PARKme System
Final Presentation
Dec. 12, 2008
Craig Emmerton
Earl Morton
Shaun McDonald
David Richards
Nikki Torres-Avila
1
Problem Statement
“Finding a parking space at GMU is a common frustration for commuters.
Campus parking lots are often overcrowded during certain times of the day and
week making parking a guessing game. This leads to students, faculty, &
visitors being late for classes and appointments.”
Utilized Six Sigma Methods in Developing the Problem Statement
•
•
•
•
Define the Problem
Identify Where the Problem is Appearing
Describe the Size of the Problem
Describe the Impact the Problem is Having on the Organization
2
GMU Survey
Average time spent to find a space at GMU
Time
(minutes)
Number of
Individuals
Percent(%)
5
1399
32
10
1063
24
Individuals Polled
Average Time to Find a P arking Space - GM U Campus
1400
1200
1000
800
20
847
19
30
450
10
40*
589
13
600
400
200
0
5
10
20
30
40
Tim e (m inutes)
*Worst case assumed is 40 minutes
Average Time (Mean) = 16.5 minutes
Standard deviation (σ) = 12.20409
Data is widely spread around the calculated mean of the data
PARKme Goal: Average Time Under 8 Minutes!
Data Provided by Josh Cantor, Director of Parking for GMU
3
System Concept of Operations (OV-1)
High-Level Operational Concept Graphic (OV-1), DoDAF Version 1.5, 23 April 2007 4
Project Role & Deliverables
PARKme Team Role
PARKme System
Deliverables
Business Case
System Developer / Integrator
•Collect stakeholder’s needs
•Develop requirements
•Analyze different architectures
•Functionally decompose the
system
Prototype / Simulation
Net Cash Flow
10 Year Plan
Colored Petri Net of System
Monte Carlo Analysis
Technical Plan
Statement of Work (SOW)
Stakeholder Analysis Report
Concept of Operations (CONOPS)
System Engineering Management Plan
(SEMP)
Analysis of Alternatives (AOA)
Risk Management Plan (RMP)
System Requirements Specification (SRS)
System Design Document (SDD)
CPN Description Document
Monte Carlo Analysis
Technology Strategy
5
Work Breakdown Structure
WBS
SCHEDULE
GANTT Chart
PERT Chart
6
PARKme Risk Management
7
Stakeholder Analysis
• Stakeholder Identification
– End User
– GMU Administration
• GMU Police/Security
–
–
–
–
Project Manager
GMU Maintainer
Engineers
Project Sponsors
• Key Stakeholders
–
–
–
–
End User
GMU Administration
Project Sponsors
Project Manager
Methodology developed by the Imperial College of London,
Used in the government and private industry
8
Stakeholder Needs Analysis
Find Parking
<<include>>
Driver
Determine User
Preferences
<<include>>
Update parking
availability
PARKme System
Formalized scenarios and translated them to use cases
9
Quality Function Deployment
10
Functional Architecture
11
Analysis of Alternatives
•
Research
•
•
•
•
Researched parking alternatives on the Internet.
Study previous academic research.
Alternatives include:
• Utilizing existing parking system with minor updates.
– Minor Updates include Parking Gate
• Valet Parking
• Automated parking systems
• Electronic devices (Sensors)
• Identified requirements to implement these alternatives.
• Analyze the benefits and constraints of each of the systems.
Conclusion
• Utilizing existing parking system with entry gates would not
improve the time required to find empty parking spaces.
• Valet parking would not be appropriate solution for a campus
parking environment.
• Automated parking would be very expensive investment and
require complete redesign of the current parking at GMU
• Sensors would be minimum impact on existing parking structure,
and provide maximum return on investment.
12
AoA Methodology
• AoA Methodology
• Use commercial-off-the-shelf (COTS) architectures.
• Components are interchangeable, new technology easily
incorporated.
• Logical Decisions for Windows (LDW)
• General Definition
• Project survey submitted to Sponsor and fellow classmates
• Each alternative had a list of criteria used as weights
• Six criteria that are being used in each of our subcomponents.
• ‘Start Up Cost’
• ‘Maintenance Cost’
• ‘Construction’
• ‘Maturity’
• ‘Reliability’
• ‘Time Between Failures’.
13
LDW & Rankings of Alternatives
Ranking of Sensors
Ranking of Human Interfaces
LDW Output Used for Architecture Comparison
Ranking of Connectivity
14
Evaluation of Alternatives
Morphological Box for PARKme System
Control System
Space Sensors
Human Interface
Connectivity
Server Network
Weight Plates
Kiosk Units
WiFi Network
Camera
Electronic Signs
T/R Antenna
RFID
GPA Device (PED)
Hard Cabled
Infrared
Internet (PED)
Cell Towers
Ultra-Sound
PDA (PED)
Motion Detector
Cell Phone (PED)
Light Sensor
Patriot Web
Weighing Factors (1-5: Lower is better)
Start Up Cost:
3 (Medium importance)
Monthly Cost:
1 (High importance)
Time between Failures:
3 (Medium importance)
Reliability:
1 (High importance)
Maturity:
3 (Medium importance)
Feasibility:
1 (High importance)
• Sensors (Transceiver)
• Wireless networking components
• Housed in a plastic covering similar in size to a street reflector
• One per parking space
• Transmits parking data via the communications network
• Communications Network
• The parking space information to the our main system.
• Mesh Network
• Main System
• The main system will be the interface between the parking space information
and the end user.
• Network server (Software)
• Human Interfacing
• Transfers parking space availability information from the main system to the user.
• In our case, electronic signs are used to relay parking space information
Creativity Techniques, The Engineering Design of System, 2000
15
Results
• Sensors
• RFID sensor chosen; weight sensor eliminated
• Weight Sensor eliminated because of surveyed construction impact
• Our student survey weighted the construction criteria as very high.
• The second highest ranking is the light sensor.
• Light sensor eliminated because of reliability!
• Human Interface (PED ranked highest)
• Initial implementation of the system will be electronic signs
• Incorporation of PEDs
• Most of the remaining options were very closely ranked except for Kiosk.
• Includes portable devices: cell phones, or laptops with Internet
connectivity.
• Connectivity
• Wi-Fi chosen; Cell towers ranked highest
• Campus control over Wi-Fi verses Cell towers
• LDW ranked the Wi-Fi network as the second preferred selection.
16
System Design
Top Level Software Functions
Hardware Interface Diagram
17
Technology Strategy
• Technology Readiness Levels
• PARKme System Requires a TRL of at
least 7
• Proprietary Software
• PARKme Software licensed for use only
on PARKme Computer Systems
• Underlying software used by the
PARKme System will be licensed for use
from corresponding software companies
• Intellectual Property Rights
• Patent search reveals 1 patent and 3
patent applications of similar systems
• Application for patent for concept of the
PARKme System
• PARKme System designed with modular
components in an “open architecture”
18
Business Case
• Provide reasoning and justification for entering the market
– Stakeholder Benefits
– PARKme Benefits
GMU Purchase
(no partnership)
GMU Purchase
(with partnership)
Development funds provided by GMU
$0
$14,000
Development funds from PARKme
$68,950
$13,000
Total Development Cost
$68,950
$27,000
Cost to GMU
$1,060,737
$1,014,066
Expected Profit to PARKme (GMU
purchase only)
$162,914
$135,279
Net Present Value (based on sale of 19
systems over 10 years)
$1,932,235
$1,906,495
19
(500,000.00)
on
t
M h1
on
t
M h2
on
t
M h3
on
t
M h4
on
t
M h5
on
t
M h6
on
t
M h7
on
t
M h8
on
M th
on 9
t
M h 10
on
t
M h1
on 1
th
1
Ye 2
ar
Ye 2
ar
Ye 3
ar
Ye 4
ar
Ye 5
ar
Ye 6
ar
Ye 7
ar
Ye 8
a
Ye r 9
ar
10
M
Cash Flow
3,500,000.00
3,000,000.00
2,500,000.00
2,000,000.00
Net Cash flow
Cumulative Cash flow
1,500,000.00
1,000,000.00
500,000.00
0.00
20
Sensitivity/Decision Analysis
PARKme Analysis
1,542,200
{10}
4,438,670
{28}
1,985,050
{110,000}
0
3,543,050
{192,000}
Systems Sold
Decision Tree Branch
Profit Expectation
2,959,385
{100,000}
3,046,385
{13,000}
Development Cost
2,954,435
{4,000}
3,017,435
{500}
Fixed cost per system
500,000 1,000,000 1,500,000 2,000,000 2,500,000 3,000,000 3,500,000 4,000,000 4,500,000 5,000,000
Tornado Diagram
21
PARKme Modeling Efforts
Small Fully Scalable Models
Monte-Carlo Timing Analysis
MathWorks MatLab
Proof of Concept
Top Level CPN Model
22
Timing Model statistics
35
30
25
20
Timing Model
15
GMU Data
10
5
0
5
10
20
30
40
50
GMU: Main campus parking conditions
• Inner campus lots full during peak times
• Outer overflow lots at 85.5% full
• 16.5 minutes on average spent looking for a
parking space
• Over 25% of students spend an average of
over 30 minutes
Parameters Modeled
• 90 % probability a parking lot is full
• 100,000 Monte-Carlo runs
Model Differences
• First students on campus always get preferred
lots
• Late students may go directly to overflow lots
• Other students have insight into best lot from
past experience
• Students may choose to park nearest to
building not hosting first class
View Actual Model Output
23
Timing Model Results
•
To compare our data with the data provided from GMU it can be noted that
the worst case of 90% is an acceptable model.
•
Our worst case model reflects an average of over 30 minutes spent looking
for a free parking space.
•
Our modeled worst case reflects an average of over seven lots searched
before a parking lot with available spaces is found.
•
•
•
Using the PARKme system a parking lot with available spaces could have
been found in 5 minutes.
Compared to the current GMU times this is a saving of over 10 minutes for
the average case and 30 minutes for the GMU worst case.
PARKme Goal: Average Time Under 8 Minutes!
24
Summary
University Image
• Technology Oriented Campus
• Embracing Green Movement
25
End of Brief
Comments & Questions?
26
Backup Slides
27
Functional Decomposition
28
Activity Diagram – IDEF0
29
Activity Model – Data Flow Diagram
30
State Transition Diagram
31
CPN Tools Top Level Architecture
RETURN
32
Digital Signs - Colored Petri Net
Parking
Lot
Driver
User
Interface
Space
Locator
RETURN
33
PED - Colored Petri Net
Driver
Parking
Lot
User
Interface
Space
Locator
RETURN
34
Monte-Carlo Model
Preferred
Building
300 feet
Average human walks at
60 ft/minute (WikiAnswers.com)
Parking
Lot (1)
Preferred
Parking
Lot (2)
Parking
Lot (3)
Parking
Lot (4)
Parking
Lot (5)
Until a Parking Space is Found
4 minutes spent driving to each parking lot
1 minute spent searching each parking lot
Parking
Lot (10)
Overflow
Parking
Lot (9)
Parking
Lot (8)
Parking
Lot (7)
Parking
Lot (6)
1 minute
Facility Perimeter
RETURN
35
PARKme System
RETURN
36
Parking Statistics
35
30
25
20
Timing Model
GMU Data
15
10
5
0
5
10
20
30
RETURN
40
50
37
Timing Model Statistics
Timing Model Output: 90% Lots Full Case
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
=====================================================
Number of Monte Carlo Runs:
100000
-------------------------------------------------------------------------------This is pure time to find the parking space
Minimum Time:
5.00 minutes
Maximum Time:
50.00 minutes
Mean Time:
32.63 minutes
Mode Time:
50.00 minutes
Mode Occurrences:
39072.00
Median Time:
35.00 minutes
Time Variance:
290.44
Time Standard Deviation:
17.04
-------------------------------------------------------------------------------This is the time to find the parking space and walk to the preferred building
Minimum Time:
10.00 minutes
Maximum Time:
100.00 minutes
Mean Time:
65.26 minutes
Mode Time:
100.00 minutes
Mode Occurrences:
39072.00
Median Time:
70.00 minutes
Time Variance:
1161.77
Time Standard Deviation:
34.08
-------------------------------------------------------------------------------Minimum Number of Lots Searched:
1.000
Maximum Number of Lots Searched:
10.00
Mean Number of Lots Searched:
6.53
Mode Number of Lots Searched:
10.00
Mode Occurrences:
39072.000
Median Number of Lots Searched:
7.00
Number of Lots Searched Variance:
11.62
Number of Lots Searched Standard Deviation: 3.41
=====================================================
GMU: Main campus parking conditions
•
•
•
•
Inner campus lots full during peak times
Outer overflow lots at 85.5% full
16.5 minutes on average spent looking for a parking space
Over 25% of students spend an average of over 30 minutes
Parameters Modeled
•
•
•
•
•
•
Distance from preferred lot to preferred building is 300 ft.
90 % chance a parking lot is full
10% chance a available lots fills up before the user arrive.
10 parking lots were modeled.
4 minutes t drive to a parking lot
1 minute to search a parking lot
RETURN
38