Enhancing Active Transportation and Demand Management (ATDM) with Advanced and Emerging Technologies and Data Sources

Printable version [PDF 5 MB]
You may need the Adobe® Reader® to view the PDFs on this page.
Contact Information: Operations Feedback at OperationsFeedback@dot.gov

U.S. Department of Transportation
Federal Highway Administration
Office of Operations
1200 New Jersey Avenue, SE
Washington, DC 20590
ops.fhwa.dot.gov

March 2020
FHWA-HOP-19-010

---

Table of Contents

[ Notice and Quality Assurance Statement ] [ Technical Report Documentation Page ] | [ List of Acronyms ]

Executive Summary
Chapter 1. Introduction
	1.1 Document Purpose
	1.2 Objectives
	1.3 Target Audience
	1.4 Document Organization
	1.5 Active Transportation and Demand Management (ATDM) Overview
Chapter 2. Emerging Technologies and Data Sources
	2.1 Identification of Emerging Technologies and Data Sources
	2.2 Description of Emerging Technologies and Data Sources
	2.3 Categories for Emerging Data Sources
	2.4 Categories for Emerging Technologies
Chapter 3. ATDM Applications
	3.1 ATDM Approaches
Chapter 4. Planning and Organizational Considerations
	4.1 Organizational Capability
	4.2 Planning for Modified ATDM Operations
	4.3 Setting Objectives and Performance Measures
	4.4 Analysis, Modeling and Simulation
	4.5 Programming and Budgeting
Chapter 5. Design and Deployment Elements and Methods
	5.1 Design Elements
	5.2 Data Sources
	5.3 System Platforms
	5.4 Infrastructure
	5.5 Technology Testing
	5.6 Public Outreach
Chapter 6. Operations and Maintenance Consideration
	6.1 Routine Operations and Maintenance Issues
	6.2 Future Proofing
Chapter 7. Case Studies
Appendix: References

List of Figures

Figure 1. Photograph. The Lodge Freeway (Michigan).

Figure 2. Photograph. Interstate-66 advanced traffic management including dynamic shoulder use.

Figure 3. Illustration. Dynamic management across the entire trip chain.

Figure 4. Illustration. Active management cycle.

Figure 5. Illustration. Connected traveler's data exchange for incident management.

Figure 6. Illustration. Connected vehicle incident zone warning concept.

Figure 7. Illustration. Connected infrastructure architecture.

Figure 8. Illustration. Characteristics of data.

Figure 9. Illustration. Cloud computing ecosystem.

Figure 10. Illustration. Decision support systems position in data science.

Figure 11. Illustration. Applications of sensing technologies.

Figure 12. Infographic. Advanced transportation demand management today and tomorrow.

Figure 13. Diagram. Recurring congestion use case (traditional).

Figure 14. Diagram. Recurring congestion use cae with new data and technology.

Figure 15. Diagram. Incident management use case (traditional).

Figure 16. Diagram. Incident management use case (new technology and data sources).

Figure 17. Diagram. Airport parking use case (traditional methods).

Figure 18. Diagram. Airport parking use case (new technologies and data sources).

Figure 19. Diagram. Steps for actively managing operations.

Figure 20. Diagram. Levels of organization maturity.

Figure 21. Diagram. Object-driven, performance-based approach to planning for operations.

Figure 22. Illustration. The five "V's" of big data.

Figure 23. Screenshot. Linkage of connected and automated vehicle goals and operations and management issues.

Figure 24. Map. Dynamic network analysis and real-time traffic management for the Philadelphia Metropolitan Area.

Figure 25. Screenshot. Transit passenger wait time.

Figure 26. Diagram. Enhancement of active parking management using technologies and data.

Figure 27. Screenshot. Smartphone parking application.

Figure 28. Screenshot. San Diego Association of Governments project area map of Interstate 15.

Figure 29. Photograph. Radio frequency identification scanners installed on Stanford University campus for the CAPRI project.

Figure 30. Chart. Number of CAPRI registrants over time.

List of Tables

Table 1. Active traffic management solutions.

Table 2. Active demand management solutions.

Table 3. Active parking management solutions.

Table 4. Sample list of emerging technologies and data sources

Table 5. Analysis, Modeling, and Simulation Tools and application attributes.

Table 6. A big data process model: acquisition, marshaling, analysis, and action.

Table 7. Modules for each challenge area.