This document proposes a prototype for archiving and mining ITS data using UML, XML, and object-oriented database design. The prototype aims to address issues with existing methods for data archiving, mining, sharing and ensuring accuracy. It will be tested through surveys of transportation professionals to determine if the prototype improves upon current practices. Chi-square analysis will be used to analyze the survey results and test hypotheses about the prototype.

1. An ITS Data Archive Prototype Using UML, XML, and OO Database Design A Dissertation Proposal Submitted to Committee Chairperson: Dr. Catherine Lawson and Committee members: Dr. Jagdish Gangolly and Dr. Peter Duchessi by Maggie Cusack

7. i.e.: NYS Bridge Authority Travel

8. The Future of the Highway Network? NY Metropolitan Region: 15,000 additional trucks a day in 1998 Source: Cambridge Systematics 30% 20% 10% 0% New York Metropolitan Regional Freight Tonnage, 1995-2020: 27% Growth, Most by Truck Population Freight Tonnage

14. IEEE 1489 Layer 7: Application Layer 6: Presentation Layer 5: Session Layer 4: Transport Layer 3: Network Layer 2: Data Link Layer 1: Physical Data ISO / OSI 7-Layer Model This is our work area. Transforms file messages Handles file format differences Provides synchronization of data flow Provides end to end delivery Switches and routes information (router) Delivers information to the next nodes Transmits bit stream on physical medium Speed: 47km/h V=47km/h 01010011 01010011 01010011 01010011

20. “ Sausage Diagram”- Describes ITS Subsystems

23. Traffic Management Subsystem Interfaces

24. Context Diagram of Archived Data Management Subsystem Interfaces

25. Context Diagram of Archived Data Management Terminators

31. Hypotheses The survey data will be used to test (reject) this null hypothesis: H O1 : The prototype is no different than existing methods for data archiving. There are several other hypothesis to be tested with this data: H O2 : The prototype is no different than current methods for data mining. H O3 : The prototype is no different than current methods for data sharing. H O4 : The prototype is no different than current methods to assure data accuracy.

34. Concept Mapping for ITS

38. Practitioner Information Name___________________________________________________________________ Title____________________________________________________________________ Organization/Company_____________________________________________________ Address_________________________________________________________________ State________________________________________ZIP_________________________ Phone_______________________________________FAX_______________________ Fill out the above or staple a business card in place. Practitioner Profile Post Secondary Education. [ ] None [ ] 2-4 Years [ ] 4+ Years Number of Years in Current [ ] 0-4 Years [ ] 5-10 Years [ ] 10+ Years Position or Related Position. How Would You Classify [ ] Policy or [ ] Mid Level [ ] Staff or Your Role in Your Agency? Upper Management Management Technical Estimated Population Served [ ] Less than 1 M [ ] 1-10M [ ] 10M + By Your Agency. [ ] Federal Government or Contractor Estimated Annual Resources [ ] $0 [ ] $0-1M [ ] $1M + Your Agency Allocates to Data Management Tasks. Percent of Your Time [ ] 0 % [ ] 0-50% [ ] 50% + Spent on Data Management. Reaction to the Prototype In Your Opinion, Does The Prototype Achieve Any of the Following Goals: Improve Data Archiving?: [ ] Yes [ ] No [ ] Not Sure Improves Data Mining? [ ] Yes [ ] No [ ] Not Sure Improves Data Sharing? [ ] Yes [ ] No [ ] Not Sure Assures Data Accuracy? [ ] Yes [ ] No [ ] Not Sure Please describe in as much detail as possible the current procedures, software, operating systems and data base systems that your agency/company uses for managing, archiving, and mining ITS data. Use the back of this form. Thank you for your participation.

41. Typical Chi-square analysis Expected values. Population served by Agency Favor Prototype Do Not Favor Prototype Not involved in data archiving Less than 1 M 250 25 25 1 to 10 M 250 25 25 Over 10 M 250 25 25

42. Typical Chi-square analysis (SAMPLE) Actual values. Population served by Agency Favor Prototype Do Not Favor Prototype Not involved in data archiving Less than 1 M 185 75 40 1 to 10 M 120 125 55 Over 10 M 290 10 0