for time urgency was included as a constant across the 24 cases because,
although the variable was not implicated in the Hassles × Trait Stress interaction,
it figured significantly in the final regression model shown in Table 3.
Figure 1 illustrates that, although a Hassles × Trait Driver Stress interaction
was found, its form is not quite as expected. Low trait stress drivers exhibited
marked reduction in state driver stress as hassles exposure increased and medium
trait stress drivers showed little reduction; however, the high trait stress drivers
did show a modest increase in state driver stress with increased hassles exposure.
Collectively the predictors in the final model for high congestion accounted for
approximately 53% of the variability in state driver stress.
Low Congestion
The final regression model for low congestion appears in Table 4. Consistent
with Hypothesis 2, state driver stress was greater among highly time urgent driv-
ers. Hypothesis 4 was also confirmed, in that high trait stress drivers demon-
strated elevated state driver stress. These predictors accounted for approximately
42% of the observed variability in state driver stress under low congestion.
According to Lazarus (1966), events or stimuli that are perceived as undesir-
able or taxing on personal resources typically result in some degree of psycholog-
ical stress. In this respect, the stress response is not a stable entity that is
automatically induced by external forces. Rather, particular response levels
depend on individual interpretations of each experience (Mason, 1975). Automo-
bile driving has been identified as a common event that is frequently interpreted
as stressful (Gulian, Matthews, et al., 1989; Hennessy & Wiesenthal, 1997;
Table 4
Significant Predictors of State Driver Stress Within Low Congestion
Criterion Predictor btR
2 change
State driver stress:
Low congestion Trait stress 0.451 4.46** .257
Time urgency 0.369 3.80** .159
Intercept -8.46 -1.36
Note. N = 56. R2 = .417, F(2, 53) = 18.92, p < .01.
**p < .01.
Novaco et al., 1990). Within any driving encounter, there is a multitude of stimuli
that may be perceived as undesirable, including bad weather, time pressures, and
slow moving vehicles. Perhaps one of the most pressing and frequent dilemmas
faced by drivers is traffic congestion (Gulian, Matthews, et al., 1989; Novaco
et al., 1979). The number of private automobiles used on a daily basis has been
steadily multiplying, with little increase in the construction of public roads and
highways (Donelly, 1998; Taylor, 1997). As a result, congestion levels, competi-
tion for space, and potential sources of frustration, irritation, and stress have
escalated. In the present study, driver stress was greater in high than in low con-
gestion for all participants. Indeed, congestion level accounted for 64% of the
observed variability, which represents a very strong predictor of state driver
stress. For many, elevated traffic volume can slow travel pace and block goals,
such as getting to work on time or travelling at a desired pace, which increases
the potential for negative interpretations of the driving experience and, ulti-
mately, driver stress (Broome, 1985).
Previous research has identified time constraints as a major precursor to
driver stress (Hennessy & Wiesenthal, 1999; Koslowsky, 1997). In the present
study, as hypothesized, time urgency predicted state driver stress in both low and
high traffic congestion. Specifically, hurried drivers were more likely to report
elevated state driver stress were than those with a flexible time schedule. To a
driver concerned with time, other drivers represent obstacles that block the goal
of reaching a destination on time, which can lead to heightened frustration, anxi-
ety, and negative affect (Broome, 1985; Novaco et al., 1979). According to
Gulian, Debney, et al. (1989), techniques designed to properly manage time con-
cerns should help minimize the occurrence of driver stress. The most direct solu-
tion would be the allotment of greater time to reach a destination, particularly on
days where traffic volume is typically greatest (Mizell, 1996). Similar
approaches could include scheduling of events during nonpeak times, when
traffic volume is typically low, or planning greater flexibility in the commence-
ment of appointments.
As expected, and consistent with previous research (Hennessy & Wiesenthal,
1997), state driver stress was also linked to a trait susceptibility toward driver
stress. Specifically, high trait stress drivers were more likely to report experienc-
ing stress in actual driving situations compared to low trait stress drivers.
According to Glendon et al. (1993), a trait susceptibility to driver stress can
develop as a result of recurrent negative driving experiences, which can then, in
turn, heighten the potential to interpret isolated driving situations as stressful.
The present findings also provided support for an interactional interpretation
(Endler & Edwards, 1986; Endler & Parker, 1992; Lewin, 1935) of driver stress,
where elements of the person, and elements of the situation are necessary to
determine stress levels. As with previous research (Hennessy & Wiesenthal,
1997; Stokols et al., 1978) the situation, notably in terms of congestion, was
instrumental in determining state driver stress levels, but the degree of reaction
was also dependent on personal experience and trait susceptibility. Specifically,
within high congestion state driver stress was predicted by the interaction of
daily hassles and trait stress. Consistent with Gulian, Matthews, et al. (1989),
nondriving demands (i.e., hassles) were carried forward to the driving environ-
ment and impacted negatively on driver stress levels, although only among high
trait stress drivers, who are most susceptible to experience driving as a negative
event. Unexpectedly, however, hassles exposure actually led to decreased state
driver stress among medium and especially low trait stress drivers.
One possible explanation for the unexpected form of the interaction may be
that low trait stress drivers, and to a lesser extent medium trait stress drivers,
demonstrate greater adaptiveness (Kohn, 1996) when confronted with the focal
stressor of high traffic congestion. According to Kohn, self-control and passive
responses are more adaptive in situations in which no effective active solution
exists. In this respect, rather than exert a great deal of cognitive energy in
attempting to actively confront the demands of high congestion, low trait stress
drivers may distract themselves by reflecting on nondriving related problems
(i.e., daily hassles). As a result, with increased hassles exposure, the tendency to
focus coping resources on previous hassles may increase while the tendency to
focus resources on demands within the driving environment may actually
decrease, leading to decreased perceptions of driver stress.
In contrast, high trait stress drivers may presumably cope less adaptively with
the demands of high traffic congestion. Owing to negative past driving experi-
ences, high trait stress drivers are generally more prone to perceive current driv-
ing events in a negative manner. This tendency may lead them to focus attention
more distinctly on current driving events compared to low trait stress drivers.
Thus, previous hassles may amplify the immediate demands of high congestion
rather than serve as a distractor. Having to consider previous hassles in addition
to those within high congestion may lead high trait stress drivers to feel over-
whelmed and perceive their resources as inadequate to meet the demands of the
driving situation, leading to elevated state driver stress.
The Need to Minimize Driver Stress
Repeated exposure to stress, without effective coping, has been linked to a
variety of physiological and psychological pathologies (Everly, 1986; Lipowski,
1984), including increased heart rate, blood pressure, anxiety, and negative affect
(Henry & Stephens, 1977; Spence, 1988; Stokols et al., 1978). The fact that
driver stress has also been found to influence performance, mood, and health in
work and home environments (Novaco et al., 1990; Schaeffer, Street, Singer, &
Baum, 1988) heightens the importance of developing techniques for dealing with
personal and situational antecedents to state driver stress. For example, stress
reducing techniques should concentrate on minimizing trait driver stress suscep-
tibility, due to its link with elevated state stress in both low and high congestion.
Since repeated negative experiences contribute to a trait susceptibility, it may be
possible to reduce this disposition through frequent positive driving experiences,
such as leisurely travel on weekends. In extreme cases, group or individual coun-
selling sessions can help deal with life problems and provide stress reducing
techniques that can be performed prior to, during, and immediately following
driving excursions, such as time management, trip planning, listening to music,
muscle relaxation techniques, meditation and adaptiveness training (Gulian,
Debney, et al., 1989; James, 1999; Kohn, 1996; Mizell, 1996; Wiesenthal et al.,
Additionally the problem of traffic congestion must also be addressed, due to
its substantial importance in determining driver stress levels. One approach may
be to offer special reduced rates for public transportation during periods of high
traffic volume, or to increase construction of car pool lanes to encourage ride
share programs. Further, more efficient design, planning, and construction of
roadways may be necessary to deal adequately with the growing volume of traf-
fic (Mackey, 1999). However, this latter suggestion is likely to be met with a
degree of resistance, due to the high costs involved in the redesign and/or recon-
struction of present transportation routes.
Limitations of the Present Study
Despite the fact that the usefulness of an interactional approach to driver
stress research was demonstrated, the present study represented a narrow evalua-
tion of personal and situational influences. Future research is needed to examine
greater variation in the level of situational constraints. Specifically, the present
study intentionally selected two driving situations that were extreme contrasts
(i.e., low and high congestion); therefore, it was not possible to determine the
effects that intermediate levels of congestion might have had on stress and driv-
ing behavior. By considering greater variation in congestion level, it may be pos-
sible to evaluate the threshold values at which background stressors, such as
daily hassles, affect state driver stress. Another limitation was the fact that state
measures were collected during a single commute, representing only a limited
and short-term assessment. According to Willumeit, Kramer, and Neubert
(1981), daily fluctuations in both personal factors (e.g., dispositions, fatigue) and
situational factors (e.g., weather, road conditions) can alter driving behavior. A
longitudinal analysis, where single drivers are evaluated over an extended period
of time, may be necessary to more fully understand the process of driving
responses. Finally, the present study did not fully address aspects of the vehicle
that might interact with the person and the situation to influence driver stress,
such as seat comfort, air conditioning, vehicle noise, all wheel drive, air bags,
and antilock brake systems (Cantilli, 1981; Evans, 1991; Huddart & Dean, 1981).
Further research is needed to specifically examine the role of the vehicle in the
interactional process between the individual and the situation.
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... Although, no single environmental factor will necessarily be interpreted as stressful by everyone, some factors have been shown to frequently lead to stress (Hennessy et al., 2000). One of the most common contributors to driver stress is traffic congestion as it tends to slow or block goal attainment (Hennessy et al., 2000). ...
... Although, no single environmental factor will necessarily be interpreted as stressful by everyone, some factors have been shown to frequently lead to stress (Hennessy et al., 2000). One of the most common contributors to driver stress is traffic congestion as it tends to slow or block goal attainment (Hennessy et al., 2000). Gulian et al. (1989) found that half of highway drivers in the United Kingdom experience irritation in traffic congestion regardless of time demands. ...
... Each of the stress factors mentioned above can negatively impact driving performance, but studies that explore the impact of interactions between these stressors are more ecologically valid, due to the intersection of these factors in real life situations. A study by Hennessy et al. (2000) found that traffic congestion, daily hassles, and trait stress susceptibility all interact and impact state driver stress. The study included 56 participants who drove in low or high traffic congestion areas. ...
... Consequently there were identified six factors which represent 45,2% from the data variance (see table 1). The highlighted factors are consistent with those presented by Kohn and Macdonald (1992) and Hennessy, Wiesenthal, and Kohn (2000). ...
... Internal consistency Kohn and Macdonald (1992) reported the fidelity of SRLE between .90 and .91 for the total score. Hennessy, Wiesenthal, and Kohn (2000) indicated that the scale could be applied by using the original items and the fidelity of SRLE scales and the total score was high, confirming the results obtained by the authors of the scale. ...
... The correlation between the items and the total score of the SRLE (between .22 and .62) is significant at the limit p<.01 indicating the utility for maintaining the items in the appreciation instrument of recent life experiences. The data presented in table 3 are close to those reported by Hennessy et al. (2000). The fidelity of SRLE was estimated by using the alpha Cronbach coefficient. ...
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This study examined the relations between daily hassles, depression and life events on mental health among adults in Romania. Data on daily hassles, depression, life changes were collected from a sample of 724 adults by means of a self-administered questionnaire which included the translated Romanian version of the Survey of Recent Life Experiences (SRLE), Beck Depression Inventory (BDI), and the Evaluation Scale of Recent Life Events (ESRLE). With the use of principle axis factoring followed by direct oblimin rotation SRLE factors were extracted. Findings revealed that hassles were related to higher depression and life changes.
... In a survey (Kahneman, Krueger, Schkade, Schwarz, & Stone, 2004), traffic was found to be one of the least enjoyable experiences in a day. It is among the leading causes of stress to those who drive in congested traffic (Hennessy, Wiesenthal, & Kohn, 2000;Stokols, Navaco, Stokols, & Campbell, 1978;Thwe, Yamamoto, Sato, & Morikawa, 2017). During rush hours, drivers in cities with severe traffic congestion, such as Moscow, Mumbai, Manila, Bangkok, and New York, tend to suffer acute stress (Hennessy & Wiesenthal, 1999). ...
... Drivers show significantly more stress than non-drivers do (Venkatesh & Pushpa, 2014). Moreover, traffic contributes at different degrees to stress, depending on mood states (von Helversen & Rieskamp, 2020), time urgency and hassle exposure (Hennessy et al., 2000), predictability (Evans, Wener, & Phillips, 2002;Wener & Evans, 2011), duration in traffic and individual trait stress susceptibility to congestion (Higgins, Sweet, & Kanaroglou, 2018), road conditions (Thwe et al., 2017), controls and choices (Schaeffer, Street, Singer & Baum, 1988), and days of the week (Gulian, Debney, Glendon, Davies, & Matthews, 1989). ...
Stress influences decision making. Stressed investors may trade in concert and drive stock market returns in a certain direction. This study examines the effect of Bangkok traffic-induced stress on Thai stock market returns. The average Longdo traffic index during morning rush hours proxies for the level of stress. Because Bangkok traffic affects only local investors, this study measures the return by the return on the Market for Alternative Investment (mai) index. Local investors have an average of 96.96% share of the mai stocks’ trading volume. The daily sample began on January 4, 2012, and ended on April 2, 2020. A test based on the artificial Hausman regression indicates that error-in-variable and omitted-variable problems are present in the estimation. Therefore, this study chooses the generalized method of moments (GMM) regression—an instrumental variable (IV) regression, together with Racicot and Théoret’s (2010) two-step IVs over the traditional ordinary least squares regression. The IVs are informative and valid, with informativeness and validity R2 values of 0.9888 and 0.0000, respectively. The slope coefficient of stock returns on the traffic index is negative and significant. Traffic-induced stress can drive stock market returns. The net selling by local institutional investors explains the significant traffic-induced stress effect in the stock market.
... Studies have confirmed that this irritation and frustration from other drivers can lead to driver aggression [46,47]. Another crucial consideration in interpreting the impact of traffic congestion is the confounding issue of time urgency, which can exaggerate the negative emotions caused by traffic congestion [48]. During an experimental study, Shiner and Campton [39] found a strong linear relationship between traffic congestion and aggressive behavior. ...
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Aggressive driving has emerged as one of the most studied behaviors in the traffic safety field, due to its association with the odds of motor vehicle crashes and especially fatal crashes. Previous research has investigated the situations which provoke anger while driving, as well as the emotional (anger) and behavioral (aggression) aspects of aggressive driving. However, surprisingly the cognitive aspects of aggressive driving have largely been neglected. This study investigated the psychometric properties of the short-forms of the Driver's Angry Thoughts Questionnaire (DATQ) and the Driving Anger Expression Inventory (DAX) in a sample of professional drivers. Furthermore, the study aimed to investigate the mediation effects of aggressive thoughts, as the cognitive aspect of aggressive driving, on the relationship between traffic congestion and driving aggression. To this end, 613 public transport bus drivers completed the DATQ and DAX and were also asked to report the level of traffic congestion they normally faced in their daily driving, using six pictures. Confirmatory factor analysis (CFA) supported the four factor DAX and the five factor DATQ, which largely replicated the original factors. The four forms of maladaptive thoughts on the road were positively associated with aggressive driving, while the positive factor (coping self-instruction) was negatively associated with aggressive driving and traffic violations. Moreover, the results indicated that traffic congestion does not contribute directly to anger expression on the road, but rather through aggressive thoughts. This study suggests that cognitive interventions may help to eliminate aggressive driving and its adverse outcomes on traffic safety.
... More specifically, major events or the cumulative effect of daily hassles have been linked with detrimental road safety outcomes (Rowden et al. 2011) such as family issues (Lagarde et al. 2004), financial difficulties (Norris et al. 2000), occupational stress , resulting in adverse situations at the wheel (Scott-Parker et al. 2018). Furthermore, increased exposure to acute life stressors may predict stress-related outcomes of drivers when exposed to certain traffic conditions, such as traffic jams and challenging road conditions, thus increasing their likelihood of involvement in traffic crashes (Hennessy et al. 2000). The few studies in this field confirm the need for research addressing the real influence of stressful events in the driving task, and, consequently, in road safety. ...
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Almost all the recent studies addressing road safety from the approach of human factors agree that stress is one of the most considerable (but underestimated) threats for safe driving. However, evidence on the relationship between stressful life events and driver performance remains scarce. Therefore, this study aimed to assess life stress-related perceptions of Spanish drivers, as well as exploring their relationships with self-reported driving performance, decision-making and other road safety-related issues. Methods: This cross-sectional research analysed the information gathered from a nationwide sample of n=840 Spanish drivers responding to an electronic survey on psychosocial issues, stress-related factors and driving issues. The results show that a high percentage (75%) consider that stress may impair their driving performance, while 76.9% of drivers report having experienced at least one major stressful life event during the last year. Despite this relatively high awareness of the negative role of stress and its associated factors for driving safety, this study found that drivers tend to ‘ignore the alarm signals’, as they often reported keeping driving, even when noticing their driving fitness and performance might be impaired by stress-related factors. This study highlights the need to create and apply interventions aimed at informing and training drivers to identify, manage and cope with stress from different spheres, including stressful life events, as a means of potentially improving their driving safety habits and outcomes.
... Even if traffic congestion did not have a direct impact on the frequency of road accidents, previous works show that stress levels from drivers would be higher when driving in highly congested traffic conditions [Hennessy and Wiesenthal, 1997;Wener and Evans, 2011] and their satisfaction levels would be worse due to an increase in the travelling time [Higgins et al., 2018]. Furthermore, drivers' stress levels are influenced not only by aspects related to the driving context -such as traffic congestion-but by a myriad of situational and personal factors [Hennessy et al., 2000] that seem to be enhanced in cities. Several studies [Trivedi et al., 2008;Srivastava, 2009;Rishi and Khuntia, 2012] [Taylor and Dorn, 2006;Lagarde et al., 2004;Simon and Corbett, 1996]. ...
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The world is undergoing a rapid urbanisation process such that the ma- jority of people now live in urban areas. In this context, it is crucial to understand the behaviour that emerges in cities as a result of complex in- teractions between environmental, social, economic and political factors. To improve our knowledge, different techniques are used in this thesis in order to quantitatively model how one city compares with another. Owing to the present-day ease of access to information, most of the results in the following pages have been obtained via assessment of real-world data, made available by different public organisations. Urban scaling is used as the main modelling framework. This approach concerns the relationship between the population size of an urban area and some other urban characteristic. The work is applied to two specific topics of interest. Firstly, the amount of coverage given by the media to Mexican urban areas, before and after the 2017 Puebla earthquake, which affected several regions in Mexico. Secondly, the number of road traffic accidents per person in urban areas from several European countries for different degrees of accident severity or different definitions for the urban areas. The thesis also contains methodological contributions regarding the problem of accounting for urban areas with extremely large population in urban scaling models. Finally, this work explores the impact of the findings presented here to support the creation of new policies involving urban areas.
The urban population are increasingly suffering from rising transport costs, worsening air quality, longer commuting time, and traffic congestion. Although much scholarly attention has focused on modeling urban traffic congestion, news contents about traffic jam have rarely been examined systematically. This study selects 12 large metropolitan cities across Asia (Beijing, Bengaluru, Hong Kong, Jakarta, Kuala Lumpur, Manila, and Singapore), Oceania (Auckland and Sydney), Europe (London) and North America (Los Angeles and Toronto) for an in-depth content analysis. More than 40,000 pieces of congestion-related articles in the 2009–2018 period have been identified in the local news media of these cities. We apply techniques of text analytics to analyze underlying themes in relation to sustainable transport and congestion-mitigation measures. Also, a sentiment analysis is conducted to examine the level of frustration expressed. Results show that traffic congestion is no longer perceived to be primarily an economic issue. Concerns over the environmental impacts of traffic congestion were increasingly discussed. Based on the content analysis, cities in Asia mentioned a lot about congestion-related PM2.5 pollution and climate change was a recurrent theme among non-Asian cities. Economic cost related to traffic congestion has received much more attention in high-income cities. With regard to congestion mitigation measures, terms related to promoting public and active transport was the most popular, followed by restriction and regulation measures, and then intelligent transport system (ITS) or smart mobility adoption. It is noteworthy that road capacity expansion has attracted the lowest coverage. Generally, high-density cities discussed more about public and active transport in alleviating traffic jams; and police enforcement was seen as important in tackling traffic congestion across all cities. In relation to the sentiment, there is a positive association between the overall traffic congestion level and the congestion frustration level expressed in local news.
Chadli, HajarBikrat, YoussefChadli, SaraSaber, MohammedFakir, AmineTahani, Abdelwahed Currently, green energy is knowing a massive growth in the world with the growth of newer energy sources such as wind energy, hydro energy, tidal energy geothermal energy, biomass energy and of Corse the Solar energy which is considered the second biggest source of electricity worldwide including morocco. The production of electricity via these centrals requires optimization at the different conversion levels. To obtain electricity that meets the standards of the electrical grid (sine wave of frequency 50 Hz), the inverter remains the first element to design and build. The structures based on multi-level inverters have brought an undeniable advantage to alternative continuous conversion, especially in high power applications. In this article a new 7-level inverter architecture with only six switches is presented and compared along with the other seven level inverter topologies. To improve the performance of our proposed multilevel inverter, we used a digital sinusoidal Pulse Width Modulation (SPWM) strategy using the Arduino wich leads to further reduction of THD. In this paper, the inverter was tested using Proteus software and Matlab Simulink simulator for harmonic analysis. Then real-time implementation of inverter was tested for a resistive load.
This paper describes a comparative study between two advanced nonlinear controls strategies; the Sliding Mode Control (SMC) and the Fractional-Order Sliding Mode Control (FOSMC), in terms of both reactive and active powers to improve the quality of the energy injected into the distribution grid by the wind energy conversion system (WECS). This later is based on the doubly-fed induction generator (DFIG). The objective is to perform modeling and direct control of the (WECS). Firstly, the dynamic modeling of the different parts of the WECS is performed. Then, the second part of this work concentrates on the proposed nonlinear control laws that rely on FOSMC and SMC. Finally, the performance of those strategies has been simulated in the MATLAB/SIMULINK environment using two wind profiles. One of them is a real wind profile of Asilah-Morroco city to test the system robustness and dynamics as opposed to real conditions.
For several decades, urban congestion causes various problems such us pollution, road wares, and congestion in intersections which deteriorates the quality of life of citizens who live in big cities. Different methods proposed to reduce urban congestion, notably traffic regulation that attend tremendous attention recently. In past years, the usage of tools from artificial intelligence, particularly distributed methods and multi-agent systems, which allow to design new methods for traffic regulation. In this context, a Multi-Agent approach for intersection management system based on the principle of trajectory reservation has been proposed to reduce the travel time average and air pollution.
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A new scale, the Driving Behaviour Inventory (DBI) was developed to study dimensions of driver stress. The DBI was administered to two independent samples of drivers who commuted daily to work and/or for whom driving was part of their job. In both studies driver stress was defined by five factors which accounted for over 40% of the variance. These were identified as: driving aggression, dislike of driving, tension and frustration connected with successful or unsuccessful overtaking, irritation when overtaken and heightened alertness and concentration. Multiple regression analyses pointed toward variables extraneous to driving as predictors of driver stress, among which life stresses appear to play a predominant role.
Comments on the argument between R. S. Lazarus et al (see record 1986-10770-001) and B. P. Dohrenwend and P. E. Shrout (see record 1986-10765-001) about confounding problems in studies examining the relationship between stress and psychopathology/symptomatology. It is suggested that neither addressed the major problem: the ability to separate statistically and conceptually the relative contributions of objective events and subjective appraisal of those events. Findings of the present author and colleagues (1981; also see record 1986-04136-001) are discussed to illustrate the point. (8 ref)
Students, colleagues, and lay people have often asked me: “What is psychosomatic medicine? What does the word ‘psychosomatic’ really mean?” To try and answer these questions with reasonable clarity I have reviewed the literature and given the matter a good deal of thought. The literature, however, reveals a lack of consensus with regard to the meaning of these terms, and it actually addresses the issue infrequently. Journals and societies calling themselves “psychosomatic” exist in various countries, and are presumably based on the assumption that their professed field of interest is a distinct and clearly delimited one. Discussions with concerned colleagues reveal, however, that ambiguity and controversy persist, and that some individuals would gladly bury the word “psychosomatic” altogether, replacing it with some other, hopefully less ambiguous term, such as “biopsychosocial,” for example. Yet, as a historian of psychosomatic medicine shrewdly observed years ago, even though the word “psychosomatic” is unsatisfactory, it is “so deeply entrenched in the literature that it will never be eradicated.”1 p 402 Indeed, so far it has resisted all attempts to eliminate it, as indicated by the fact that both this journal and the Society of which it is an organ continue to be called “psychosomatic.” This being so, another attempt to trace the roots of and to define the terms in question is called for, so as to provide a basis for a wider discussion.
Driver stress and related issues influence a number of people and situational interactions. Indeed, in a review of the literature on driving stress, Gulian (1987) found a substantial body of research describing physiological modifications associated with various traffic incidents (e.g., increase in heart rate). Yet few investigations have addressed driver stress from a psychological viewpoint (but see: Stokols, Novaco, Stokols and Campbell, 1978; Hoyos and Kastner, 1987). There is apparently little information regarding coping strategies used by people to deal with driving-induced stress.
Technical Report
You can consciously modify your driving personality by controlling what you look at in traffic, what you believe about other motorists, and the new driving habits you practice. Self-witnessing efforts reveal to you what you look at, what you focus on, what kind of thought-habits you have acquired, what quality of emotions surround you in traffic. The emotion is the result of your habits of thought and feeling as a driver. You are helpless in changing your emotions by an act of will or resolution. But you can use systematic self-modification techniques to suppress the habits of thought and feeling you observe in yourself. You can substitute for them new and healthier mental patterns and thereby permanently improve the quality your traffic life. Why Did You Do That? How do you control what you believe about other drivers? One technique is to examine your driving attributions. Consider, for instance, a slow moving car in your lane. Why is the driver going so slow? You can attribute the cause to several elements: (1) the driver's disposition. You might think that the person is inconsiderate, incompetent, stupid, dumb. (2) the driver's appearance, such as race, gender, age, or ethnic background. (3) the traffic situation. You might think that the car is old or malfunctioning, or perhaps there is a child in the car, or someone is sick. The first two causes are called "dispositional attributions" while the third is known as "situational attributions." Social psychologists have found under experimental conditions, that when people make a dispositional attribution , they react with negative emotions. On the other hand, when people make a situational attribution, they feel more tolerant or even positive. Full text available online:
Detection and control of harmful environments is a classical public health strategy. This strategy can be applied to the social as well as to the biological and physical environments. This article seeks to highlight some of the relationships between the health of individuals and the social environments in which they exist. It is indicated that in many instances, it may be desirable to change the public health paradigm from the bio-individual to the social level because some of the most powerful forces that affect change in disease patterns and in the health of populations can often operate at this level. Although the discussions in this article are mainly rooted in the Indian social and cultural context, the conclusions drawn here should also be applicable in a more general sense to many other parts of the world both in the lesser industrialized as well as industrialized nations.