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EZ Pass 2.0 for DOD Installation Entry
Jefferey Barr, Peter Liedman, Scherard Lindsay
College of Engineering, Florida State University
ESI 5590: Human Factors for Systems Engineering
Advisor: Dr David C. Gross
May 4, 2023
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Abstract
Entry points of DoD installations become quickly congested due to a bottleneck of vehicles
coming from different paths paired with traffic lights causing vehicles to stop and increasing
check point visits while personnel verify user credentials to ensure secure entry. Traffic studies
were conducted to determine the average time of traffic light intervals, peak traffic hours and
time required for a certain number of vehicles to pass through the check point. The installation of
a pre-verified user check in would mitigate the issues of bottlenecking at peak hours of
installation access and increase the level of security by having user data logged once a mile
away.
Keywords: Key Words here
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Purpose/Introduction
The purpose of this project is to understand the design limitations of the roadway
entrance to a secure facility as a system and to provide recommendations for decreasing the wait
time at the gate to improve traffic flow easing frustration and decrease security concerns.
As service members, civil servants and contractors begin to slowly go back into the office
the consequence of increased traffic comes alongside. Some installations allowed entry by selfscanning their Common Access Card (CACs) but with some army “bases will conduct the checks
in phases through mid-June with the exact timing determined by the base.”( Kheel,R.)
Hypothesis
A general simulation can be developed to understand the flow of traffic through a Entry
Control Point (ECP) and used to suggest improvements to decrease the wait time through the
ECP.
Human Factors issues
The human factors issues of our project are:
1.
Understanding the drivers' behaviors (arrival times, how long their wait is, cellphone use
while driving, getting ID, etc.)
2.
Understanding gate personnel/staffing needs to support surge in traffic
3.
Incorporating both influences into a system model for future access design and entrance
criteria.
Can these be linked to the Human Factor’s Goals (page 4 in the text)?
Literature Review
Method
Link to system engineering – especially the system engineering V
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The general methodology that was utilized when building this project was as follows:
Observe: Observe an analogous system to the one being tested (in our case the entrance
to NSWC PCD), from that observation collect preliminary data. In parallel perform a literature
review to understand the challenges for the project.
Simulate: Using the preliminary data and the literature review generate a simulation,
validate the simulation against the data to ensure the simulation is correct, collect additional data
as needed.
Analyze: Once the simulation is complete, run various scenarios to better understand the
system and what the causes and effects are that make the system perform well/poorly.
Report: Summarize and report findings
The flow chart below shows the methodology that was used to develop the model for this
project. To first understand the system, the system was observed using some basic questions:
What’s the situation (are cars backed up)? How many cars are entering the Entry Control Point?
Where do things seem to be backing up?
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After the initial observations, a basic simulation (in Rockwell Arena 2022) was created
using the questions and answers generated from the Observe Scenario step. While generating
that model some additional questions were generated.
Armed with additional data and a developing model, some statistical analysis was
performed on the data that was gathered. This analysis was used to determine if additional data
would be required or if enough data had been gathered.
Once complete, the simulation could then be used to complete the research. It’s
important to note that the data and model are not complete at this point but are enough to
understand the system in question and make some additional recommendations to the system
users and follow on simulation authors.
When analyzing the data the following procedure was used to determine if enough
(sample size) data had been gathered.
1.
Gather some data (small sample ≤ 30)
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2.
Determine maximum relative error (gamma) by educated guess.
3.
Perform Statistical analysis including sample number (N), mean, and variance for
gathered data.
4.
Calculate gamma prime
5.
Calculate the relative error (n*), based on the predetermined significance level
(alpha), mean, and variance for a gathered data.
6.
Very sample size (i) until calculated relative error (n*) ≤ gamma prime. This is
your predicted sample size.
Data
Data was gathered to understand the traffic light into the Entry Control Point, the Travel
time from the traffic light to the guard, the time it takes for the guard to clear a vehicle, and how
many cars are allowed through the turn signal at one time. As discussed later in this
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paper/presentation, the most important data was the timing of the turn signal, and the time for a
guard to clear a vehicle.
Once the data was gathered a basic statistical analysis was performed to determine if the
data gathered was enough to meet the appropriate relative error requirement (10%).If the data
wasn’t enough, but close enough the data would be considered good, if the sample size seemed
inadequate additional data would be gathered. After the relative error analysis, in which the
sample size was predicted, it was determined that the data was adequate to continue developing
the simulation, however it would be noted that additional data should be gathered to improve the
simulation.
Data
Mean
Max
Min
Standard
Deviation
Sample size
Min required
data
Guard
Processing
Time (sec)
Travel
Time (sec)
10.67
34.77
6.72
25.77
29.00
21.21
Turn Signal Schedule
Left
Green
Red
Turn
Light
Light
(sec)
(sec)
(sec)
37.22
27.28
31.51
42.45
37.24
38.89
34.32
20.54
22.19
5.14
3.46
2.46
4.30
6.80
27
5
10
10
10
78
8
4
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For the most part when comparing the sample size of the data to the “Minimum required
data”, most data was adequate. The Guard processing time data was grossly inadequate, but
valid enough to develop the model. In later iterations of the project, attention will need to be
paid to gathering more data in that area.
Explain how minimum required data was determined.
Simulation
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This is the final simulation (Rockwell Arena), the “left turn lane” create blocks generate
incremental series of cars, each generates 15 cars every 96 seconds based on the observations of
the traffic light before the ECP. There are four in case the user wants to simulate a different
traffic light pattern or real world scenario. This process is repeated 4 times to simulate the
morning rush from 0600 to 0620. The Time to Guard circle is a delay to simulate the time it
takes the vehicles to get to the guards, then the decision block (diamond shape “Entrance Lane”)
splits the cars between the two guard shacks and the fast pass lane. The decision block can be
adjusted to direct all the traffic to one guard or a combination of the three. Each guard process
block is set to process vehicles on a 10.67 sec mean processing time, with a standard deviation of
5.14. The three dispose blocks wrap up the simulation, and the pass inspection is just for fun, for
future simulations. The counter (blue 0) and graph are set up to log the number of vehicles in
line in “real time”.
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Results
While observing the traffic flow the team realized that the two key components of the
simulation was the traffic entering the facility, in this case how many cars the turn signal was
allowing each light cycle, and how many cars the guard(s) were able to able to process. If the
guards couldn’t process all the cars each cycle, then it would create a backup and the problem
would start to compound. This graph shows how many cars accumulate in line if just one guard
tries to process 15 cars per light cycle.
The distance between the ECP and the turn signal can hold a maximum of 20 cars. If the
simulation consistently gets above 20 cars, then the guards are having trouble processing the
incoming traffic. This can be shown in the graph below, there are time’s when the standing
traffic approaches the 20 car mark (left axis of graph). In this scenario the guards are just
holding their own. This simulation is set up to generate 15 cars every 96 seconds, and two
guards are processing cars as the same rate (10.67 mean, 5.14 stdv) as before. Think two guards
two lanes.
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On this third simulation the team has added a “fast pass” lane, it doesn’t require a guard
but does take the same amount of time as a guard. So, three lanes clearing cars. This graph
shows that the three lanes are better keeping up with the cars, the red line which indicates
number of cars, reaches or gets near zero more often than the other two simulations. Since most
peaks top out at around 15 it shows that the process can clear all 15 cars entering the facility each
traffic light cycle.
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Results
Probably should explain the results
Simulation
One Guard
Time
58’50”
Two Guards
Two Guards & Fast
Pass
33’50”
33’15”
Max backup
>128,
unacceptable
20, 21, Borderline
20, acceptable
Observations
Backup out of control
Barely handled
Barely handled
Discussion
To better understand the effects that guard processing time had on entrance queue length
the team build a simulation. This simulation allowed the team to very the number of vehicles
entering the check point and number of guard stations to determine the main influences of the
system.
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One main goal of the project was to understand if adding additional lanes increased the
efficiency in the process of entering DoD installations during peak traffic hours. “Additional
lanes” might include the construction of additional lanes, or the creation of a fast pass system.
With the additional use of technology base security forces can operate with minimal
stress and keep a consistent flow of traffic mitigating the congestion of base entry points at peak
times. The additional use of multiple guards in one lane provides positive results and anticipation
of peak traffic hours mitigate traffic congestion.
Key Takeaways / Conclusion
Reference some of the human factors interventions from pages 6 & 7 in the textbook and
link them back to the goals

If the guard(s) can clear the 15 cars from the Left Turn Lane every 105 seconds, there
will never be a backup. It doesn’t matter if this takes 2 guards or 12. But there’s no need
to add extra guards, they’ll just cost more money. A corollary to this would be, an EZ
Pass lane would need to clear people as fast or faster than a guard to be of any value to
the system.

Having the guards in place early will help, the simulation shows that the traffic backup
compounds quickly (within 3-4- light cycles with one guard processing vehicles); having
two guards ready for rush hour will keep the backup to a minimum.

Two guards, or even two guards and the fast pass are barely holding on so any delay in
vehicle processing (extra long look at badge or car has to turn around) will begin the
compounding problem. It’s hard to recover from this delay in the height of rush hour.

Implementation of ride sharing & public transportation programs is an additional and
cost-effective alternative.
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
Users should avoid peak hours

Because of the constant and relentless flow of traffic, 3 lanes are only slightly more
efficient than 2 lanes, consideration should be given to the cost of introducing a third
lane.

At peak hours and extra delay in processing time can have large effects in entrance delay
times.

1. Implement staggered work schedules: One of the most effective ways to reduce
congestion during entry in the mornings is to implement staggered work schedules. This
means that employees will start and end their workday at different times, which will help
to spread out the traffic and reduce congestion.

2. Increase public transportation options: Another way to reduce congestion is to increase
public transportation options. This can include providing shuttle buses or encouraging
employees to carpool or use public transportation.

3. Improve traffic flow: Improving traffic flow can also help to reduce congestion during
entry in the mornings. This can include adding additional lanes, improving traffic signals,
or implementing traffic calming measures.

4. Encourage telecommuting: Encouraging employees to work from home or
telecommute can also help to reduce congestion during entry in the mornings. This can be
especially effective for employees who do not need to be physically present at the
installation
Suggestion for future work
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References
Human Factors | FHWA. (n.d.). Retrieved February 9, 2023, from
https://highways.dot.gov/research/research-programs/safety/human-factors
Human Factors | NHTSA. (n.d.). [Text]. Retrieved February 9, 2023, from
https://www.nhtsa.gov/research-data/human-factors
Salimifard, K., & Ansari, M. (2013). Modeling and Simulation of Urban Traffic Signals.
International Journal of Modeling and Optimization, 172–175.
https://doi.org/10.7763/IJMO.2013.V3.261Links to an external site.
Yuniawan, D., Fajar, A., Hariyanto, S., & Setiawan, R. (2018). Traffic Queue Modeling Using
Arena Simulation Software (a Case study of Mergan 4-Way Intersection in Malang City).
6.
National Academies of Sciences, Engineering, and Medicine. (2018). Improving operational
capabilities and reducing congestion in the national airspace system: Proceedings of a
workshop. The National Academies Press.
Kheel,R. (Apr 28, 2023) Soldiers and Other Visitors to Army Bases Could See Heavy Traffic at
Gates Amid '100% ID Checks'. Retrieved Apr 28,2023 from
https://www.military.com/daily-news/2023/04/28/major-delays-getting-army-basespossible-service-launches-100-id-checks.html
A note on references: References should be used and cited in the paper, otherwise they are a
bibliography. And Dr Gross says “I don’t know why anyone would read a source and not
use it in the paper”.
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Appendix A
If we need it
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Appendix B
If we need it