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An ITS Data Archive PrototypeAn ITS Data Archive PrototypeUsing UML, XML, and OO Using UML, XML, and OO

Database DesignDatabase Design

A Dissertation ProposalSubmitted to Committee Chairperson:

Dr. Catherine Lawson and Committee members:

Dr. Jagdish Gangolly and Dr. Peter Duchessi

by Maggie Cusack

AbstractAbstract

Knowledge and information form the basis of informed travel behavior.

The foundation of individual choice in transportation decision-making is based on availability and robustness of traveler information.

Historical information or learned information combined with new information contributes to the individual decision-maker’s mode, route and travel time choices.

Information support systems known as Intelligent Transportation Systems (ITS) have evolved along with technological advances to divulge information to travelers.

AbstractAbstract

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.

Applications of ITS can also be used to guide non-motorized travel.

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.

AbstractAbstract

The data archiving and data mining efforts that support these ITS technologies is the subject of this investigation.

This work contributes to the advance of database organization, design and transferability between software systems, operating systems and network environments.

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.

AbstractAbstract

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:

The Unified Modeling Language (UML); eXtensible Mark-up Language (XML); and Object Oriented (OO) database design.

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.

Background to the ProblemBackground to the Problem

Influential Trends:

– 1. an increase in the volumes of data being collected, stored and managed, and

– 2. an increase in the critical need to manage the transportation system to accommodate the increase in overall system volumes.

i.e.: NYS Bridge Authority Traveli.e.: NYS Bridge Authority Travel

The Future of the Highway Network?The Future of the Highway Network?NY Metropolitan Region: NY Metropolitan Region:

15,000 additional trucks a day in 199815,000 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


data collection and communication technologies have improved significantly in the past few years

a common language for transferring information inside and between transportation agencies and other stakeholders is under development but still does not yet exist

a few efforts have been made to exploit available technologies for communication

very few successfully span the dirge of information types


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.

This is based on the precept that drivers can change their behavior if the information they receive is timely, accurate and informative.

Scope of this EndeavorScope of this Endeavor

This paper proposes that using– standard database design– simple object transfer concepts and rules– along with current data mining concepts

can greatly enhance the state of the art in ITS for:– data collection– data storage (data archiving)– manipulation– retrieval, and – ultimately better use of data (data mining) in the decision

making processes.

Existing ITS Data StandardsExisting ITS Data Standardsand Ontologiesand Ontologies

National ITS Architecture National Transportation Communications for ITS

Protocol - NTCIP Archived Data User Services – ADUS ITS Data Registry Transportation Management Data Dictionary -

TMDD Unified Network Transportation System –

UNETRANS Open GIS Consortium - OGC

IEEE IEEE Data Definition SWGData Definition SWG Many light-duty motor vehicles, and increasing numbers of heavy

commercial vehicles, are equipped with some form of event data recorder.

These systems, which are designed and produced by individual motor vehicle manufacturers and component suppliers, are diverse in function, and proprietary in nature.

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.

The recommendations of this SWG will supplement the work of the output SWG by providing a basis for defining output protocols.

IEEE 1489 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

Existing sets of IEEE transportation Existing sets of IEEE transportation data definition standards for ITSdata definition standards for ITS

– 1455-1999 IEEE Standard for Message Sets for Vehicle/Roadside Communications

– 1488-2000 IEEE Trial-Use Standard for Message Set Template for Intelligent Transportation Systems

– 1489 Standard for Data Dictionaries for Intelligent Transportation Systems

– 1489 Standard for Data Dictionaries for Intelligent Transportation Systems RR

– 1489-1999 Standard for Data Dictionaries for Intelligent Transportation Systems

– 1512-2000 IEEE Standard for Common Incident Management Message Sets for use by Emergency Management Centers

– P1512.1 Standard for Traffic Incident Management Message Sets for Use by Emergency Management Centers

– P1512.2 Standard for Public Safety Incident Management Message Sets for Use by Emergency Management Centers

– P1512.3 Standard for Hazardous Material Incident Management Message Sets for Use by Emergency Management Centers

– P1512a Standard for Emergency Management Data Dictionary

– 1512b Amendment for Implementing Foreign Data Elements Found In Standard for Common Incident Management Message Sets

For Use By Emergency Management Centers, IEEE 1512-2000 – 1512b

Amendment for Data Elements Found In Standard for Common Incident Management Message Sets For Use By Emergency Management Centers, IEEE 1512- 2000

IEEE ITS Data RegistryIEEE ITS Data Registry

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.

Attributes are:

Class NameClassification Scheme NameClassification Scheme VersionData Concept TypeRelationship TypeRegistration StatusRepresentation Class TermSubmitter Organization NameSteward Organization NameUser

Open GIS ConsortiumOpen GIS Consortium

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.

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

ITS ArchitectureITS Architecture

National (USDOT/FHWA/FTA)Statewide (NYSDOT)Regional (NYSDOT/MTA/TRANSCOM)

“Sausage Diagram”- Describes ITS Subsystems

Traffic Management Subsystem Interfaces

ADMSArchived Data Management Subsystem

TRMSTransit Management

TMSTraffic Management

TASToll Administration

PMSParking Management

MCMSMaintenance and Construction

Management

ISPInformation Service Provider

EMMSEmissions Management

EMEmergency Management

CVASCommercial Vehicle Administration

RSRoadway Subsystem

archive requestsarchive status

transit archive dataarchive requests

archive statustraffic archive data


toll archive data


parking archive dataarchive requests

archive statusmaint and constr archive data


traveler archive dataarchive requests

archive statusemissions archive data


emergency archive data


commercial vehicle archive datadata collection and monitoring control

roadside archive data

Fixed Point to Fixed Point

Context Diagram of Archived Data Management Subsystem Interfaces

ADMSArchived Data Management Subsystem

Archived Data User Systems Weather Service Surface Transportation WeatherService

Other Data Sources Multimodal Transportation ServiceProvider

Intermodal Freight Depot

Asset Management

Other Archives

Map Update Provider

Government Reporting Systems

Financial Institution

Archived Data Administrator

archive analysis resultsarchive request confirmation

archived data productsarchive analysis requests

archived data product requestsarchive requests

archive statusweather information


transportation weather information


other data source archive dataarchive requests

archive statusmultimodal archive data


intermodal freight archive dataarchive requests

archive statusasset archive data

archive coordination

map update requestmap updates

government reporting system datagovernment reporting data receipt

payment requesttransaction status

archive management dataarchive management requests

Fixed Point to Fixed PointHuman

Context Diagram of Archived Data Management Terminators

Traffic Management Data Dictionary Traffic Management Data Dictionary (TMDD) – AASHTO/FHWA/ITE(TMDD) – AASHTO/FHWA/ITE

Data dictionaries work in conjunction with at least two sets of standards to provide effective data communications interchange between users.

TMDD Standards include message sets to handle individual information exchanges on specific topics.

In a loose sense, message sets are the sentences where Data Elements (DEs) are the individual words.

The additional required set of standards provides for the actual communications protocols.

These standards describe how the messages are encoded for transmission and then transmitted and received by the other party.

Archived Data User ServicesArchived Data User ServicesADUSADUS

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.

The Federal emphasis on The Federal emphasis on ADUS is meant to:ADUS is meant to:

Create a single stream data management system Foster data integration across different ITS

sources and organizations Address the institutional and technical issues in

creating a functional ADUS

HypothesesHypothesesThe survey data will be used to test (reject) this null hypothesis:

HO1 : The prototype is no different than existing methods

for data archiving.

There are several other hypothesis to be tested with this data:

HO2 : The prototype is no different than current methods

for data mining.HO3 : The prototype is no different than current methods

for data sharing.HO4: The prototype is no different than current methods to assure data accuracy.

MethodologyMethodology

Create prototypeSurvey Transportation researchers and other

professionals using an intercept survey at TRB in 2004

Follow up with additional surveys if necessary

Post-process surveys using chi-square statistic for analysis


Interviewer will determine if the practitioner’s responsibilities are appropriate, and fill out the first portion of the survey tool (Name, info, etc.)

Practitioner will be asked to view brief introductory slide show

Practitioner will then be asked to view the prototype data base

Practitioner will then complete the remainder of the survey.

Concept Mapping for ITSConcept Mapping for ITS

Practitioner Profile Post Secondary Education. [ ] None [ ] 2-4 Years [ ] 4+ Years Number of Years in Current [ ] 0-4 Years [ ] 5-10 Years [ ] 10+ YearsPosition or Related Position. How Would You Classify [ ] Policy or [ ] Mid Level [ ] Staff orYour 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.


Analysis of the data will yield a wealth of information and may reveal trends and biases in many areas.

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.

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.

Test results will provide Test results will provide information on (examples):information on (examples):

Number of years Practitioner has been in the field of Transportation.

Practitioner’s typical role in their agency. Number of dollars typically allocated to data

archiving in a typical year. Pre-existing data archiving and mining system,

from practitioner’s description. Population of the geographic area served by the

Agency that the Practitioner is affiliated with.

Typical Chi-square analysisTypical Chi-square analysis

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

Expected values.

Typical Chi-square analysisTypical Chi-square analysis

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

(SAMPLE) Actual values.

AnalysisAnalysis

expected cell count(observed cell count – expected cell count)2

X2 =

(250-185)2

_____________

250

(25-40)2

_____________

25

(25-75)2

_____________

25X2 = + +

Rejection region: •dependent upon the expected values•cell counts must be at least 5•K-1=2, 2df

X2 = 125.9

ConclusionConclusion

Contribution to National efforts to standardize, simplify and promote data sharing between stakeholders

Contribution to information science in working through a sound methodology for open data sharing, regardless of source or destination of the data

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