Data & Information Integration
Framework for Highway Projects
Mid-Continent Transportation
Symposium
Asregedew Woldesenbet
David H. Jeong (Ph.D.)
Michael P. Lewis (Ph.D., P.E.)
August 15, 2013
Outline
Research Question
Lessons Learned
Methodology
Evolution
Integration Framework
Case Study
Gap Analysis
Conclusion/Future Work
Research Question

Is data currently being collected provides the
information needed for decision-making?
◦ Minimal recognition or interest in using these data
◦ Lack of in-house resources and capabilities to analyze data
◦ Insufficient data for any meaningful analysis
◦ Nonstandard /non-digital data format
◦ Poorly defined procedures/mechanism
Lessons Learned
◦ Strategic decisions supported by statistically reliable
information
 Credit card industry
 Retail industry
 Healthcare industry
◦ Big Data
◦ System/Tools
 KM tools and KDD approaches
◦ DM, AI, DSS, ML, BI
 Management philosophies
◦ BPR, TQM, SCM, CE, LC
 Database System
◦ Ontology frameworks, cloud computing
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Generations of Data & Information
Management: Transportation Industry
Knowledge
Portal
3rd Generation
2nd Generation
Database/
Datawarehouse
1st Generation
File Cabinet,
PC
System
Evolution of Data and Information
Integration for Highway Agencies
3rd Generation
1st Generation
Various DATABASES
- Data Collection Efforts
Data Collection
- Manual/Paper-Based
Approach
- Expert Judgment
System
- File Cabinet e.g. Contract Documents
- PC e.g. Cost Data
- Database e.g. Road Inventory
- Other Databases
2nd Generation
Active Information & Knowledge
Extract
Data Collection
- Semi-Automated/Automated
Approach
- Statistical Tools
- Artificial Intelligence
System
- Project Management System
- Database e.g. SiteManager
- Data Warehouse (DW)
Integrated Data &
Information Framework ion
to Support Decision Making
Data Collection
- Automated
- Standard Data Collection Procedure
Approach
- Pattern Recognition
- Knowledge Discovery in Database (KDD)
- Data Mining (DM)
System
- Ontology Based Knowledge Management System
- Big Data Analytics Algorithm
- Knowledge Portal e.g. cloud-based system
Data & Information Integration
Input
Processor
DM4
Output
X
DM3
D1
D2
DM2
I2
..…
..…
Dn
In
X
DM1
DM3
D1
..…
D3
DM2
DM1
I1
X
D2
DMn
X
X
X
I1
I2
X
X
X
D3
X
D4
X
I3
Row Form
X
X
Column Form
Context Graph
Input/Output Matrix
Element Form
Data & Information Integration
Framework
Planning
Phase
Decision
Design
Phase
Bidding
Phase
DMA
Data
D11
D12
I11
I12 ….. I1N
D13
D14
DATABASE I
….. D1n
I21
D21
I22
D22
…..
I2n
D23
DATABASE II
…..
Legend :
DMN
Im1
D2n
Operation
Phase
Active Path
Inactive Path
Non-Existing Path
….....
DMB
Information
Construction
Phase
Dm1
…………….
Im2
Dm2
Im3 …..
Dm3
Imn
…..
DATABASE N
Three-Tiered Hierarchical Framework
Dmn
Case Study

Daily Work Reports (DWR)

Preconstruction Cost Data

Pavement Condition Data
Case Study
Division/
Source
Database
Type of Data
Roadway Inventory
System
Planning/
Research
Grip lite/
Highway
Inventory
Traffic
Sub-Elements
Collection Method
Functional Class, Right of Way, Route
Classification, Terrain Area Type, right-ofway, railroad crossing, etc.
Average Annual Daily Traffic (AADT),
Manual / Semisignals, lightings, traffic control, crash
Automated
statistic, etc.
Bridge Inventory
Bridge span, width, length, load limit,
inspection reports, etc.
Preconstruction
In-house
Spreadsheets
Preliminary
Engineering Data
Engineering hours, number of sheets, etc. Manual
Construction
Division
SiteManager
Construction Data
Daily work report, reported quantity,
material, change order contractor
payment etc.
Manual
Pavement History
Pavement surface type, thickness,
composition, etc.
In-house - Automated
Distress Data
Longitudinal Cracking, Transverse
Consultant - Roadware
Cracking, Patching, Spalling, Fatigue, etc.
Friction Data
Average Roughness, Ride, Average Rut
etc.
In-house
Other (structural)
Deflectometer (FWD), ESAL
In-house Roadrater
Pavement
Management
Pavement
management
System (PMS)
Current Data Utilization
DWR Info
Contractor
Description
Data Type
No
Use
I1
I2
D6
ID 000001-100000
Last and first name
xx/xx/xxxx
Temp. oF
Temp. oF
Sunny, windy, cloudy, etc
Numeric : Ordinal
Character : Nominal
Numeric : Ordinal
Numeric : Interval
Numeric : Interval
Character : Nominal
X
X
X
I3
X
X
X
X
X
X
D7
Sunny, windy, cloudy, etc.
Character : Nominal
X
X
Work Suspended Time
D8
Time AM/PM
Numeric : Ordinal
X
X
Work Resumed Time
Humidity
Precipitation
Contractor
D9
D10
D11
Time AM/PM
X
X
D12
Name
Numeric : Ordinal
Character : Nominal
X
X
Subcontractor
D13
Name
Character : Nominal
X
X
Supervisor
D14
Foreman, superintendent, etc.
Character : Nominal
Personnel
Supervisor Hourly
work
Personnel Hourly work
Supervisor Number
D15
Laborer, concrete finisher, etc.
Character : Nominal
X
X
Number of Hours
Numeric : Interval
X
X
Number of Hours
Count
Numeric : Interval
Numeric : Interval
X
X
X
X
Contractor ID
Inspector Name
Date
Low Temperature
High Temperature
AM Condition
D1
PM Condition
D2
D3
D4
D5
D16
D17
D18
-
X
X
X
Percentage
Completion
Data
Reporting
Data Attributes
Dispute
Resolution
Type of Data
Contractor
Payment
Current Use
I4
Ideal Data, Information &
Decision-Making Framework
Three-Tiered Framework
Planning Phase
Decision
DM1
DM2
DM3
Resource
Allocation
Determine
Contract Time
Production Rate
Databases
Contractor
Type
D1
Inspector
D2
Date ……….
D3
DM4
Maintenance Roadway Design
Information
Data
Construction Phase
Design Phase
Precipitation
D11
DM5
DM6
DM7
Bridge Design
Traffic & Safety
Design
Cost Tracking
I5
Project
Type
D12
Accident Analysis
………. Distance
D29
Sitemanager
Accidents ……….
D30
I6
Contractor Payment
Supervisor
Remark
D36
Project
Management
I1
Prime
……….
Contr. Work
D37
Performance Measure
Type of
Day
D39
Construction Data
Gap Analysis
Missing data (D1 - D3)
- Humidity, precipitation, etc.
Unstructured Data (D1 - D3)
- Remarks ((D33 –D41)
Not used data (D1 - D3)
- Accidents (D30), delays (D31), etc.
Current Data
Ideal Data
Current Information
Ideal Information
Missing information (I5 - I9)
- Production rate
- Accident analysis
- As-built information, etc.
Current Decisions
Ideal Decisions
Missing decisions (DM1 - DM9)
- Resource Allocation
- Contract time determination
- Maintenance, etc.
Gap Analysis
Criteria
Gap
Need for data analyst or data scientist
Staff
Need for responsible party in data collection, information
generation and decision-making
Need for decision-maker requirement, identifying
Function
characteristics and use
Need for data and information to reach the user or decisionTime
maker in a timely manner
Availability
Missing data and information
Need for change of textual or linguistic data types, lack of
Format/Structure
standard
Division having standalone units to match only particular
Individuality
needs
Need for appropriate tools and technology to extract
Technology
information
Conclusion

Summary
◦ DWR are often utilized in reporting and preparation of legal
disputes.
◦ Reported quantity and work item are the primary data that
are utilized in contractor payments and tracking project
progress.
◦ More than 35% of the DWR data are linguistic in nature.
◦

Conclusion
◦ Lack of skilled data analysts and experts to analyze data
◦ Lack of well-developed requirement analysis and
performance measures.
◦ Focus of specific divisions or business processes to promote
own division’s need rather than develop integrated system
Conclusion

Data, Information & Decision-making Guideline
Requirement Analysis
Identify Key Decisions
Identify Data, Information &
Knowledge (DIK)
Strategic & Network Level
Decisions
Quality Function Deployment
Program & Project Selection
Level Decisions
Identify Key Performance
Indicators
Project Level Decisions
Identify Database & Decision
Tools
Evaluation/Assessment
Check Availability of Data,
Information & Knowledge
Data Generation Scheme
Assess Current level of Use &
Quality
Define Data, Information &
Knowledge
Develop Data, Information,
Decision-Making Path
Develop New Module/
Database
Perform Cost/Benefit Analysis
Data Process/Manipulation
Cleanup Data
Convert Data into Information
Apply Appropriate Tool or
Decision Support System
Conclusion

Contribution
◦ Ability to show types of data that should be collected and
potential information & knowledge generation
◦ A general guide to highway agencies in the development of
active utilization of currently existing databases.
◦ Help develop new data collection, information & knowledge
generation plan to support key decisions
Future Study
◦ Emphasize in developing an enterprise wide ontology-based
framework
◦ Application of big data analytics to justify the return on
investment for the data collection efforts and effectively
utilize the increasing amount of data.