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The July 2004 Report - Traffic Congestion and Reliability: Linking Solutions to Problems is still available for viewing.

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final report

Traffic Congestion and Reliability:
Trends and Advanced Strategies for Congestion Mitigation

prepared for

Federal Highway Administration

prepared by

Cambridge Systematics, Inc.
100 Cambridge Park Drive, Suite 400
Cambridge, Massachusetts 02140


Texas Transportation Institute


September 1, 2005

Table of Contents

Executive Summary
What is Congestion?
The Importance of Travel Time Reliability
How Do We Measure Travel Time Reliability?
Measuring Reliability
What Value Does Providing Reliable Travel Times Have?
Congestion and Reliability Trends
Congestion Is Getting Worse
Travel Reliability Is Also Getting Worse
Strategies to Reduce Congestion and Improve Reliability – Focus on Operations
Promising Operations Strategies on the Horizon
Next Steps: Building the Foundation for Effective Transportation Operations
Coordination Between Planning and Operations
Sharing Data Effectively: Using Operations Data for Improved Operations
Marking Progress Through Performance Measurement
1.0 Introduction
2.0 The Nature of Traffic Congestion and Reliability: Causes, How They Are Measured, and Why They Matter
2.1 What is Congestion?
2.2 Causes of Congestion and Unreliable Travel
2.2.1 Background: The Seven Sources of Congestion
2.2.2 How the Seven Sources Cause Congestion
2.2.3 The Reliability of Travel Time and Why It Matters
2.2.4 How Travelers, Operators, and Planners View Reliability
2.3 Tracking Congestion
2.3.1 Why Monitor Congestion?
2.3.2 Congestion Performance Measures
2.3.3 Methods Used to Develop Congestion Performance Measures
2.4 Congestion's Consequences
3.0 Recent Trends in Congestion
3.1 Estimating the Sources of Congestion
3.2 What Has Been Happening to Congestion Nationally?
3.2.1 What the Roadway-Based Data Are Telling Us
3.2.2 What the Survey Data Are Telling Us
3.3 A Closer Look at Congestion Trends
3.3.1 Cities with Detailed Traffic Monitoring Systems
3.3.2 The Nation's Major Traffic Bottlenecks
3.3.3 Expected Congestion in the Future: Not Just for Metropolitan Areas Anymore
4.0 Congestion Strategies: What Works?
4.1 The Toolbox for Congestion Relief: What Can We Do about Traffic Congestion?
4.2 Examples of Recent Efforts To Address Congestion
4.3 Promising Congestion Relief Strategies on the Horizon
5.0 Building the Foundation for Effective Transportation Operations
5.1 Using Data Effectively: Archived Operations Data for Improved Operations
5.1.1 Marking Progress Through Performance Measurement
5.1.2 Reusing Operations Data for Performance Measurement
5.2 Linking Planning and Operations
5.2.1 "Planning for Operations"
5.2.2 Developing Regional Collaborations to Foster Operations
6.0 Concluding Thoughts: Where Do We Go from Here?
6.1 How Can Everyone Pitch in Against Congestion?
6.2 Operations-Related Congestion Mitigation Activities at the FHWA
A. Technologies for Advanced Traffic Monitoring
A.1 Data Sources

List of Tables

Table ES.1 Reliability Statistics, Atlanta, Georgia (2000-2003)
Table 2.1 Factors Contributing to Extreme Congestion
Table 2.2 Example Congestion Performance Metrics
Table 3.1 Most Freeway Corridors in Seattle, Washington and Atlanta, Georgia Have Experienced a Growth in Congestion and Unreliable Travel.
Table 3.2 The Worst Physical Bottlenecks in the United States
Table 4.1 The Effectiveness of CHART's Traffic incident Management on Average Incident Duration
Table 4.2 Total Direct Benefits to Maryland Highway Users in Year 2002
Table 6.1 Selected FHWA Operations Congestion Mitigation Resources

List of Figures

Figure ES.1 Congestion Has Grown Substantially in U.S. Cities over the Past 20 Years
Figure ES.2 The Sources of Congestion
Figure ES.3 Weekday Travel Times
Figure ES.4 Distribution of Travel Times, State Route 520 Seattle, Eastbound, 4:00-7:00 p.m. Weekdays (11.5 Miles Long)
Figure ES.5 Weekday Peak-Period Congestion Has Grown in Several Ways in the Past 20 Years in Our Largest Cities
Figure 2.1 The Number and Duration of Incidents Varies Greatly from Day to Day
Figure 2.2 Traffic Levels Vary Substantially over the Course of a Week
Figure 2.3 Anatomy of Congestion
Figure 2.4 Relationship of Incident and Bottleneck Delay to Traffic Intensity
Figure 2.5 Distribution of Travel Times, State Route 520 Seattle, Eastbound, 4:00-7:00 p.m. Weekdays (11.5 Miles Long)
Figure 2.6 Weekday Travel Times
Figure 2.7 Is It a Good Day or Bad Day for Commuting: Comparing Current Travel Times to Historical Conditions
Figure 2.8 Congestion and Unreliable Travel Have Increased on I-75 Southbound in Central Atlanta, Georgia
Figure 2.9 Measuring Travel Time Is the Basis for Congestion Measures
Figure 2.10 Example of the Newly Designed Urban Congestion Report Used by FHWA to Track Monthly Changes in Congestion
Figure 2.11 Interstate 5 Average Travel Rate for Trucks: 10-Mile Segments
Figure 3.1 Results of Two Modeling Studies to Estimate Congestion by Source
Figure 3.2 The Sources of Congestion
Figure 3.3 Congestion Has Grown Substantially in U.S. Cities over the Past 20 Years
Figure 3.4 Peak-Period Congestion Trends by Urban Area Population Group
Figure 3.5 Weekday Peak-Period Congestion Has Grown in Several Ways in the Past 20 Years in Our Largest Cities
Figure 3.6 How Many Rush Hours in a Day?
Figure 3.7 Travel Time Reliability Illustration
Figure 3.8 The Average Commute Travel Time in a Privately Owned Vehicle (POV) Has Increased
Figure 3.9 The Average Commute Trip Length in a POV Has Increased
Figure 3.10 After Showing Modest Improvement in the 1990s, Average Commute Speeds Have Begun to Worsen
Figure 3.11 Trips in San Francisco Are Now Taking Longer to Complete
Figure 3.12 Daily and Monthly Trends in Congestion
Figure 3.13 Annual Trends in Congestion
Figure 3.14 Corridor Statistics for Seattle and Atlanta
Figure 3.15 Interchange Capacity Bottlenecks on Freeways Used as Urban Truck Corridors
Figure 3.16 Congested Highways (1998)
Figure 3.17 Potentially Congested Highways (2020)
Figure 3.18 Vehicle-Miles of Travel on Major Rural and Urban Roads Increased Between 1990 to 2002
Figure 4.1 A Variety of Strategies, When Used in Combination, Can Effectively Deal with Congestion
Figure 4.2 Springfield Interchange Congestion Management Plan Budget Breakdown
Figure 4.3 Road Commission of Oakland County Road Condition Web Site Map
Figure 4.4 SCATS Hardware Structure
Figure 4.5 Milwaukee Area Freeway Web Site Map
Figure 4.6 CHART's Traffic incident Mapping Interface
Figure 4.7 Sample Drilldown Map from CHART
Figure 4.8 Reduction in Delays Due to CHART Operations
Figure 4.9 Houston's SAFEclear Program Has Improved Incident Response Times
Figure 4.10 Cost of Wasted Fuel and Time on Katy Freeway under Alternative Construction Scenarios
Figure 5.1 Reusing Real-Time Operations Data, Used Initially in Control Strategies, Is an Effective Way of Monitoring Performance and Providing Data for Future Assessments
Figure 5.2 Percentage of Maryland Lane-Miles with Average Annual Volumes Below Congested Levels
Figure 5.3 Example of Seattle's Portrayal of Historic Congestion Patterns
Figure 5.4 Example Minnesota Dashboard
Figure 5.5 Planning for Operations

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