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Driver Behavior Analysis under Simulated Animal Crossing Scene
Anam Ardeshiri *
Doctorate Candidate
Department of Transportation and Urban Infrastructure Studies
Morgan State University
1700 E. Cold Spring Lane, Baltimore, MD 21251, USA
Phone: (443)885-4734
Fax: (443)885-8324
Email: [email protected]
Shawn Ellerbe
Research Assistant
Department of Transportation and Urban Infrastructure Studies
Morgan State University
1700 E. Cold Spring Lane, Baltimore, MD 21251, USA
Phone: (443)885-4734
Fax: (443)885-8324
Email: [email protected]
Mansoureh Jeihani
Associate Professor
Department of Transportation and Urban Infrastructure Studies
Morgan State University
1700 E. Cold Spring Lane, Baltimore, MD 21251, USA
Phone: (443)885-1873
Fax: (443)885-8324
Email: [email protected]
*
Corresponding Author
Word Count: 6,900 (Including 4 Tables and 8 Figures)
Submission Date: August 1, 2013
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ABSTRACT
Animal-vehicle collisions (AVC) have an increasing impact on U.S. roadway safety; this trend
necessitates comprehensive analysis of the variety of factors involved in such hazardous events.
Human factor as a complicated component in this context has yet to be thoroughly studied. This
study utilizes synthetic driving simulator data to better identify heterogeneity of driving behavior
with AVC. More than 100 subjects were recruited to drive on a large-size realistic road network
in four different animal related scenarios. In two scenarios, the animal passing occurred
unexpectedly on a freeway and a highway, with or without a warning system. Using the driving
simulator method, the study results revealed interesting facts regarding animal-driver
interactions. The effect of driver-specific and road-related factors on the risk of AVC was
determined using statistical analysis, such as Pearson’s chi-square and a logistic regression
model. Drivers’ speeding behavior and collision probability were found to be associated with
their socio-economic characteristics.
Keywords: Animal-vehicle interaction, collision risk, driving simulator, driver behavior, speed.
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INTRODUCTION
Animal-vehicle collisions (AVC) have constantly been a safety issue in the field of
transportation. Over the years, several studies have been conducted to identify trends,
characteristics, and hotspots of AVC; consequently to develop methods to alleviate animal
crossings and collision risks. While animal behavior was subject to several researches, less is
studied about drivers’ perception, intention, behavior, and promptness of response in the
presence of animal in the roadway. Drivers as a key factor can play a significant role in AVC
risk mitigation. Lack of real or synthetic data is a major obstacle to performing human behavior
studies in this field. The current research analyzed the behavior of drivers as they were randomly
exposed to a crossing animal using driving simulator technique. Extensive data have been
collected by recruiting human subjects driving a high fidelity simulator. Subjects encountered an
animal crossing the highway at an unexpected point in their driving experiments. The primary
objective of this study is to evaluate the effect of drivers’ characteristics (socio-economic and
speed choice) and road attributes (speed limit and warning signs) on the risk of AVC.
Annually there are 1–2 million wildlife-vehicle collisions (WVC) in the U.S., with the
trend increasing (1). Huijser and Kociolek (2) analyzed historical WVC data to propose
mitigation measures by influencing driver behavior and animal movements. It was concluded
that animal detection systems (ADS), wildlife fencing with or without wildlife crossing
structures, and long tunnels or bridges provided the greatest reduction in WVC (2). ADS can
detect large animals as they approach roadways. Although field studies at several locations in
Switzerland and the U.S. (Utah and Arizona) demonstrated a strong reduction in WVC after
installation of ADS up to an average of 82 and 91 percent (1, 2, 3), the total WVC in a wider area
remained unchanged (2). Sound-based and sight-based impediments were found to be inefficient
tools compared to physical barriers along the roadways to hinder deer access to roads (4).
The animal detection systems can be unreliable due to false negatives and false positives
(3). A false negative is when an animal approaches the system but it fails to pick it up, and a
false positive is when the system displays that there is an animal in the area, but there is not. It is
necessary to eliminate false negatives and false positives in ADS, as the former presents a
hazardous situation for drivers and the latter may cause drivers to ignore activated signs (2, 3). In
general, ADS have the potential to be a reliable and effective mitigation tool, though they are not
a perfect solution for every location and further research and development is required prior to
wide-scale ADS applications.
Sullivan (5) compiled the Fatality Analysis Reporting System (FARS) data from 1990 to
2008 and found that over a 19-year period, AVC have more than doubled in the U.S. Using
logistic regressions and odds ratio of fatal versus nonfatal AVC, the study (5) concluded that
there is a strong association between speed limit and AVC risk in darkness, which might be due
to forward vision restriction of drivers at night driving. A 3-year before/after experiments were
conducted at ten locations to see whether roadside mowing could significantly reduce deervehicle collision rate (6); however, the study’s results neither supported nor contradicted a
significant relationship between mowing and AVC.
Gunson et al. (7) reviewed the current WVC empirical models and summarized the type
and measurement of dominant factors that can increase or decrease WVC. For instance, the
authors stated that WVC were more likely to occur on roadways with high volume of traffic or
with low visibility of drivers. Using general linear model, McShea et al. (8) indicated that
although road-related factors, such as traffic volume and road type, were significant predictors in
deer-vehicle collisions, deer density and deer harvest levels were not correlated with the location
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of collisions. The authors (8) affirmed that changing drivers’ behavior and road factors should be
the main focus of attention.
METHODOLOGY
This study focused on AVC by using driving simulator data as it related to drivers’ behaviors and
characteristics. The UC-win/Road simulator, a product of FORUM8 Co. (9), was utilized to
replicate drivers’ behavior. UC-win/Road is an advanced, computer-based driving simulator
owned by Morgan State University (MSU), and enables researchers to collect details of vehicles’
interaction with the roadway and environment. The simulator’s hardware consists of steering
wheel, acceleration and brake pedals, cockpit, three surrounding monitors, ignition key, safety
seat belt, automatic transmission, push buttons, and flash lights. The UC-win/Road software,
which is a virtual reality system, is capable of creating and editing entire network elements
including road type, road alignments, cross sections, roadside signs, terrain setup, traffic
composition, and traffic generation.
The UC-win/Road simulator fairly addresses driver-vehicle interactions and replicates the
driving feeling and workload by simulating a variety of surroundings, such as the vehicle’s
sound, vehicle cockpit, and side and rear mirrors. It adjusts three-dimensional and FBX scenes
according to distance from viewpoint and matches terrain and sky texture to reality. Visual
option tools support the real-time presentation of traffic flow, weather condition, spatial
environment, and static objects. The visualization of three-dimensional trees, cars, and crossing
animal is smoothly dynamic as the subject vehicle moves along the road. Except for its low-sized
cabin, the UC-win/Road simulator is considered high-fidelity in undertaking real transportation
network and driving skills.
Figure 1 displays a schematic snapshot of the built environment in the UC-win/Road
simulator for the current study network, while an animal is crossing the road. A real network
southwest of the Baltimore metro area (Maryland) was created in the simulator for some
concurrent researches including driver-animal interaction. Human subjects in this study started a
journey from a fixed origin on highway MD-100 to reach Baltimore City. After driving 3.45 mi
(5,550 m) on MD-100, drivers could choose among two viable alternatives with different road
specifications to reach the destination, based on the travel time information they received
through a dynamic message sign (DMS) embedded in the network. Drivers were exposed to
animal scenarios in both routes. The simulator recorded several useful parameters, such as travel
time, travel distance, instant speed, geographic positions, offset from lane center, lane number,
and lane changing.
Four research scenarios were developed in this study to analyze driver-animal
interactions. Table 1 describes the four scenarios incorporated in this study. Along with other
warning, regulatory, and guide signs, drivers observed a typical deer-crossing cautionary sign in
all scenarios; however, only in two scenarios an animal (a cow, due to technical restriction of the
simulator) crossed the road. The animal was triggered to pass with a constant speed of 5 mph (8
km/hr) when the subject vehicle reached a benchmark in both alternatives. In one of the two
animal-crossing scenarios, a flashing light underneath the animal sign warned drivers of the
presence of an animal. In one of the two no-animal-crossing scenarios, an inactive flashing light
was installed. Figure 2 shows the animal sign and flashing light simulated in this study.
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FIGURE 1 Schematic scene of animal crossing in UC-win/Road driving simulator.
TABLE 1 Description of the Four Research Scenarios
Scenario Warning system
Animal crossing
i
ii
iii
iv
Animal sign
Animal sign
Animal sign, inactive flashing light
Animal sign, active flashing light
No
Yes
No
Yes
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FIGURE 2 Animal sign (left) and attached beacon (right) in driving simulator environment.
Drivers’ socio-economic information, such as gender, age group, education level, driving
license type, income level, car ownership, annual driving mileage, and familiarity with the study
area, were collected through survey questionnaire. Research participants were recruited through
flier distribution, where fliers were placed randomly on cars’ windshields and handed out inside
and outside the MSU campus in Baltimore area. Since the subjects were reimbursed for their
time participating in the research, it allowed the research team to recruit subjects randomly. The
study had 102 volunteers with the average drive frequencies of 5.5, while each drive took an
average of 22 minutes. After at least 10 minutes practice, subjects were asked to drive the four
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predefined scenarios, overall up to 12 times based on their availability and suitability. Table 2
presents the descriptive statistics of the socioeconomic variables in the database. Sixty-four
percent of the subjects were male, and the largest age group (36 percent) belonged to young
adults between 18 and 25 years old. The sample had a balanced distribution of education level
and income level. The average annual mileage driven was reported 14,800 miles. For more
information on study design see reference (10).
TABLE 2 Descriptive Statistics of Subjects’ Characteristics
Characteristics
States
Percentages
Gender
Female
36
Male
64
Age
< 18 *
3
18-25
36
26-35
18
36-45
20
46-55
10
> 55
13
Education level
High school or less
31
College degree
32
Post-graduate
37
Household income level
< $20,000
23
$20,000- $30,000
18
$30,000- $50,000
13
$50,000- $75,000
16
$75,000- $100,000
13
> $100,000
17
Annual mileage driven
≤ 8,000
29
8,001 - 15,000
35
15,001 - 30,000
25
≥ 30,000
11
Route familiarity
Unfamiliar
25
Somewhat familiar
43
Familiar
32
*
holding learner’s permit
RESULTS AND DISCUSSION
Overall 560 experiments were conducted in this study by 102 participants, while the drivers
encountered an animal crossing the roadway in 62 percent of the occasions. Controlling for
animal crossing scenarios, drivers collided with the animal in 19 percent of the cases. This
section cross-classifies the collision rate by the driver-specific and environment-specific
attributes. Furthermore, investigation is conducted on the association between drivers’ speed
choice, lane number, and collision risk.
Drivers’ Characteristics
The overall collision rate was computed 19 percent when the drivers were exposed to an animal
crossing scenario (i.e. scenarios ii and iv). Figure 3 shows the AVC rate based on drivers’ age
group and gender. The left graph indicates a significant decrease in AVC risk as the drivers’ age
increases and declares that older drivers used more caution than younger drivers. While AVC has
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a peak of 23 percent among young drivers, it reduces drastically to 10 percent for adults over 45.
The right graph compares female and male drivers and demonstrates a lower collision rate
among female drivers. However, the t-test results indicated that the 5 percent difference in AVC
rate between genders is not statistically significant at α = 0.05.
25%
23%
25%
22%
20%
20%
13%
15%
10%
10%
5%
10%
0%
≤ 25
26-35
36-45
> 45
Male
Female
FIGURE 3 AVC rate based on age groups (left) and gender (right).
Figure 4 evaluates the effect of education and income level on AVC. As shown in the left
graph, there is no significant difference among drivers with various educational degrees and all
three groups are close to 19 percent average. However, there is an unexpected high AVC rate (33
percent) in people within the $20,000–30,000 income range, 12 percent higher than the second
high income group—more than $100,000. The $20,000–30,000 income group built 18 percent of
the sample size and mostly consisted of young drivers (below 35). Figure 5 depicts the effect of
driving experience and route familiarity on AVC risk. Drivers with low and high annual mileage
tended to have a higher AVC risk than the two mid-groups. Familiar drivers had the least
collision rate with 16 percent followed by unfamiliar and somewhat familiar drivers. According
to Figure 5, there is no clear indication that experienced drivers are more cautious than
inexperienced drivers.
35%
35%
30%
30%
25%
20%
18%
18%
20%
25%
20%
15%
15%
33%
19%
18%
21%
19%
14%
10%
10%
21
22
23
16%
15%
5%
0%
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8
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17
18
19
20
21%
High College Post
school degree graduate
< $20K $20K- $30K- $50K- $75K>
$30K $50K $75K $100K $100K
FIGURE 4 AVC rate based on education level (left) and income group (right).
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28%
30%
25%
25%
21%
19%
20%
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12
13
14
15
16
17
18
10%
8,000 15,000
15,000 - > 30,000
30,000
Unfamiliar Somewhat
familiar
Familiar
FIGURE 5 AVC rate based on annual mileage (left) and route familiarity (right).
Road Attributes
Figure 6 illustrates the effect of road type and flashing light sign on AVC. The left graph
compares AVC in the two alternative routes, I-95 and MD-295. The former was a 65 mph speed
limit expressway with 4 lanes at each direction and the latter was a 55 mph speed limit divided
highway with 2 lanes at each direction. AVC was relatively lower in the wider road, probably
due to the broader visibility and higher maneuver chance that allowed drivers to avoid possible
collisions. As explained in Table 1, in two scenarios an animal crossed the road, while in one of
them, a flashing light warned drivers of the presence of animal. Surprisingly, when the beacon
attached to the animal sign was flashing, AVC rate was higher than the no-light option.
However, the t-test result ascertained that the AVC rates were not statistically different between
flashing light and no-light scenarios at α = 0.05. The flashing light found to be ineffective in this
experiment due to its size, design, and performance. Hammond and Wade (11) in a simulation
study found that the prototype signage with inactive beacon light was not significantly different
from the standard animal sign; however, an active beacon light was effective in speed reduction.
25%
20%
23%
25%
23%
20%
17%
16%
15%
15%
10%
19
20
21
22
23
24
25
26
27
28
29
30
16%
15%
10%
< 8,000
23%
19%
20%
16%
15%
30%
10%
I-95
MD-295
No light
Flashing light
FIGURE 6 AVC rate based on road type (left) and flashing light (right).
Speed Analysis
Instant speeds were measured at two reference points for all the experimental cases to evaluate
the speed fluctuations due to the presence of animal in the road. The first point was located just
before triggering the animal to the road. The second point was located approximately 150 m
further immediately after the animal’s emergence to the road (prior to a possible collision).
Figure 7 displays the variation in average speeds before and after the hypothetical line of animal
crossing. In the two no-animal scenarios (i.e. scenario-i: thick solid blue line and scenario-iii:
thin solid red line), there is a relatively constant speeding pattern, though the average speed was
nearly 6 km/hr lower when there was no light attached to the animal sign. A substantial speed
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reduction occurred (nearly 10 km/hr) as the drivers were exposed to the animal, and this
reduction was more significant when the beacon was flashing. Appropriate statistical test
declared that the 10 km/hr speed reduction due to animal crossing was significant at α = 0.01.
Figure 8 contrasts the speed variation plots between male and female respondents for
both animal and no-animal scenarios. Although female drove distinctly slower than male, the
reaction pattern to animal crossing event is homogeneous among male and female. The earlier
finding of female’s lower AVC rate than male can be associated with their slower speed. In a
similar analysis, no distinction was found among different age groups in their response pattern to
animal crossing.
Average speed (km/hr)
100
95
90
No light, no animal
No light, animal
Light OFF, no animal
Light ON, animal
85
80
75
11
12
13
Before
After
FIGURE 7 Average speeds variation before and after animal crossing for four scenarios.
Average speed (km/hr)
100
95
90
Male; No-animal
Male, Animal
Female; No-animal
Female, Animal
85
80
75
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15
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22
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Before
After
FIGURE 8 Average speeds variation before and after animal crossing based on gender.
Collision Risk Model
To develop an AVC risk model, potential predictors were identified using a bi-variate correlation
analysis. The correlation coefficients of 12 explanatory variables with the dependent variable
were computed and p-values are presented in Table 3 to verify the significant variables. A chisquare test was conducted to measure the correlation of 9 categorical (ordinal or nominal)
variables with collision risk, while a point-biserial test computed the correlation of the 3
quantitative variables with collision risk. Among drivers’ characteristics, age, gender, and partly
income level appeared to be significantly correlated with AVC. Education level, annual driving
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mileage, and network familiarity had no significant association with AVC risk based on the
study data. However, drive frequency, driving lane number, and speeds before and after animal
exposure were strongly significant predictors among other driver behavior factors. The two roadspecific factors, flashing light and highway type were found to have moderate association with
AVC risk.
Since the outcome variable (collision) is binary, a logistic regression mechanism was
utilized to develop an AVC risk model. Table 4 presents the results and goodness-of-fit of the
AVC regression model. Among candidate variables chosen from correlation analysis, only
highway type, speed after animal exposure, and driving lane number had a close relationship
with collision risk. Drive frequency, age, and income level had trivial negative relationships with
AVC probability. The odds of AVC rise nearly 4 percent as the driving speed increases by 1
km/hr. The odds of AVC in highway I-95 (the wider road) are nearly 85 percent less than MD295. Although the effect of unobserved variables might have confounded the coefficients of the
model, the model’s overall goodness-of-fit presented in Table 4 is satisfactory.
TABLE 3 Correlation Coefficient between Independent Variables and Dependent Variable
Point-biserial correlation (Pearson’s R)
Pearson chi-square test
Variable
Variable
value Significance
value Significance
Drive frequency
19.70
0.050
Speed before
0.206
0.000
Flashing light
2.73
0.098
Speed after
0.275
0.000
Highway type
1.95
0.162
Age
– 0.125
0.021
Gender
2.37
0.306
Education level
1.79
0.617
Income level
8.83
0.116
Driving mileage
3.12
0.374
Network familiarity
2.91
0.405
Driving lane
81.08
0.000
TABLE 4 Logistic Regression Model Results
Variable
β
Standard error
Drive frequency
– 0.098
0.066
Highway type
– 1.898
0.516
Speed after
0.036
0.009
Age
– 0.228
0.197
Income level
– 0.097
0.118
Driving lane
1.079
0.277
Constant
– 4.610
0.995
Chi-square = 52.06 (0.000)
– 2 log likelihood = 179.00 Nagelkerke R2 = 0.319
Percentage correct = 78.9 %
Significance
0.138
0.000
0.000
0.247
0.411
0.000
0.000
Exp (β)
0.907
0.150
1.037
0.796
0.907
2.942
0.010
Driving lane was a substantial determinant of AVC in our study. Due to the animal
crossing parameters designed for the DS experiments (animal’s fixed triggering point and its
constant speed), central traveling lanes in I-95 (lanes 2 and 3) and second traveling lane in MD295 (left lane) earned particularly higher rate of collisions among other highway lanes. Given the
lane 4 as the reference group in the model, the odds of AVC in lane 3 is three times higher than
lane 2, which is itself three times higher than lane 1. This shows heterogeneity of the traveling
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lane selection by drivers. Further lane changing analysis indicated that among animal-crossing
scenarios that did not lead to collision, drivers maintained their lanes in 90 percent of the cases;
while in 8 and 2 percent of the cases they shifted to the right and left lanes, respectively, to elude
collision. However, among the 67 cases with AVC, 93 percent had no lane change, and in 1 and
6 percent of the cases drivers switched to the right and left lanes, respectively.
CONCLUSION AND RECOMMENDATION
This study utilized a driving simulator to investigate animal-vehicle interaction and collision risk
as they related to drivers’ behavior and characteristics. Four research scenarios were developed
to evaluate the accidental presence of animal in the highway and to address the effect of beacon
light attached to animal sign. In an extensive data collection, 102 subjects drove the simulator,
overall 560 times. The subjects encountered an animal crossing the roadway in 62 percent of the
experiments and AVC occurred in 19 percent of the animal-present cases. Preliminary analysis
revealed that younger drivers were more likely to be involved in AVC. The males’ average speed
was substantially higher than their female counterparts that resulted in a higher AVC rate.
However, the chi-square test could not reject the homogeneity hypothesis between gender and
AVC. Although drivers had more severe speed reduction when the beacon was flashing, the
AVC rate was higher for the beacon scenario than the no-beacon option. Speed analysis
indicated that for the animal-crossing scenario the average speed decreased by 10 km/hr as
drivers noticed the animal. Pearson’s chi-square and logistic regression model complemented the
statistical analysis of the potential factors on AVC. Drivers’ education level, income level,
network familiarity, and driving mileage appeared to have no clear association with AVC. The
AVC rate was essentially higher on the narrower highway (with 2 lanes each direction) than the
wider (4 lanes each direction). Drive frequency, age, and income level showed less significant
inverse association with AVC.
Certain limitations of the study should be noted. Due to the simulator’s restriction, the
study could not simulate deer crossing as the most familiar large animal in the study region;
subsequently a cow was animated to cross the highway. The animal was triggered to cross the
highway from specified location with fixed speed and direction. Therefore, the collision
probability was strongly associated with the driver’s lateral position and traveling lane selection.
The design of variable speed and acceleration for animal may reveal more facts about driving
behavior. The results of our study suggested that speeding behavior is among the most
significant human factors that affect AVC risk. The animal sign and flashing beacon were found
not be effective in our DS study due to their design and appearance in the DS environment.
Following the solutions presented in AVC literature, a future research direction would be
evaluating the effectiveness of animal warning message on dynamic message sign (DMS) to alert
drivers and reduce AVC risk.
ACKNOWLEDGEMENTS
The authors would like to thank the Maryland State Highway Administration and Morgan State
University’s National Transportation Center for their funding supports throughout the study.
REFERENCES
(1)
Huijser, M. P., P. McGowen, J. Fuller, A. Hardy, A. Kociolek, A. P. Clevenger, D. Smith, and R.
Ament. Wildlife-Vehicle Collision Reduction Study: Report to Congress. Report FHWA-HRT-08034. FHWA, U.S. Department of Transportation, 2008.
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(2)
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TRB Annual Meeting 2014
TRB 2014 Annual Meeting
Paper revised from original submittal
Paper revised from original submittal.