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Final Presentation Dec. 12, 2008

PARKme System. Final Presentation Dec. 12, 2008. Craig Emmerton Earl Morton Shaun McDonald David Richards Nikki Torres-Avila. 1. 1. Problem Statement.

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Final Presentation Dec. 12, 2008

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  1. PARKme System Final PresentationDec. 12, 2008 Craig Emmerton Earl Morton Shaun McDonald David Richards Nikki Torres-Avila 1 1

  2. 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 2

  3. GMU Survey Average time spent to find a space at GMU *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

  4. System Concept of Operations (OV-1) 4 4 High-Level Operational Concept Graphic (OV-1), DoDAF Version 1.5, 23 April 2007

  5. Project Role & Deliverables PARKme Team Role • System Developer / Integrator • Collect stakeholder’s needs • Develop requirements • Analyze different architectures • Functionally decompose the system PARKme System Deliverables Business Case Net Cash Flow 10 Year Plan Prototype / Simulation 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

  6. Work Breakdown Structure WBS SCHEDULE GANTT Chart PERT Chart

  7. PARKme Risk Management

  8. 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

  9. Stakeholder Needs Analysis Determine User Preferences Find Parking <<include>> Driver <<include>> Update parking availability PARKme System Formalized scenarios and translated them to use cases

  10. Quality Function Deployment

  11. Functional Architecture

  12. Analysis of Alternatives 12 • 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.

  13. 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’.

  14. LDW & Rankings of Alternatives Ranking of Sensors Ranking of Human Interfaces LDW Output Used for Architecture Comparison Ranking of Connectivity

  15. Evaluation of Alternatives 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

  16. 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.

  17. System Design Top Level Software Functions Hardware Interface Diagram 17

  18. 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

  19. Business Case Provide reasoning and justification for entering the market Stakeholder Benefits PARKme Benefits 19

  20. Cash Flow

  21. Sensitivity/Decision Analysis Decision Tree Branch Tornado Diagram

  22. PARKme Modeling Efforts Small Fully Scalable Models Proof of Concept Top Level CPN Model Monte-Carlo Timing Analysis MathWorks MatLab

  23. Timing Model statistics • 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

  24. 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!

  25. Summary • University Image • Technology Oriented Campus • Embracing Green Movement

  26. End of Brief Comments & Questions? 26 26

  27. Backup Slides 27 27

  28. Functional Decomposition

  29. Activity Diagram – IDEF0 29

  30. Activity Model – Data Flow Diagram 30

  31. State Transition Diagram

  32. CPN Tools Top Level Architecture RETURN

  33. Digital Signs - Colored Petri Net Parking Lot Driver User Interface Space Locator RETURN

  34. PED - Colored Petri Net Driver Parking Lot User Interface Space Locator RETURN

  35. Monte-Carlo Model Preferred Building Average human walks at 60 ft/minute (WikiAnswers.com) 900 feet 300 feet 600 feet 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

  36. PARKme System RETURN

  37. Parking Statistics RETURN

  38. Timing Model Statistics • ===================================================== • 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 • ===================================================== Timing Model Output: 90% Lots Full Case • 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

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