An ITS Data Archive Prototype Using UML, XML, and OO Database Design A Dissertation Proposal Submitted to Committee Chairp...
Abstract <ul><li>Knowledge and information form the basis of informed travel behavior.  </li></ul><ul><li>The foundation o...
Abstract <ul><li>In-vehicle navigation systems (IVNS), variable message signs (VMS) and other technologies have the potent...
Abstract <ul><li>The data archiving and data mining efforts that support these ITS technologies is the subject of this inv...
Abstract <ul><li>This work proposes to establishing and test a data archiving ontology that will be built from industry la...
Background to the Problem <ul><li>Influential Trends: </li></ul><ul><ul><li>1. an increase in the volumes of data being co...
i.e.: NYS Bridge Authority Travel
The Future of the Highway Network? NY Metropolitan Region:  15,000 additional trucks a day in 1998 Source:  Cambridge Syst...
Background to the Problem <ul><li>data collection and communication technologies have improved significantly in the past f...
Background to the Problem <ul><li>In the field of transportation, data is used to understand the state of the system, and ...
Scope of this Endeavor <ul><li>This paper proposes that using </li></ul><ul><ul><li>standard database design </li></ul></u...
Existing ITS Data Standards and Ontologies <ul><li>National ITS Architecture </li></ul><ul><li>National Transportation Com...
IEEE  Data Definition SWG <ul><li>Many light-duty motor vehicles, and increasing numbers of heavy commercial vehicles, are...
IEEE 1489  Layer 7: Application Layer 6: Presentation Layer 5: Session Layer 4: Transport Layer 3: Network Layer 2: Data L...
Existing sets of IEEE transportation data definition standards for ITS <ul><ul><li>1455-1999  </li></ul></ul><ul><ul><ul><...
IEEE ITS Data Registry <ul><li>A centralized data dictionary or repository for all ITS data elements and other data concep...
Open GIS Consortium <ul><li>Timely, accurate geospatial information and geoprocessing services - easily accessible and cap...
 
ITS Architecture <ul><li>National (USDOT/FHWA/FTA) </li></ul><ul><li>Statewide (NYSDOT) </li></ul><ul><li>Regional (NYSDOT...
“ Sausage Diagram”- Describes ITS Subsystems
 
 
Traffic Management Subsystem Interfaces
Context Diagram of Archived Data Management Subsystem Interfaces
Context Diagram of Archived Data Management Terminators
 
Traffic Management Data Dictionary (TMDD) – AASHTO/FHWA/ITE <ul><li>Data dictionaries work in conjunction with at least tw...
Archived Data User Services ADUS <ul><li>The specifications for travel monitoring data are necessary because these data ar...
The Federal emphasis on ADUS is meant to: <ul><li>Create a single stream data management system </li></ul><ul><li>Foster d...
 
Hypotheses The survey data will be used to test (reject) this null hypothesis:  H O1   : The prototype is no different tha...
Methodology <ul><li>Create prototype </li></ul><ul><li>Survey Transportation researchers and other professionals using an ...
Methodology <ul><li>Interviewer will determine if the practitioner’s responsibilities are appropriate, and fill out the fi...
Concept Mapping for ITS
 
 
 
Practitioner Information   Name___________________________________________________________________ Title__________________...
Methodology <ul><li>Analysis of the data will yield a wealth of information and may reveal trends and biases in many areas...
Test results will provide information on (examples): <ul><li>Number of years Practitioner has been in the field of Transpo...
Typical Chi-square analysis Expected values. Population served by Agency Favor Prototype Do Not Favor Prototype Not involv...
Typical Chi-square analysis (SAMPLE) Actual values. Population served by Agency Favor Prototype Do Not Favor Prototype Not...
Analysis expected cell count (observed cell count – expected cell count) 2 X 2  = <ul><li>Rejection region:  </li></ul><ul...
 
Conclusion <ul><li>Contribution to National efforts to standardize, simplify and promote data sharing between stakeholders...
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  1. 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
  2. 2. Abstract <ul><li>Knowledge and information form the basis of informed travel behavior. </li></ul><ul><li>The foundation of individual choice in transportation decision-making is based on availability and robustness of traveler information. </li></ul><ul><li>Historical information or learned information combined with new information contributes to the individual decision-maker’s mode, route and travel time choices. </li></ul><ul><li>Information support systems known as Intelligent Transportation Systems (ITS) have evolved along with technological advances to divulge information to travelers. </li></ul>
  3. 3. Abstract <ul><li>In-vehicle navigation systems (IVNS), variable message signs (VMS) and other technologies have the potential to help alleviate overall capacity problems and incident related capacity overloads on existing roadways, on transit systems, at airports and ports. </li></ul><ul><li>Applications of ITS can also be used to guide non-motorized travel. </li></ul><ul><li>By using dynamic traffic assignment (DTA) models populated with streams of real-time field information from field devices accurate and timely information can be broadcast to the traveling public via VMS, IVNS and other broadcast technologies. </li></ul>
  4. 4. Abstract <ul><li>The data archiving and data mining efforts that support these ITS technologies is the subject of this investigation. </li></ul><ul><li>This work contributes to the advance of database organization, design and transferability between software systems, operating systems and network environments. </li></ul><ul><li>This work suggests a standard ontology for transportation information for ITS data archiving and mining, because there is a demonstrated need for an industry wide nomenclature. </li></ul>
  5. 5. Abstract <ul><li>This work proposes to establishing and test a data archiving ontology that will be built from industry language and standards, based on sound Information Technology (IT) principals, including the use of: </li></ul><ul><ul><ul><li>The Unified Modeling Language (UML); </li></ul></ul></ul><ul><ul><ul><li>eXtensible Mark-up Language (XML); </li></ul></ul></ul><ul><ul><ul><li>and Object Oriented (OO) database design. </li></ul></ul></ul><ul><li>This is a proposed industry-wide solution to the ITS data archiving problem, this work will also contribute to many other developments in data sharing. </li></ul>
  6. 6. Background to the Problem <ul><li>Influential Trends: </li></ul><ul><ul><li>1. an increase in the volumes of data being collected, stored and managed, and </li></ul></ul><ul><ul><li>2. an increase in the critical need to manage the transportation system to accommodate the increase in overall system volumes. </li></ul></ul>
  7. 7. i.e.: NYS Bridge Authority Travel
  8. 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
  9. 9. Background to the Problem <ul><li>data collection and communication technologies have improved significantly in the past few years </li></ul><ul><li>a common language for transferring information inside and between transportation agencies and other stakeholders is under development but still does not yet exist </li></ul><ul><li>a few efforts have been made to exploit available technologies for communication </li></ul><ul><li>very few successfully span the dirge of information types </li></ul>
  10. 10. Background to the Problem <ul><li>In the field of transportation, data is used to understand the state of the system, and to project the future state of the system, while the communication of that knowledge to the public is understood to be the most beneficial way of improving the overall function of the system. </li></ul><ul><li>This is based on the precept that drivers can change their behavior if the information they receive is timely, accurate and informative. </li></ul>
  11. 11. Scope of this Endeavor <ul><li>This paper proposes that using </li></ul><ul><ul><li>standard database design </li></ul></ul><ul><ul><li>simple object transfer concepts and rules </li></ul></ul><ul><ul><li>along with current data mining concepts </li></ul></ul><ul><li>can greatly enhance the state of the art in ITS for: </li></ul><ul><ul><li>data collection </li></ul></ul><ul><ul><li>data storage (data archiving) </li></ul></ul><ul><ul><li>manipulation </li></ul></ul><ul><ul><li>retrieval, and </li></ul></ul><ul><ul><li>ultimately better use of data (data mining) in the decision making processes. </li></ul></ul>
  12. 12. Existing ITS Data Standards and Ontologies <ul><li>National ITS Architecture </li></ul><ul><li>National Transportation Communications for ITS Protocol - NTCIP </li></ul><ul><li>Archived Data User Services – ADUS </li></ul><ul><li>ITS Data Registry </li></ul><ul><li>Transportation Management Data Dictionary - TMDD </li></ul><ul><li>Unified Network Transportation System – UNETRANS </li></ul><ul><li>Open GIS Consortium - OGC </li></ul>
  13. 13. IEEE Data Definition SWG <ul><li>Many light-duty motor vehicles, and increasing numbers of heavy commercial vehicles, are equipped with some form of event data recorder. </li></ul><ul><li>These systems, which are designed and produced by individual motor vehicle manufacturers and component suppliers, are diverse in function, and proprietary in nature. </li></ul><ul><li>The continuing implementation of Event Data Records (EDR) systems provides an opportunity to voluntarily standardize the output format of data elements by multiple vehicle manufacturers, which will make MVEDR data more useful to end-users. </li></ul><ul><li>The recommendations of this SWG will supplement the work of the output SWG by providing a basis for defining output protocols. </li></ul>
  14. 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
  15. 15. Existing sets of IEEE transportation data definition standards for ITS <ul><ul><li>1455-1999 </li></ul></ul><ul><ul><ul><li>IEEE Standard for Message Sets for Vehicle/Roadside Communications </li></ul></ul></ul><ul><ul><li>1488-2000 </li></ul></ul><ul><ul><ul><li>IEEE Trial-Use Standard for Message Set Template for Intelligent Transportation Systems </li></ul></ul></ul><ul><ul><li>1489 </li></ul></ul><ul><ul><ul><li>Standard for Data Dictionaries for Intelligent Transportation Systems </li></ul></ul></ul><ul><ul><li>1489 </li></ul></ul><ul><ul><ul><li>Standard for Data Dictionaries for Intelligent Transportation Systems RR </li></ul></ul></ul><ul><ul><li>1489-1999 </li></ul></ul><ul><ul><ul><li>Standard for Data Dictionaries for Intelligent Transportation Systems </li></ul></ul></ul><ul><ul><li>1512-2000 </li></ul></ul><ul><ul><ul><li>IEEE Standard for Common Incident Management Message Sets for use by Emergency Management Centers </li></ul></ul></ul><ul><ul><li>P1512.1 </li></ul></ul><ul><ul><ul><li>Standard for Traffic Incident Management Message Sets for Use by Emergency Management Centers </li></ul></ul></ul><ul><ul><li>P1512.2 </li></ul></ul><ul><ul><ul><li>Standard for Public Safety Incident Management Message Sets for Use by Emergency Management Centers </li></ul></ul></ul><ul><ul><li>P1512.3 </li></ul></ul><ul><ul><ul><li>Standard for Hazardous Material Incident Management Message Sets for Use by Emergency Management Centers </li></ul></ul></ul><ul><ul><li>P1512a </li></ul></ul><ul><ul><ul><li>Standard for Emergency Management Data Dictionary </li></ul></ul></ul><ul><ul><li>1512b </li></ul></ul><ul><ul><ul><li>Amendment for Implementing Foreign Data Elements Found In Standard for Common Incident Management Message Sets For Use By Emergency Management Centers, IEEE 1512-2000 </li></ul></ul></ul><ul><ul><li>1512b </li></ul></ul><ul><ul><ul><li>Amendment for Data Elements Found In Standard for Common Incident Management Message Sets For Use By Emergency Management Centers, IEEE 1512- 2000 </li></ul></ul></ul>
  16. 16. IEEE ITS Data Registry <ul><li>A centralized data dictionary or repository for all ITS data elements and other data concepts that have been formally specified and established for use with the US national ITS domain. </li></ul><ul><li>Attributes are: </li></ul><ul><ul><ul><li>Class Name </li></ul></ul></ul><ul><ul><ul><li>Classification Scheme Name </li></ul></ul></ul><ul><ul><ul><li>Classification Scheme Version </li></ul></ul></ul><ul><ul><ul><li>Data Concept Type </li></ul></ul></ul><ul><ul><ul><li>Relationship Type </li></ul></ul></ul><ul><ul><ul><li>Registration Status </li></ul></ul></ul><ul><ul><ul><li>Representation Class Term </li></ul></ul></ul><ul><ul><ul><li>Submitter Organization Name </li></ul></ul></ul><ul><ul><ul><li>Steward Organization Name </li></ul></ul></ul><ul><ul><ul><li>User </li></ul></ul></ul>
  17. 17. Open GIS Consortium <ul><li>Timely, accurate geospatial information and geoprocessing services - easily accessible and capable of being shared across federal, state, and local jurisdictions and multiple security levels - are fundamental to Critical Infrastructure Protection. </li></ul><ul><li>Homeland Security will be seriously hampered without the real-time ability to quickly visualize patterns of activity and understand the multi-layered, location-based context of emergency situations </li></ul>
  18. 19. ITS Architecture <ul><li>National (USDOT/FHWA/FTA) </li></ul><ul><li>Statewide (NYSDOT) </li></ul><ul><li>Regional (NYSDOT/MTA/TRANSCOM) </li></ul>
  19. 20. “ Sausage Diagram”- Describes ITS Subsystems
  20. 23. Traffic Management Subsystem Interfaces
  21. 24. Context Diagram of Archived Data Management Subsystem Interfaces
  22. 25. Context Diagram of Archived Data Management Terminators
  23. 27. Traffic Management Data Dictionary (TMDD) – AASHTO/FHWA/ITE <ul><li>Data dictionaries work in conjunction with at least two sets of standards to provide effective data communications interchange between users. </li></ul><ul><li>TMDD Standards include message sets to handle individual information exchanges on specific topics. </li></ul><ul><li>In a loose sense, message sets are the sentences where Data Elements (DEs) are the individual words. </li></ul><ul><li>The additional required set of standards provides for the actual communications protocols. </li></ul><ul><li>These standards describe how the messages are encoded for transmission and then transmitted and received by the other party. </li></ul>
  24. 28. Archived Data User Services ADUS <ul><li>The specifications for travel monitoring data are necessary because these data are the most common type of ITS-generated data currently available and they have the widest range of use in post-hoc applications. </li></ul>
  25. 29. The Federal emphasis on ADUS is meant to: <ul><li>Create a single stream data management system </li></ul><ul><li>Foster data integration across different ITS sources and organizations </li></ul><ul><li>Address the institutional and technical issues in creating a functional ADUS </li></ul>
  26. 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.
  27. 32. Methodology <ul><li>Create prototype </li></ul><ul><li>Survey Transportation researchers and other professionals using an intercept survey at TRB in 2004 </li></ul><ul><li>Follow up with additional surveys if necessary </li></ul><ul><li>Post-process surveys using chi-square statistic for analysis </li></ul>
  28. 33. Methodology <ul><li>Interviewer will determine if the practitioner’s responsibilities are appropriate, and fill out the first portion of the survey tool (Name, info, etc.) </li></ul><ul><li>Practitioner will be asked to view brief introductory slide show </li></ul><ul><li>Practitioner will then be asked to view the prototype data base </li></ul><ul><li>Practitioner will then complete the remainder of the survey. </li></ul>
  29. 34. Concept Mapping for ITS
  30. 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.
  31. 39. Methodology <ul><li>Analysis of the data will yield a wealth of information and may reveal trends and biases in many areas. </li></ul><ul><li>Some post survey processing will be necessary to develop the expected cell counts, basing that expectation on other prototypical databases developed to help with data archiving, mining, sharing and improvements in data accuracy. </li></ul><ul><li>A minimum number of 300 complete surveys is desired, and is a reasonable goal, which will insure a statistically significant number of observations, with post-hoc surveys likely to be conducted to verify trends and anomalies. </li></ul>
  32. 40. Test results will provide information on (examples): <ul><li>Number of years Practitioner has been in the field of Transportation. </li></ul><ul><li>Practitioner’s typical role in their agency. </li></ul><ul><li>Number of dollars typically allocated to data archiving in a typical year. </li></ul><ul><li>Pre-existing data archiving and mining system, from practitioner’s description. </li></ul><ul><li>Population of the geographic area served by the Agency that the Practitioner is affiliated with. </li></ul>
  33. 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
  34. 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
  35. 43. Analysis expected cell count (observed cell count – expected cell count) 2 X 2 = <ul><li>Rejection region: </li></ul><ul><ul><li>dependent upon the expected values </li></ul></ul><ul><ul><li>cell counts must be at least 5 </li></ul></ul><ul><ul><li>K -1=2, 2df </li></ul></ul>X 2 = 125.9 (250-185) 2 _____________ 250 (25-40) 2 _____________ 25 (25-75) 2 _____________ 25 X 2 = + +
  36. 45. Conclusion <ul><li>Contribution to National efforts to standardize, simplify and promote data sharing between stakeholders </li></ul><ul><li>Contribution to information science in working through a sound methodology for open data sharing, regardless of source or destination of the data </li></ul>

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