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October 2014 | ergonomics in design
feature
How Much Can a Smart Parking
System Save You?
Discrete event simulation can
show whether benefits are
balanced by costs in saving time
and reducing environmental
impacts.
FEATURE AT GLANCE:
We conducted this study to
investigate the effect of smart
parking systems on parking
search times in large parking
lots. Smart parking systems are
systems that provide real-time
parking spot availability
information to drivers. We
used discrete event simulation
to model a university parking
lot and estimate how much
time could be saved without
physically implementing a
system for experimentation.
We found that smart parking
systems can reduce search
times by an average of 11 s. This
shows potential for a multi-lot
smart parking system that
might save a larger amount of
time and reduce harmful vehicle
emissions.
KEYWORDS:
discrete event simulation,
smart parking system, vehicle
emissions
By Glenn Surpris, Dahai Liu, & Dennis Vincenzi
Finding parking can be a frustrating
experience in any large city. One
characteristic of an advanced
civilization is its ability to transport large
numbers of people in an efficient manner.
As parking facilities grow large, drivers need
advanced methods of parking to reduce
inefficiencies in finding spaces.
Smart parking systems (SPSs) have arisen
in high-density population areas to help
drivers find parking. SPSs employ monitors
at entrances, exits, and individual spaces
to keep track of space usage and direct
drivers to open spaces. Additionally, SPSs
can communicate with drivers with variable
message signs and mobile/Internet interfaces
(Lu, Lin, Zhu, & Shen, 2009; Maccubbin,
2000). In this article, we evaluate the benefits
of SPSs on university parking search times
using discrete event simulation (DES).
The university setting offers an oppor-
tunity for evaluating the benefits of an SPS.
As a push for campus expansions grows
around the country, university administra-
tors face increasing demand for parking
space (Brown-West, 1996). The question
we sought to answer was, “How much time
will an SPS save?” The time spent parking is
important because it can lead to determining
how much fuel and emissions can be saved
by giving the driver more information.
A DES study involves creating a simula-
tion model from real-world statistical data,
ensuring that the model mimics the aspect
of the real-world model being studied,
experimenting with the model, and statisti-
cally testing the outputs of the two models.
We collected data on arrival rates, parking
durations, distances between spaces, depar-
ture rates from areas in the lot, and driving
speeds and used those data to build a DES
model in Arena simulation software. After
we validated the model, we altered the
logic to experiment with the application of
an SPS. Finally, we compared the average
parking search time of the two models to
determine which, if any, was significantly
lower.
A SMARTER WAY TO PARK
An SPS can be defined as any system that
monitors parking occupancy and makes
that information available to drivers who
are searching for spaces. In this section, we
examine state-of-the-art SPSs and explore
parking studies that use DES.
Smart parking technology.
SPSs come in
different forms and vary in cost depending
on the technology used and features
provided. For example, Yan-Zhong, Li-Min,
Hong-Song, Ting-Xin, and Zheng-Jun
(2006) developed a basic SPS that uses
individual sensors for every parking space.
These sensors are connected to a wireless
network to inform drivers of a lot’s capacity
and the location of empty spots. It also
updates the appropriate guiding nodes
located at major intersections. The guiding
nodes do not direct drivers to individual
spots; rather, they guide drivers to parking
areas with unused capacity.
Lu et al. (2009) published a paper on a
sophisticated smart parking scheme called
SPARK (i.e., Smart Parking). The scheme
uses a communication network to send
information to drivers and provide navi-
gation. In the SPARK network, towers
stationed around lots communicate with
devices in vehicles to direct drivers to the
nearest available spaces, inform drivers of
spots available within multiple parking lots,
and alert drivers of the probability of a lot’s
being full by the time they arrive.
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ergonomics in design | October 2014
16
Parking simulation studies using discrete event
simulation.
We chose DES as the experimentation tool
to determine how much time could be saved with an
SPS, because it is an inexpensive solution that is effective
when used with the correct empirical methods. DES helps
researchers analyze systems through the modeling of system
changes at discrete intervals in time. Researchers use DES
to model systems such as manufacturing plants, computer
networks, and parking lots (Kelton, Sadowski, & Swets, 2010).
DES is a viable tool for studying the problems of evaluating
the effect of real-time parking information on parking search
time (Fries, Chowdhury, Dunning, & Gahrooei, 2010; Harris
& Dessouky, 1997; Kelton et al., 2010; Lu et al., 2009).
For example, Lu et al. (2009) performed a simulation study
to evaluate the effect of their SPARK scheme on a parking
facility for a mall. They measured searching time delay (i.e.,
the time that elapsed from when a vehicle entered the lot to
the instant that driver found a parking space). The article
suggested that a decrease in the search time delay was found
when the SPARK logic was used versus the real-world logic.
In another example, Fries et al. (2010) conducted a simu-
lation study to evaluate the effect of real-time information
on the parking activities on a rural campus. The real-time
parking information was distributed to drivers via three
variable-message signs placed along a perimeter road.
Decreases of 15% (591 min) in downtown network delays
were converted to dollars, and cost savings were found.
This study builds on the Lu et al. (2009) and Fries et al.
(2010) studies in that it demonstrates SPS benefits under
a different set of assumptions (e.g., there is no metered
parking at the participating university), adds to the scarce
DES parking literature, and provides a resource on which the
participating university can conduct in-depth assessments of
how SPSs can affect its unique parking environment.
THE PARKING EXPERIMENT
For our study, we chose the Earhart lot at Embry-Riddle
Aeronautical University (ERAU). During fall and spring
semesters, lots at ERAU are noticeably congested, resulting in
long searches for parking spaces. This congestion is especially
evident during the workweek at midday.
ERAU has a total of 21 distinct lots that service more than
5,000 faculty, students, and staff. ERAU lots are divided by
driver type (i.e., faculty/staff, commuter students, and student
residents). The Earhart lot is separated from the main campus
by a four-lane, two-way road. Drivers usually park in Earhart
and cross the road to participate in activities on the main
campus during midday when the main campus lots are full.
The portion of the lot that is under study has 234 regular
parking spaces and 7 handicapped parking spaces. Earhart
has distinctive entry and exit points, along with a well-defined
perimeter, as seen in Figure 1.
Modeling assumptions.
Law (2006) and Kelton et al. (2010)
stated that all DES models would carry a set of assumptions
HEADS UP!
A smart parking system (SPS) of any kind will be
one more piece of information that a driver has to
pay attention to. Given that the modern automobile
is quickly filling with new gadgets, such as rearview
cameras and infotainment systems, it is important
that anything new the driver has to interact with
keeps the driver’s attention focused on the road. The
importance of maintaining the driver’s focus on the
road is supported by theories that explain driving
breakdowns when dual-tasking (e.g., driving and
looking at a screen to find the nearest parking spot)
as interference that occurs when two tasks compete
for the same sensory and motor resources (Sheridan,
2004). If a driver can keep his or her eyes and head
pointed toward the road, there will be less compe-
tition for those resources while the driver finds a
parking space.
Borrowing a page from the domain of avia-
tion, heads-up displays (HUDs) have long been
considered for integration into the automotive
domain (Tufano, 1997). Provided that the HUDs
are configured correctly and do not occlude objects
in the driver’s visual field of view, research suggests
that drivers with HUDs may be better equipped to
handle emergency situations than with a traditional
heads-down display (HDD; Liu & Wen, 2004). More
specifically, HUDs displaying navigational informa-
tion allow drivers to spend more time looking at
the road while feeling less stressed and taxed while
driving (Medenica, Kun, Paek, & Palinko, 2011).
Until HUDs become a standard in every vehicle,
other head-up methods of disseminating parking
information to drivers (e.g., variable-message signs
and inset pavement lighting) should be favored to
heads-down methods (e.g., placing a parking finder
app on a cell phone).
How does the SPS evaluated in this study dissemi-
nate information to users? That is a good question!
The method for relaying parking information to
the driver was not modeled in this study. We were
interested in the time saved after the driver receives
the information; additionally, we reasoned that
the results of the study would not be generalizable
for the university if we modeled any one SPS tech-
nology. This assumption has both its pros and its
cons. For example, different dissemination methods
might take longer to relay information, or a driver
may take time to consider two similar parking
choices, and this time is not accounted for. We think
these study limitations should be noted as the results
are interpreted.
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October 2014 | ergonomics in design
about workings of the system that should not affect the
variable of interest. These assumptions allowed us to ignore
irrelevant variables and simplify the problem. This section
summarizes the assumptions used for the study.
• The parking lot activity was simulated for a period
of 1.5 hr. We assumed parking search time would
be greatest as the parking lot reaches maximum
capacity, which we assumed would occur between
the hours of 10:30 a.m. and 12:00 p.m.
• The spaces are the resources of the parking
system. We categorized them into units of about
24 spaces, or half of an aisle. These 11 units each
had a resource capacity that equals the number of
empty spaces in that unit. They were grouped into
units for modeling ease. We based this modeling
approach on that adopted by Yan-Zhong et al.
(2006). In the simulation, each of the parking spaces
has a distance equal to the midpoint of the unit that
it belongs to. We assumed that for any parking unit,
the time it takes to travel to the farthest spot away
from the entrance would be negated by the time it
takes to travel to the closest spot to the entrance.
The stations and their midpoints (black dots) are
shown in Figure 1.
• We simulated parked car departures from the lot by
adding capacity to the stations.
• Under the SPS logic, drivers proceeded to available
parking via the most direct route.
Model development.
The first part of our experimental
design included defining the descriptions of the systems under
comparison. The models in this section are abstractions of
how the systems operated. For clarification, we refer to the
DES model that represents the real Earhart lot as the base
model. We refer to the DES model that operates under an SPS
as the experimental model.
In the base model, drivers arrived at the lot at different
rates based on the time of day (i.e., half-hourly). After a driver
entered the lot, the search began, and the parking search
time clock started. We stopped the clock when a driver began
parking in a spot. Drivers used a search behavior based on
probabilities to find a space. When a driver found a space, he
or she parked there. The driver remained parked for the entire
simulation.
Given that parking search time was the variable of interest,
we did not model parking durations and departures of cars.
We modeled departures as events that add capacity to each
station. In theory, a departure is another parking space addi-
tion and another opportunity to observe parking search
times.
In the experimental model, drivers entered the parking
lot at the same rate as in the base model. We assumed that
drivers enter a lot only if it has an available spot. When
they entered the lot, drivers were shown the nearest parking
space. This dissemination occurred instantly. The closest
available station with capacity was designated for the driver.
Drivers, following the search behavior of minimizing
walking disutility, chose to park in this spot. The capacity
of the station decreased when a space was designated for
a driver, which eliminated two drivers’ trying to park in
the same spot. As with the base model, drivers who found
parking remained parked, but departures were modeled as
additional capacity.
Data collection.
We collected the data needed for the
computer-simulated model of the parking facility at the
Earhart lot. The data used for the models were collected on
4 days (i.e., Monday through Thursday) from 10:30 a.m. to
12:00 p.m., when, through observations, Earhart appeared
most full. This period was the maximum amount of time/
conditions for which we could collect data because of outside
constraints. All the model inputs and their units are shown in
Table 1.
We entered the arrival and departure rates into Arena in a
schedule, since we determined that the observed fluctuations
in the rates warranted a nonstationary arrival rate from the
data collected, as clearly seen from Figures 2 and 3.
Table 2 illustrates the parking space at the initialization of
the model. The average number across the 4-day period was
used as the initial number of parking capacity for the model.
We analyzed the distribution of driving speeds with a
goodness-of-fit test. Goodness-of-fit tests allow for the testing
of the null hypothesis of the data points fitting a particular
distribution (Kelton et al., 2010). Based on the chi-square test
for the best fit, a triangular distribution TRIA (8, 14.2, 27) was
used to describe the driving speed within the parking lot (with
p = .264).
Figure 1. Blueprint with outlines of stations and circles representing
station centers. Adapted from Cohen (1993).
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RESULTS
Validation.
Before we constructed the experimental model,
we validated the base model by comparing its parking search
times with those of the real Earhart parking lot for 4 days
between 10:30 a.m. and 12:00 p.m. The search times for the
real lot and base model are plotted in Figure 4.
On the basis of these results, we inferred that the base
model was a valid representation of the real lot in terms of
parking search time (t = 1.048, p = .298).
Experimental results.
After we developed and validated the
experimental model, we compared its 10-day parking search
time mean with the 10-day search time mean of the base
model. The samples were recorded from a simulation that was
run for 10 simulation days at 1.5 hr a day. The parking search
time is also displayed graphically in Figure 5.
We were able to show that drivers within the experimental
model have significantly lower parking search times
Table 1. Data Collection for Modeling and Validation
Input Name Measuring
Units
Data Format
Number of cars in
each station at start
Cars Averaged over the
observation period
Arrival rates at
entrance
Number of
cars per 30
min
Nonstationary
schedulea
Driving speed Feet per
second
Triangular
distribution (8,
14.2, 27)
Number of spaces Spaces 234
Distance between
parking stations
and entrance
Feet Range: 96 to 503
Departure rates from
each station
Cars per 30
min
Nonstationary
schedulea
Parking search times
for validation
Seconds Averaged over the
observation period
aBased on observation.
Figure 2. The arrival rates for each time window by day.
Figure 3. The departure rates for each time window by day.
Table 2. Resource Capacity at 10:30 a.m.
Station
Day
AverageMonday Tuesday Wednesday Thursday
1 0 0 0 0 0
2 0 0 0 0 0
3 1 0 0 0 1
4 0 0 1 0 1
5 1 0 0 1 1
6 5 2 0 9 4
7 17 11 3 18 13
8 1 0 0 2 1
9 1 1 0 12 4
10 14 16 7 17 14
11 11 9 4 12 9
Figure 4. Parking search times for the real lot and base
model.
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October 2014 | ergonomics in design
(M = 20.21, SD = 9.06) than drivers of the base model (M =
31.29, SD = 19.7); t = 12.709, p < .001). From these results, we
see that in a lot with more than 200 spaces, an SPS can signifi-
cantly decrease parking search time by an average of 11 s
across a 10-day span.
The base parking search strategy is based on observed
movements in the Earhart lot without an SPS. In the experi-
mental model, however, the search strategy is based on
checking the available capacity of parking spaces from the
shortest to longest distance from the entrance. The quick
dispersion of this knowledge is likely the key factor in
reducing the parking search times of drivers in the experi-
mental model. Because no time is wasted investigating
stations that cannot be seen from the entrance, drivers experi-
ence a lower parking search time with an SPS. These findings
support the conclusions (i.e., SPSs reduce search time) drawn
from the Caicedo, Robuste, and Lopez-Pita (2006) study.
However, the cost savings seem low, and the question arises:
“Is it worth it?”
ECONOMIC IMPACT
Before adopting an SPS at ERAU, we first examined the
cost of such a system. The costs for an SPS to govern the
Earhart lot can be estimated from an informal costing method
proposed by the authors of a field study analyzing the costs
of an SPS for Bay Area Rapid Transit (U.S. Department of
Transportation, 2008). The California Partners for Advanced
Transit and Highways researchers estimated that a parking
system of that nature would have a capital cost ranging from
$150 to $250 per space, and the annual maintenance and
operations cost would range from $50 to $60 per space (U.S.
Department of Transportation, 2007). Using the lowest esti-
mates, an SPS of this type for Earhart would have a capital
cost of $35,100 and $11,700 in yearly operations and mainte-
nance costs, respectively.
Next, we examined how the SPS might affect the environ-
ment. The U.S. Environmental Protection Agency (2011)
estimated that 423 g (0.93 lbs) of tailpipe carbon dioxide
(CO2) is emitted by the average car after driving 1.6 km (1
mile). We know from our data collection that drivers most
frequently drove at a rate of 4.83 m/s (0.003 miles per second)
or 17.38 km (10.8 miles) per hour. Using this number, we
estimated that an 11-s time saving equates to drivers traveling
48.28 m (0.03 miles) during a 2-week period. During our
simulation, 633 drivers were generated, which equates to a
collective savings of 30.56 km (18.99 miles) traveled during
the 2-week simulation period. In a typical 19-week semester,
that is a savings of 290.33 km (180.41 miles).
We conclude that if an SPS were installed, the lot under
study would save 64.3 kg (141.76 lbs) in generated CO2 emis-
sions. The CO2 saved by the SPS would be enough to fill about
36 m3 (9,500 gallons). A word of caution: Although that may
sound like a lot, consider how the CO2 generated by those cars
will be measured in tons (the average car generates 5.1 tons of
CO2 in a year), whereas the savings from this SPS for this lot
alone will be measured in kilograms.
Ultimately, 11 s is a small amount in the scheme of a
student’s entire day. Although the current parking search time
and environmental savings do not justify an expensive system,
it is likely that an economy of scale could be taken advantage
of as more drivers share the cost of a university-wide system
and receive greater benefits. Moreover, one limitation of our
study was that we sampled only a portion of parking search
times for any given driver. One assumption we came across
as we searched for an explanation for our low search time
savings was that because the lot was across the street from the
main campus, it can be assumed that most drivers entering
the lot entered as a last resort and had already been searching
for some time. That assumption means additional time would
have been saved had the SPS governed multiple lots instead of
the one lot in this study.
CONCLUSION
In this article, we investigated how SPSs affect parking
search time using discrete event simulation. Our results
confirm that an SPS can make a positive difference in how
people use parking lots by reducing search time. As society
develops and grows in size, there will be a need to update
the transportation infrastructure to accommodate this new
growth. New SPS technology is a promising tool to improve
the search process as lots grow larger.
Although our findings were significant and savings could
be realized with an SPS, the costs of the system outweigh the
benefits in this case. There is reason to believe, however, that
the benefits of an SPS would be magnified on a greater scale
in which there were many lots to choose from, much like
the study conducted by Fries et al. (2010). The next step in
determining whether or not an SPS would be beneficial to
ERAU is to model multiple parking lots to get a more accurate
depiction of parking search time savings and their economic
impact.
REFERENCES
Brown-West, O. (1996). Optimization model for parking in the campus
environment. Transportation Research Record, 1564, 46–53. Retrieved
Figure 5. Parking search times for the base and experimental
models.
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from http://trb.metapress.com.ezproxy.libproxy.db.erau.edu/
content/11rum7360524r701/fulltext.pdf
Caicedo, F., Robuste, F., & Lopez-Pita, A. (2006). Parking management
and modeling of car park patron behavior in underground facili-
ties. Transportation Research Record, 1956, 60–67. Retrieved from
http://trb.metapress.com.ezproxy.libproxy.db.erau.edu/content/
p34158340372k00u/fulltext.pdf
Cohen, Z. (1993). Embry-Riddle Aeronautical University: Field house site.
Ormond Beach, FL: Zev Cohen & Associates.
Fries, R., Chowdhury, M., Dunning, A., & Gahrooei, M. (2010). Evaluation of
real-time parking information. Transportation Research Record, 2189, 1–7.
doi:10.3141/2189-01
Harris, J., & Dessouky, Y. (1997). A simulation approach for analyzing
parking space availability at a major university. In Proceedings of the 1997
Winter Simulation Conference (pp. 1195–1198). Piscataway, NJ: Institute
of Electrical and Electronics Engineers.
Kelton, W., Sadowski, R., & Swets, N. (2010). Simulation with Arena (5th ed.).
Boston, MA: McGraw-Hill.
Law, A. M. (2006). How to build valid and credible simulation models. In
Proceedings of the 2006 Winter Simulation Conference (pp. 58–66). Piscat-
away, NJ: Institute of Electrical and Electronics Engineers. Retrieved from
http://www.informs-sim.org/wsc06papers/006.pdf
Liu, Y. C., & Wen, M. H. (2004). Comparison of head-up display (HUD) vs.
head-down display (HDD): Driving performance of commercial vehicle
operators in Taiwan. International Journal of Human-Computer Studies,
61, 679–697.
Lu, R., Lin, X., Zhu, H., & Shen, X., (2009). SPARK: A new VANET-based
smart parking scheme for large parking lots. In Proceedings of the IEEE
INFOCOM (pp. 1413–1421). Piscataway, NJ: Institute of Electrical and
Electronics Engineers.
Maccubbin, R. P., & Hoel, L. A. (2000). Evaluating ITS parking management
strategies: A systems approach (No. UVA/29472/CE00/102). Charlottes-
ville: University of Virginia.
Medenica, Z., Kun, A. L., Paek, T., & Palinko, O. (2011). Augmented reality
vs. street views: A driving simulator study comparing two emerging navi-
gation aids. In Proceedings of the 13th International Conference on Human
Computer Interaction With Mobile Devices and Services (pp. 265–274).
New York, NY: Association for Computing Machinery.
Sheridan, T. B. (2004). Driver distraction from a control theory perspective.
Human Factors, 46, 587–599.
Tufano, D. R. (1997). Automotive HUDs: The overlooked safety issues.
Human Factors, 39, 303–311.
U.S. Department of Transportation. (2007). A smart parking field test
conducted for the California Department of Transportation and the Bay
Area Rapid Transit. Retrieved from http://www.itsknowledgeresources.its.
dot.gov/its/benecost.nsf/0/8A816D1AC8960BD1852573E7006C8E27?Ope
nDocument&Query=Home
U.S. Department of Transportation. (2008). The capital cost to implement a
smart parking system. Retrieved from http://www.itscosts.its.dot.gov/its/
benecost.nsf/SummID/SC2011-00215?OpenDocument&Query=Home
U.S. Environmental Protection Agency, Office of Transportation and Air
Quality. (2011). Greenhouse gas emissions from a typical passenger vehicle.
Retrieved from http://www.epa.gov/oms/climate/documents/420f11041.pdf
Yan-Zhong, B., Li-Min, S., Hong-Song, Z., Ting-Xin, Y., & Zheng-Jun, L.
(2006). A parking management system based on wireless sensor network.
Acta Automatica Sinica, 32, 968–977.
Glenn Surpris is a research associate at Design
Interactive, Inc. He has contributed to the devel-
opment of solutions that measure and quantify
human performance in training simulation
environments. His research interests include
tracking unobservable trainee performance in
simulators with helmet-mounted displays and
developing new training effectiveness evaluation methods. He
graduated from Embry-Riddle Aeronautical University with an
MS in human factors and systems and earned a BA in psychology
from Johns Hopkins University.
Dahai Liu is a professor in human factors
and systems at Embry-Riddle Aeronautical
University. He received his PhD in industrial
engineering from the University of Nebraska
at Lincoln. He has published extensively in
the area of system modeling and simulation,
human factors in unmanned systems, human–
computer interaction, and artificial intelligence. His work has
been funded by the Office of Naval Research, the Federal Aviation
Administration, and NASA.
Dennis Vincenzi received his PhD in human
factors psychology in 1998 from the University
of Central Florida. He is an assistant professor
in the College of Aeronautics at Embry-Riddle
Aeronautical University Worldwide. He has
more than 16 years of combined human factors experience in
the areas of training systems development, advanced simulation
interface design, augmented reality and helmet-mounted display
design and assessment, human performance assessment, and
unmanned aerial systems design, development, and analysis.
Copyright 2014 by Human Factors and Ergonomics Society. All rights reserved.
DOI: 10.1177/1064804614526202
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