Transportation Research Part F 8 (2005) 429–439
www.elsevier.com/locate/trf
The tendency of drivers to pass other vehicles
Hillel Bar-Gera *, David Shinar
Department of Industrial Engineering and Management, Ben-Gurion University, P.O. Box 653, Beer-Sheva 84105, Israel
Received 30 December 2004; received in revised form 27 May 2005; accepted 28 June 2005
Abstract
The purpose of this study was to assess the speed differential threshold—if there is one—at which drivers
decide to pass a lead vehicle. Drivers in a simulator encountered vehicles in front that were programmed to
travel at speeds that were similar, slightly below, or even slightly above the drivers own speed. The study
involved a total of 152 such passing opportunities. In almost all of the encounters with slower vehicles
(traveling at speeds slower than 3 km/h of the driver) they passed them, and in two thirds of the encounters
when the lead vehicles were moving at their speed they passed them too. Most surprising was that in 50% of
the encounters drivers passed the lead vehicle when it was traveling faster than their average speed. In these
situations drivers actually increased their own speed substantially to accomplish the passing maneuver,
despite the fact that not passing the lead vehicle would not have caused any delays. The tendency to pass
appears to be related to the drivers own speed variability: the more variable the drivers speed the more
likely he or she was to pass the vehicle ahead even when its speed was greater than their average speed.
The results are interpreted in terms of (a) driver aggression, and (b) association of car following with added
effort, attention overload, or risk. The latter explanation implies that the tendency to pass vehicles may be
reduced with the introduction of in-vehicle technologies such as adaptive cruise control.
2005 Elsevier Ltd. All rights reserved.
Keywords: Driving behavior; Passing; Road safety
*
Corresponding author. Tel.: +972 8 6461398; fax: +972 8 6472958.
E-mail address: [email protected] (H. Bar-Gera).
1369-8478/$ - see front matter 2005 Elsevier Ltd. All rights reserved.
doi:10.1016/j.trf.2005.06.001
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1. Introduction
Passing is a common maneuver in traffic, with significant impacts on safety as well as on system
performance. Passing a vehicle on two lane roads through a lane that is designated for opposing
traffic is associated with an increased risk of head-on collisions. However, in preparation for passing drivers often reduce their headways to a point where they are no longer maintaining a safe
distance from the car ahead (Rajalin, Hassel, & Summala, 1997), thus creating an increased risk
of rear-end accidents as well. Passing a vehicle on a divided highway through a lane that is designated for traffic in the same direction is not as risky, but it still creates potential conflicts between vehicles that may lead to accidents.
While in some cases passing is essential to the proper functioning of the roadway system, in
other cases it may be unnecessary and undesirable. In these latter cases it may be possible to influence drivers behavior in a way that will reduce the frequencies of passing maneuvers, and thus
improve traffic safety.
An intuitively appealing explanation for passing is that roadways accommodate vehicles that
travel at different speeds; either as a result of technical reasons such as vehicle weight, engine
capacity, slope, etc., or as a result of drivers preferences. The basic motivation of drivers to pass
other vehicles is therefore to avoid loss of time that results from traveling at a lower speed forced
by a slower moving vehicle ahead. This motivation is considered rational and legitimate, in the
sense that the primary motivation for driving at a certain speed is to minimize the travel time
while maintaining a constant risk level (Wilde, 1994). This motivation suggests a decision model
in which speed is the primal decision while passing decisions are derived from it.
However, casual observations suggest that in real traffic passing may occur even if it is not justified by a speed difference between vehicles. Indeed, it is not too uncommon to observe vehicle A
overtaking vehicle B shortly after vehicle B overtook vehicle A. Naatanen and Summala (1976,
p. 42) suggest that in addition to the principal motives of speed and safety, drivers are influenced
by less official and less accepted extra motives, such as aggressiveness, sensation seeking, and
competitiveness. Indeed, several studies have shown that extra motives influence passing decisions
of specific driver groups more than others. Matthews et al. (1998) demonstrated in a driving simulator that frequencies of passing in general and risky passing in particular are higher for younger
drivers, as well as for drivers that are categorized as relatively aggressive according to their selfreports. Zhang, Fraser, Lindsay, Clarke, and Mao (1998) provided additional evidence for higher
frequency of passing among younger drivers. Passing has also been associated with aggressive
driving since it is an instrumental aggressive behavior that is intended to overcome the frustration
of driving slowly behind another vehicle (Shinar, 1998; Shinar & Compton, 2004). Particularly
frustrating is the situation of driving in congestion, where drivers feel modestly victorious when
they overtake another vehicle, but extremely defeated when they are being overtaken (Redelmeier
& Tibshirani, 2000). Gidron, Gal, and Desevilya (2003) suggested that one way to identify potentially aggressive road hostile drivers is by consideration of their self-reported frustration when
driving behind a slower vehicle.
Extra motives like driver aggression and sensation seeking are important explanations for frequent passing of slower vehicles as a byproduct of excessive speed. For the most part, these behaviors can be explained by a decision model in which speed is the primal criterion and passing
decisions are derived from it. In contrast, a decision to pass when it is not necessary in order
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431
to maintain the drivers own chosen speed is considered as a tendency to pass. There are several
possible explanations for such behavior; some of them are typical less-accepted extra motives, like
competitiveness, while others may be acceptable and rational. For example, a cognitive approach
would suggest that drivers might prefer to pass if maintaining a relatively constant safe headway is
a demanding attentional task. Thus, the tendency to pass may reflect more than just a manifestation of aggressive driving.
In a road without entrances or exits, where all vehicles maintain perfectly constant speeds and
never pullover to the shoulders, the only possibility for a vehicle to get closer to a lead vehicle is if
the following vehicle is faster than the lead vehicle. Real life traffic, unfortunately, is much more
complicated. As a result there are many cases where a driver may observe a vehicle upfront, which
is only momentarily slower, and thus may be followed without any need to slow down. The simplest example is perhaps when two vehicles drive at the same speed of 100 km/h, at a constant
distance of 300 m; then, the lead vehicle slows down to 90 km/h for about a minute, and returns
to his original speed of 100 km/h. Drivers may therefore encounter opportunities to pass other
vehicles whose average speed is either slower or faster than their own average speed. Our question
is whether these drivers will decide to pass, or not.
It is fairly obvious that—conditions permitting—most drivers will pass a vehicle traveling at a
substantially lower speed than them, say by 15 km/h or more. The interesting question is: what is
the speed difference threshold at which drivers will decide to pass, and will drivers exhibit a tendency to pass even when the lead vehicle travels at the same speed or even slightly faster?
The specific purpose of this study was to test whether such tendency to pass other vehicles exists, and if so, how to characterize it. Our study is based on experiments in a driving simulator,
where drivers encountered vehicles traveling in the same direction at speeds that were either
slightly below, the same, or slightly above theirs.
2. Method
2.1. Subjects
The drivers were 19 students (8 females; 11 males), 22–29 years old, all with Snellen acuity of
6/9 or better, and all with at least four years of driving experience.
2.2. Procedure
Each driver participated in one session lasting less than one hour. Each session was divided into
two parts: driving in the simulator and filling out questionnaires. The simulated roadway consisted of a 40 km long 4-lane divided highway with the geometry of a rural roadway close to
the University. The road was relatively flat with a few curves. The posted speed limit was
90 km/h, and the typical drive lasted less than 30 min. Along the drive the driver encountered
a few vehicles traveling in one of the opposite lanes. Occasionally a vehicle traveling much faster,
and in the left lane, would overtake the subjects car.
The main challenge in the design of this experiment was to create opportunities to pass vehicles
that travel at a speed that is similar to the drivers preferred cruising speed in a reasonably natural
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and effective way. This challenge is enhanced by the fact that different drivers may prefer different
cruising speeds. To observe drivers true behavior, we let drivers choose their own speed without
any specific guidance from the experimenters. The speed of simulated vehicles was then set relative
to the chosen speed of the driver.
The key independent variable was the designed speed differences. Four designed speed differences of 6.4, 3.2, 0, and 3.2 km/h (i.e., 4, 2, 0, and 2 mph) were evaluated; where a negative
difference means that the target speed of the simulated vehicle was lower than the drivers speed,
and a positive difference means that the target speed of the simulated vehicle was higher than the
drivers speed. For each designed speed difference, we tested the drivers response to two different
types of lead vehicles: a passenger car and a truck. In total, each driver encountered eight opportunities to pass simulated vehicles (2 vehicle types · 4 speed differences). The order of appearance
of the different passing opportunities was randomly chosen for each driver.
So that the simulated lead vehicle will not pop up suddenly in front of the vehicle, it first appeared at a distance of 180 m. To ensure an interaction between the vehicles in a reasonable
amount of time, the initial speed of the lead vehicle was 16 km/h (10 mph) lower than that of
the subjects chosen speed at the time of appearance of the simulated vehicle. The target speed
of the simulated vehicle was determined according to the designed speed difference and the drivers
speed at the moment that the time-to-collision between the two vehicles decreased to 27 s. At the
initial speed difference of 16 km/h this was expected to happen at a distance of about 120 m. The
transition from the initial speed to the target speed was gradual over 20, 23, 28 and 35 s for the
four different designed speed differences, respectively. These times were computed so that the simulated vehicle would reach its target speed when the gap was approximately 60 m, assuming that
the drivers speed remains stable. This gap was chosen on the basis of several pilot runs that indicated that drivers hardly react to a lead vehicle at distances greater than 60 m. It should be
pointed out that when the design is for the simulated vehicle to have the same or higher speed
as the driver, the scheme described above allows drivers, if they want to, to maintain their speed
constant, and the resulting gap will remain safe at all times.
2.3. Equipment
Data for the study were collected from the simulator runs and from questionnaires completed
by the drivers. The simulator is STISIM (by Systems Technology Inc.) integrated into the cab of a
fixed-based 1995 Rover passenger car with its original brake, accelerator and steering systems, a
sound system, and a projector (XGA Hitachi CP-X958) that provided the driver with a scene projected on a screen in front of the car. The image of the roadway and the simulated vehicles was
projected on a 3 · 3 m screen, located 3 m in front of the driver, covering a field of view of 40,
with a 1:1 magnification ratio.
Speed impressions in our set-up were tested in an empirical validation study that compared
speed perception and speed production in a passenger vehicle on a real road with speed perception and speed production in the simulator. The simulated drive had a road with the same
geometry and similar roadside features as the real road. Across a range of speeds of 40–
100 km/h, the squared correlations between the real speed and the perceived speed were
0.997 on the road and 0.979 for speed estimation and 0.861 for speed production in the
simulator.
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Three questionnaires were used to get the subjects subjective impressions: driving habits (given
before the drive), Swedish Occupational Fatigue Inventory—SOFI (given before and after the
drive (Aahsberg, 1998), and Zuckermans (1979) sensation seeking questionnaire (given after
the drive). The driving habits questionnaire asked drivers to rank themselves relative to other
drivers in terms of safety, speed, passing, keeping to the right lane, keeping safe distance and rules
observance in general. The instructions to the drivers were to drive as they would normally, under
no particular stress or time constraint, according to their driving habits as they reported them in
the driving habits questionnaire that they completed. These instructions intend to indirectly
ensure that drivers understand that they may pass and choose their speed as they would on a real
road, without making the purpose of the study too obviously clear. After filling out the questionnaires and hearing the instructions each driver was given 5 min to get used to the simulator before
the actual experiment began.
3. Results and discussion
The basic analysis, presented in Fig. 1, shows for each designed speed difference the distribution
of driver actions in the different opportunities to pass. With two types of simulated vehicles, cars
and trucks, for each of the 19 drivers, there were a total of 38 passing opportunities at every designed speed difference. In addition to the overt behavior, of passing or not passing, the experimenters also observed that in quite a few cases drivers wanted to pass, but did not succeed in
doing so, possibly due to speed/accelerator limitations of the simulator. While there is no formal
test to determine the validity of the observations made by the experimenters in this regard, they do
seem relevant to the analysis. Therefore, drivers actions were divided into three categories: did
pass, did not pass but wanted to (according to the experimenters judgment), and did not pass.
Fig. 1. Distribution of driver actions as a function of designed speed difference. (a) Did pass; (b) did not pass, but
wanted to; (c) did not pass.
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The results show that drivers nearly always passed a slower vehicle (92% of the opportunities
when the lead vehicle was slower by a speed difference of 6.4 km/h, and 87% at a speed difference
of 3.2 km/h). This was quite expected, assuming that there is no threshold for a negative speed
difference. More surprising was the finding that in most cases (66%) drivers also passed vehicles
traveling at the same speed, and in half the cases (50%) they also passed vehicles traveling at a
higher speed! If we also consider the drivers decisions to pass (rather than just their overt passing
behavior) then the rates of pass decisions are 100% and 95% for slower lead vehicles, 74% for
lead vehicles traveling at the same speed, and 66% for faster vehicles.
To analyze the data in further detail consider Fig. 2 that illustrates the speed and passing behavior of one driver. The top thick line in this figure shows the drivers speed as a function of time.
The thin line immediately below the thick line represents the speed of the simulated lead vehicle
from the moment it appears until it disappears, either behind or in front of the driver. The bottom—dashed-line represents the gap between the lead vehicle and the drivers vehicle; positive values indicating that the simulated lead vehicle is in front of the driver and negative values
indicating that the driver has passed the vehicle. Since there were eight opportunities for passing,
there are 8 thin lines and 8 dashed-lines, one pair for each simulated vehicle. It can be seen that the
driver presented in Fig. 2 indeed adopted a fairly consistent strategy, according to which he chose
a comfortable speed that was maintained so long as the road ahead was clear. However, as soon
as another vehicle appeared fairly close in front of him, the driver accelerated in order to pass that
vehicle, almost regardless of the speed of the simulated lead vehicle. This behavior is observed for
all 8 interactions with the simulated vehicle, as can be discerned from the eight peak speeds.
A detailed illustration of this pattern of behavior is shown in Fig. 3, that isolates the sixth of the
eight interactions. Simulated vehicle 6 appeared at time 1056 s, and at a speed 75 km/h. The speed
of the subject at that time was 91 km/h. Ten seconds later, when the drivers speed was 92.8 km/h,
the simulated vehicle target speed was set to 89.6 km/h, in order to achieve the design speed difference of 3.2 km/h. The simulated vehicle then accelerated gradually until it reached this target
Fig. 2. Speeds of driver and simulated vehicles, and the distance from the driver to the simulated vehicles as a function
of time. Example of a driver with strong tendency to pass regardless of the speed difference.
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Fig. 3. Speeds of driver and simulated vehicles, and the distance from the driver to the simulated vehicles as a function
of time. Example of a driver with strong tendency to pass regardless of the speed difference, zooming on a single
simulated vehicle.
speed at time 1089 s. At that time the distance between the driver and the simulated vehicle was
72 m. It is hard to tell exactly when the driver made his decision to pass, but at time 1125 he accelerated substantially to 120 km/h and passed the simulated vehicle. As soon as the passing was
over, the driver returned to his normal speed.
Similar figures were constructed and examined for all 19 drivers. As expected not all drivers
behaved in the same way. An example of an extreme pattern that is totally different is presented
in Fig. 4 (for Driver No. 18). This driver did not pass faster vehicles at all, making his decisions on
the basis of the speed differences only, according to the conventional model for rational behavior.
Fig. 4. Speeds of driver and simulated vehicles, and the distance from the driver to the simulated vehicles as a function
of time. Example of a ‘‘perfectly rational’’ driver that passes only slower vehicles.
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Note that both drivers (No. 7 and No. 18) traveled at similar speeds of 90 km/h, and kept them
fairly stable throughout the experiment. Detailed inspection of similar figures for all drivers indicates that the ‘‘tendency to pass’’ like that of Driver No. 7 is more typical than the ‘‘conventional
rationalism’’ of Driver No. 18.
Figs. 2–4 also show the variability in driver speeds. This variability creates two methodological
problems. The first problem is that simulated vehicle target speed was determined relative to the
drivers speed at a specific moment, which does not necessarily represent the drivers hypothetical
preferred cruising speed. Second, as a result of the drivers speed variations the distance at which
the simulated vehicle reaches its target speed may not always be exactly 60 m as originally designed. These two issues suggest refinements to the analysis presented in Fig. 1; refinements that
would consider the drivers actual aggregate behavior rather than the designed parameters only.
In the following we consider the average speed of each driver throughout the experiment as a
representative of his/her desired speed. (The first 3 min are excluded as they are used for acceleration; the last 2 min are also excluded to avoid other potential edge problems.) Most drivers kept
a fairly stable speed throughout the experiment, and thus the average speed seems a fairly reasonable reference point. Average speeds were approximately normally distributed (mean = 96.8 km/
h; standard deviation = 10.7 km/h), and 90% of the drivers had average speeds in the range of 84–
112 km/h. These speeds appear to be fairly reasonable for Israeli roadways of this type. There was
a modest Spearman correlation (q2 = 0.35) between the designed difference and the actual difference between simulated vehicle target speed and the drivers average speed. The latter difference,
considered as the relative simulated vehicle speed, will be used from hereon.
The actual average distance at which the simulated vehicle reaches its target speed was 54 m;
fairly close to the designed value of 60 m. However, the variability around this value was very substantial. In 11 cases the stabilization distance was negative, meaning that passing occurred before
the simulated vehicle reached its target speed. Previous studies show that drivers feel comfortable
with following vehicles at headways of approximately 1 s (Ohta, 1994; Taieb & Shinar, 2001).
Therefore we consider as invalid the 25 encounters where the stabilization distance was less than
25 m, which is equivalent to 1 s of driving at the typical speed of 90 km/h. In addition we consider
as invalid the 9 encounters where the stabilization distance was higher than 100 m, typically
accompanied by speed difference of 20 km/h or more, as it is quite possible that in these encounters the drivers did not notice the simulated vehicles at all. These invalid cases are excluded from
the subsequent analysis, which is based on the remaining 118 encounters for potential passing
behavior.
The results of the refined analysis are presented in Fig. 5, and are very similar to the results presented in Fig. 1. They therefore support the same conclusions: drivers passed practically all simulated vehicles that were slower than the drivers average speed. Drivers passed vehicles that are
faster by up to 3 km/h in 64% of the cases, and vehicles that are faster by 3–6 km/h in 47% of the
cases. Sample sizes for higher speed differences were too small to support meaningful percentages
computations. However, as anecdotal evidence we may point out that in six of the valid encounters with speed differences in the range of 12–18 km/h, two drivers did not pass, but two did pass,
and two attempted to pass.
Our last goal was to examine whether the tendency to pass is correlated with other driver attributes. (Such correlations do not necessarily imply cause and effect relationship, since different
attributes may be indicators of the same underlying attitude.) For this purpose we considered
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Fig. 5. Distribution of driver actions as a function of the difference between the simulated vehicle target speed and the
drivers average speed. (a) Did pass; (b) did not pass, but wanted to; (c) did not pass.
the 59 valid passing opportunities where the simulated vehicle speeds were higher than the drivers
average speed. The primary dependent variable was whether each opportunity ended in passing or
not. We used logistic regression with random intercept (Broström, 2003; Dohoo, Martin, &
Stryhn, 2003 ch. 22) to control for the variation among drivers in our repeated measures data.
We examined first which attributes of the specific passing opportunity should be controlled for.
We found that both relative speed of the simulated vehicle and the stabilization distance were statistically significant (p = 0.05, p = 0.008 respectively), while simulated vehicle type (car vs. truck)
was not statistically significant (p = 0.15). To examine the importance of the variation between
drivers, i.e. the random component of the intercept, we used Akaikes Information Criterion defined as: AIC = 2 Æ ln (likelihood) + 2 Æ #parameters. Where two models are compared, the model that yields the lower AIC is considered better (Akaike, 1974; Burnham & Anderson, 2002). In
our case the introduction of a random component to the intercept reduced the AIC from 73.3 to
70.7, meaning that the individual differences are significant.
In consideration of the correlations among the various drivers attributes, a separate logistic
regression with random intercept model was fitted for each attribute, together with the basic controlling opportunity-specific attributes (relative speed of the simulated vehicle and the stabilization distance). The results showed that higher probability for passing was associated with
higher speed variation (p = 0.0005). When speed variation was considered in the model, excluding
the random effect of the intercept from the model reduced the AIC from 46.3 to 44.3 (in fact it
hardly changed the models likelihood), suggesting that the remaining effect of variations between
drivers is practically negligible. Other drivers attributes were not statistically significant, including
average speed, gender and sensation seeking scores. (This is possibly due to the sample size that is
quite small for analysis of personality questionnaire data.)
A similar procedure was used to analyze drivers ‘‘decisions to pass’’, which include 11 attempts
to pass together with 29 actual passes. In this case, among the attributes that are specific to each
passing opportunity, only the relative speed of the simulated vehicle was marginally statistically
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significant (p = 0.07), while the stabilization distance was not statistically significant (p = 0.27).
Simulated vehicle type (car vs. truck) was not statistically significant in this case either
(p = 0.22). Statistically significant drivers attributes, in conjunction with relative simulated vehicle speed, were speed variation (p = 0.019) and average speed (p = 0.021). Gender and sensation
seeking scores were not statistically significant, possibly due to the sample size.
4. Conclusions and future research
The results of our experiments indicate that there is a strong tendency of drivers to pass vehicles that travel in front of them, even if these vehicles are moving faster than their average
speed. Furthermore, this tendency to pass is related to drivers speed variability. These two findings, together, suggest that drivers seem to have a range of preferred speeds, and they perceive
vehicles that travel at any speed within that range as an interference. This interference and the
need to monitor headways pose an additional mental load, and drivers reduce this attention
load by passing such vehicles. The perceived interference and additional attention requirements
are so significant that in many cases drivers are willing to increase their own speed quite substantially just to avoid the need to follow another vehicle that travel at a similar speed to their
own.
The mental load involved in not passing a lead vehicle is aggravated because following other
vehicles forces drivers to constantly adjust their speeds, thus creating an increased mental load
as well as an increased risk of accidents. And because drivers can reasonably expect other vehicles
to have speed variances of their own, these problems are aggravated even further. It remains for
future studies to evaluate the perceived risk and effort that drivers associate with car following,
and to compare it with the risk and effort of other maneuvers such as passing on an undivided
two-lane highway, or passing on a modestly congested divided four-lane highway.
If individual vehicle speed variances are indeed a major component in the tendency to pass,
then means of reducing these variances may be useful to reduce the frequencies of passing, and
improve roadway safety. Particularly interesting in this respect are technologies such as cruise
control and adaptive cruise control. Both can be much more effective in reducing speed variances
then education and enforcement activities. Adaptive cruise control offers an additional advantage
as it directly reduces the effort involved in—and risks associated with—car following. Testing
whether these technologies influence the tendency to pass remains a subject for future studies.
Finally, the limitations of the results presented here should be recognized. All of the drivers
were relatively young; and the experiments were conducted in a simulator. Future studies should
validate these findings with a more heterogeneous sample of drivers and on actual roads (with
appropriately instrumented vehicles).
Acknowledgements
The authors wish to thank Shai Grissaro and Chen Burger who conducted the experiments and
some of the analyses, and Israel Parmet for statistical advice. The study was partly funded by the
Paul Ivanier Center for Robotics and Production Management.
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