State of The Practice on Data Access, Sharing, and Integration

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FOREWORD

The effective analysis of transportation operations strategies for planning and investment decisions requires an accurate and complete understanding of transportation system performance obtained by integrating multiple sources of transportation data from multiple stakeholders. This report examines the state of the practice in data sharing and integration, specifically (1) current data sharing and integration practices among State and local agencies, (2) example data environments, (3) technical integration formats, and (4) business rules for integration and sharing.

The review was conducted at the outset of a Federal Highway Administration project to develop a prototype framework for sharing planning and operations data between State and local transportation agencies from multiple sources within a region. The results of the review were used to inform the development of the framework. Transportation operators, planners, and data professionals can use this report to enhance their data sharing and integration efforts by building on the experiences and effective practices of other agencies that are documented in this report.

Brian P. Cronin
Director, Office of Operations
Research and Development

Technical Report Documentation Page

SI* (Modern Metric) Conversion Factors

TABLE OF CONTENTS

CHAPTER 1. INTRODUCTION

• SUMMARY OF OVERARCHING PROJECT
• OBJECTIVES OF THE STATE-OF-THE-PRACTICE REVIEW
• RELATIONSHIP TO USE OF DATA IN PLANNING FOR OPERATIONS STATE-OF-THE-PRACTICE REVIEW
• ORGANIZATION OF STATE-OF-THE-PRACTICE REPORT

CHAPTER 2. CURRENT INTEGRATION PRACTICES

• DENVER, COLORADO
  • Effective Practices
  • Success Factors
  • Gaps and Lessons Learned
  • Applicability to the VDA Framework
    
    
• LAS VEGAS, NEVADA
  • Effective Practices
  • Success Factors
  • Gaps and Lessons Learned
  • Applicability to the VDA Framework
    
    
• SEATTLE, WASHINGTON
  • Effective Practices
  • Success Factors
  • Gaps and Lessons Learned
  • Applicability to the VDA Framework
    
    
• PITTSBURGH, PENNSYLVANIA
  • Effective Practices
  • Success Factors
  • Gaps and Lessons Learned
  • Applicability to the VDA Framework
    
    
• SAN DIEGO, CALIFORNIA
  • Effective Practices
  • Success Factors
  • Gaps and Lessons Learned
  • Applicability to the VDA Framework
    
    
• SAN FRANCISCO BAY AREA, CALIFORNIA
  • Effective Practices
  • Success Factors
  • Gaps and Lessons Learned
  • Applicability to the VDA Framework

CHAPTER 3. EXAMPLE DATA ENVIRONMENTS

• UNIVERSITY OF MARYLAND’S REGIONAL INTEGRATED TRANSPORTATION INFORMATION SYSTEM
  • Effective Practices
  • Success Factors
  • Gaps and Lessons Learned
  • Applicability to the VDA Framework
    
    
• CALTRANS’ PEMS
  • Effective Practices
  • Success Factors
  • Gaps and Lessons Learned
  • Applicability to the VDA Framework
    
    
• PORTLAND STATE UNIVERSITY’S PORTAL
  • Effective Practices
  • Success Factors
  • Gaps and Challenges
  • Applicability to the VDA Framework
    
    
• UNIVERSITY OF WASHINGTON’S TRAFFIC DATA ACQUISITION AND DISTRIBUTION DATABASE
  • Effective Practices
  • Success Factors
  • Gaps and Lessons Learned
  • Applicability to the VDA Framework
    
    
• CLARUS
  • Effective Practices
  • Success Factors
  • Applicability to the VDA Framework
    
    
• FHWA DATA CAPTURE AND MANAGEMENT PROGRAM TEST DATASETS
  • Effective Practices
  • Success Factors
  • Gaps and Lessons Learned
  • Applicability to the VDA Framework
    
    
• UTAH DEPARTMENT OF TRANSPORTATION’S UPLAN
  • Effective Practices
  • Success Factors
  • Gaps and Lessons Learned
  • Applicability to the VDA Framework
    
    
• FLORIDA DEPARTMENT OF TRANSPORTATION’S ITS DATA CAPTURE AND PERFORMANCE MANAGEMENT TOOL
  • Effective Practices
  • Success Factors
  • Gaps and Lessons Learned
  • Applicability to the VDA Framework

CHAPTER 4. TECHNICAL INTEGRATION FORMATS

• GEOGRAPHIC REFERENCING AND RESOLUTION
• TEMPORAL RESOLUTION
• QUALITY CONTROL AND ASSURANCE
• METADATA
  • FGDC Metadata Standards
  • ISO 19115 Metadata Content Standard
  • Federal Enterprise Architecture Data Reference Model
  • Minnesota Department of Transportation Example
  • California Example
  • Metadata Guidelines for the Research Data Exchange⁽⁶⁷⁾
    
    
• MIGRATION STRATEGIES
  • Simple Version Migration
  • Format Standardization
  • Typed Object Model Conversion
  • Object Interchange Format
    
    
• FILE AND DATA FORMATS
  • Factors to Consider When Choosing Formats
    
    
• DATA RESOLUTION
• ACTIVE VERSUS PASSIVE ARCHIVING
• VERSION CONTROL
• DATA TRANSFORMATION
• APPLICABILITY TO THE VDA FRAMEWORK

CHAPTER 5. BUSINESS RULES FOR INTEGRATING AND SHARING

• DATA MANAGEMENT
  • Real-Time Data Capture and Management State of the Practice Assessment and Innovations Scan
  • Oak Ridge National Laboratory: Best Practices for Preparing Environmental Datasets to Share and Archive
  • Massachusetts Institute of Technology Libraries
  • Policy Analysis and Recommendations for the Data Capture and Management Program: Implementation of Open Data Policies and System Policies for the Research Data Exchange and Data Environments
  • State-of-the-Practice and Lessons Learned on Implementing Open Data and Open Source Policies
  • Railinc
    
    
• DATA GOVERNANCE
  • NCHRP 666, Target Setting Methods and Data Management to Support Performance-Based Resource Allocation by Transportation Agencies
  • National Information Exchange Model
  • Identification of Critical Policy Issues for the DCM and DMA Programs
  • Oregon Department of Transportation
  • Virginia Department of Transportation
    
    
• DATA SHARING AGREEMENTS

REFERENCES

LIST OF FIGURES

Figure 1. Table. DRCOG RCTO goals, objectives, initiatives, and performance measures
Figure 2. Screenshot. CDOT CoTrip Web site
Figure 3. Screenshot. Your CDOT Dollar Web page
Figure 4. Diagram. Recommended CDOT performance measures data system configuration
Figure 5. Screenshot. CDOT OTIS
Figure 6. Screenshot. CDOT CoTrip XML data feeds
Figure 7. Screenshot. FAST PMMS
Figure 8. Diagram. Data architecture for PSRC’s geospatial database
Figure 9. Screenshot. SPC Congestion Management Corridor Web site
Figure 10. Diagram. ICMS context diagram
Figure 11. Screenshot. SF Bay Area 511 Web site
Figure 12. Screenshot. 511 SF Bay available data feeds
Figure 13. Diagram. TravInfo open messaging service architecture and link status information
Figure 14. Diagram. Data feeds to RITIS, which standardizes the data and presents them to various user groups
Figure 15. Diagram. Some of the tools available through RITIS for real-time and historic data visualization, analysis, and sharing
Figure 16. Diagram. RITIS data architecture
Figure 17. Screenshot. RITIS demonstration snapshot of an incident timeline
Figure 18. Screenshot. PeMS welcome screen
Figure 19. Screenshot. PeMS main screen
Figure 20. Screenshot. PeMS corridor module Alameda I-80 daily animation
Figure 21. Screenshot. PeMS departure time series on I-15 north southbound
Figure 22. Screenshot. PeMS lane requirement chart modeling on I-110 north in district 7
Figure 23. Screenshot. PeMS data clearinghouse
Figure 24. Screenshot. PORTAL snapshot
Figure 25. Diagram. TDAD architecture
Figure 26. Screenshot. Clarus user interface
Figure 27. Screenshot. Clarus data on RITIS regional map
Figure 28. Screenshot. Graphic interpretation of weather data in RITIS
Figure 29. Screenshot. UPlan user interface
Figure 30. Diagram. High-level design of ITSDCAP
Figure 31. Screenshot. Interface for performance measure estimation
Figure 32. Diagram. Actively managed VDA Framework, which involves several processes to make data fully usable

LIST OF TABLES

Table 1. Roles and responsibilities for DRCOG’s RCTO data warehouse and performance reporting initiative
Table 2. ICMS interfacing systems and owner agencies
Table 3. TDAD data elements
Table 4. TDAD data fusion techniques
Table 5. Types of data consumers and applications
Table 6. Data quality targets
Table 7. ISO 19115 core metadata elements
Table 8. MnDOT metadata element schedule
Table 9. Example of matching temporal and spatial resolutions of data to applications

ACRONYMS AND ABBREVIATIONS