Scoping and Conducting Data-Driven 21st Century Transportation System Analyses
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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
Table of Contents
Background: A Brief History of Surface Transportation System Analyses for Operations
Objectives and Value of Transportation Analysis for Operations
Traditional and Emerging Capabilities
Capturing System Dynamics
The 21st Century Analytical Project Scoping Process
Needs-Driven, Data-Driven Analytics
Navigating This Document
Module 1. Characterizing System Dynamics and Diagnosing Problems
Envisioning Target System Performance
Creating System Congestion, Reliability, and Safety Profiles
Preliminary Analytics Project Statements
Prioritizing Analytic Problem Statements
Moving to Module 2: The Analytic Problem Statement
Module 2. Data-Driven Transportation Analysis Project Scoping
Need for and Characteristics of an Analytic Project Scoping Plan
Defining and Scoping the Project
Selecting the Appropriate Analysis Tool Type
Selecting Performance Measures
Analyzing Data Requirements
Refining Alternatives and Mitigation Strategies
Cost, Schedule and Responsibility for the Analysis
The Scoping Tool
Moving to Module 3: Summarizing the Project Scoping Plan
Module 3. Preparing Data to Conduct a Transportation Analysis
Assessing Data Gaps
Making Data Analytics-Ready
Moving to Module 4: Operational Conditions Summary
Module 4. Conducting and Documenting Transportation Analyses
The Analysis Plan
Stakeholder Involvement and Review
Re-Examination of Problem Identification and Diagnosis
Experimental Design for Analysis of Different Operational Conditions
Experimental and Control Cases (with and without)
Analysis Tool Calibration
Document Analysis Results
Data, Analysis Tools, and Model Sustainability
List of Figures
Figure 1. Diagram. 21st Century analytic project scoping process.
Figure 2. Diagram. 21st Century analytic project scoping process.
Figure 3. Flowchart. Interactions among analytic projects with different time scales.
Figure 4. Diagram. System characterization and diagnostics within the 21st Century analytic project scoping process.
Figure 5. Graphic. Definition of transportation performance management.
Figure 6. Illustration. Transportation system products and negative associated by-products.
Figure 7. Illustration. Example of a measuring system product.
Figure 8. Map. Washington State Department of Transportation congestion maps.
Figure 9. Snapshot. Kansas City Scout traffic dashboard.
Figure 10. Chart. Reliability measures are related to average congestion measures.
Figure 11. Chart. Roadway travel time distributions.
Figure 12. Snapshot. Top multi-vehicle incident locations by route.
Figure 13. Snapshot. Maryland average annual incident frequency during morning peak hours by location.
Figure 14. Chart. National capital region congestion report.
Figure 15. Flowchart. Leveraging direct (data-driven) and indirect (non-data) observations.
Figure 16. Flowchart. Cross-validating observations to create a candidate hypothesis.
Figure 17. Illustration. Example of reconciling perception and observation.
Figure 18. Chart. Risk/reward assessment chart.
Figure 19. Chart. Risk-reward assessment of candidate analytical projects.
Figure 20. Diagram. Project Scoping within the 21st Century analytic project scoping process.
Figure 21. Flowchart. Federal Highway Administration Traffic Analysis Toolbox: Overview of analysis factors to be considered in selecting appropriate analysis tools.
Figure 22. Screenshot. Analysis scoping tool—summary of example user inputs.
Figure 23. Screenshot. Analysis scoping tool—example output.
Figure 24. Diagram. Data preparation within the 21st Century analytic project scoping process.
Figure 25. Chart. Annual average corridor travel time profile on
Seattle I-405 south bound in 2012.
Figure 26. Chart. One-day travel time profile with an associated incident.
Figure 27. Illustration. Example probe data from snow plow trucks.
Figure 28. Illustration. A fully connected vehicle environment.
Figure 29. Screenshot. Daily detail views for displaying predicted daily activities and trips from cell phone.
Figure 30. Screenshot. Speed data on two lanes.
Figure 31. Screenshot. All values are missing.
Figure 32. Screenshot. Few values are missing.
Figure 33. Chart. Natural Variation in Transportation System.
Figure 34. Diagram. Various operational conditions.
Figure 35. Diagram. Projects execution and documentation within the 21st Century analytic project scoping process.
Figure 36. Chart. Sample comparison of project alternatives using schematic drawing.
Figure 37. Snapshot. Evaluation of potential transit service.
Figure 38. Snapshot. Summary comparison of transit competitiveness.
Figure 39. Map. Change in speeds comparison.
Figure 40. Map. Comparison of change in delays.
Figure 41. Snapshot. Illustration of an alternative.
Figure 42. Heat map. Speed diagram for one analysis scenario—I-15 simulation.
Figure 43. Snapshot. Accident rates in space and in time.
Figure 44. Snapshot. Accident rates by location.
Figure 45. Chart. Comparison of alternatives across performance measures.
Figure 46. Chart. Summary of monetary benefits across performance measures.
Figure 47. Chart. Sample measures of effectiveness summary table, Minnesota Department of Transportation/ Federal Highway Administration Traffic Analysis Toolbox Volume IV.
Figure 48. Chart. Delay Comparison between two scenarios: In vehicle-hours; I-210 simulation.
Figure 49. Heat map. Comparison of freeway speeds between alternatives.
List of Tables
Table 1. Steps in the generation of an analytic problem statement.
Table 2. Example analytic problem statement.
Table 3. Example high-level allocation of analysis responsibilities.
Table 4. Project scoping summary elements.
Table 5a. An operational conditions summary template - data summary.
Table 5b. An operational conditions summary template - attributes.
Table 6. Project results summary elements.