July 19, 2004 www.camsys.com

Traffic Congestion and Reliability: 

Linking Solutions to Problems 

prepared for

Federal Highway Administration 

prepared by 

Cambridge Systematics, Inc. 

with 

Texas Transportation Institute 

final

report 
final report 

Traffic Congestion and Reliability: 

Linking Solutions to Problems 

prepared for 

Federal Highway Administration 

prepared by 

Cambridge Systematics, Inc. 

100 Cambridge Park Drive, Suite 400 

Cambridge, Massachusetts 02140 

with 

Texas Transportation Institute 

date 

July 19, 2004 
Traffic Congestion and Reliability: Linking Solutions to Problems 

Cambridge Systematics, Inc. i 

7091.600 

Table of Contents 

Executive Summary.................................................................................................... 1

Trends in National Congestion.......................................................................... 1

The Sources of Congestion and Unreliable Travel........................................... 5

What Can We Do About Traffic Congestion? ................................................... 10

Next Steps.......................................................................................................... 13

Detailed Definitions.......................................................................................... 16

1.0 Introduction.....................................................................................................1-1

2.0 Background to the Congestion Problem......................................................2-1

2.1 The Sources of Congestion .....................................................................2-1

2.2 The Growing Importance of Travel Time Reliability ...........................2-5

3.0 National State of Congestion: Congestion by the Numbers and 

Why It Matters ................................................................................................3-1

3.1 Trends In National Congestion..............................................................3-1

3.2 Congestion and Everyday Life............................................................. 3-13

4.0 How Can We Deal with Congestion?...........................................................4-1

4.1 The Toolbox for Congestion Relief ........................................................4-1

4.2 Using Vision-Oriented Planning And Regional Collaboration to 

address congestion..................................................................................4-6

4.3 The Diversity in Future Congestion Solutions......................................4-8

4.4 The Positive Effects of Operational and Combined Strategies on 

Congestion...............................................................................................4-9

4.5 Summary ............................................................................................... 4-19

5.0 Next Steps........................................................................................................5-1

5.1 How Can Everyone Pitch In Against Congestion?...............................5-1

5.2 Activities at the Federal Highway Administration ..............................5-3
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Appendix A. Data Sources....................................................................................A-1 

Appendix B. State of the Practice: 

Performance Measurement for Congestion and Operations.................... B-1 

B.1 Trends in the Use of Congestion and Operations Performance 

Measurement by Transportation Agencies...........................................B-1 

B.2 Challenges Ahead ...................................................................................B-4 

B.3 What Have We Learned?......................................................................B-10 

Appendix C. Delay and Reliability Measures ................................................... C-1 

Appendix D. City-Level Trends in Reliability ..................................................D-1 
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Table of Contents 

Executive Summary ........................................................................................................ 1

Trends in National Congestion............................................................................. 1

The Sources of Congestion and Unreliable Travel............................................. 5

What Can We Do About Traffic Congestion?...................................................... 10

Next Steps .............................................................................................................. 13

Detailed Definitions.............................................................................................. 16

1.0 Introduction .........................................................................................................1-1

2.0 Background to the Congestion Problem ........................................................2-1

2.1 The Sources of Congestion ........................................................................2-1

2.2 The Growing Importance of Travel Time Reliability ............................2-5

3.0 National State of Congestion: Congestion by the Numbers and 

Why It Matters.....................................................................................................3-1

3.1 Trends In National Congestion.................................................................3-1

3.2 Congestion and Everyday Life ...............................................................3-13

4.0 How Can We Deal with Congestion? .............................................................4-1

4.1 The Toolbox for Congestion Relief...........................................................4-1

4.2 Using Vision-Oriented Planning And Regional Collaboration to 

address congestion .....................................................................................4-6

4.3 The Diversity in Future Congestion Solutions .......................................4-8

4.4 The Positive Effects of Operational and Combined Strategies on 

Congestion...................................................................................................4-9

4.5 Summary....................................................................................................4-19

5.0 Next Steps.............................................................................................................5-1

5.1 How Can Everyone Pitch In Against Congestion? ................................5-1

5.2 Activities at the Federal Highway Administration................................5-3
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Appendix A. Data Sources .......................................................................................A-1 

Appendix B. State of the Practice: 

Performance Measurement for Congestion and Operations..................... B-1 

B.1 Trends in the Use of Congestion and Operations Performance 

Measurement by Transportation Agencies............................................ B-1 

B.2 Challenges Ahead...................................................................................... B-4 

B.3 What Have We Learned?........................................................................ B-10 

Appendix C. Delay and Reliability Measures......................................................C-1 

Appendix D. City-Level Trends in Reliability.....................................................D-1 
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List of Tables 

Table 2.1 The Worst Physical Bottlenecks in the United States 2002..................2-2

Table 2.2 Effect of Treating Unreliable Travel Times on I-75 in 

Central Atlanta Hypothetical.....................................................................2-9

Table 3.1 Forecasts of Passenger Transportation System Activity......................3-7

Table 3.2 Forecasts of Freight Transportation System Activity...........................3-7

Table 3.3 Growth in Interstate Highway Traffic 1992-2002................................3-11

Table 4.1 Impacts of Fully Deploying Operational Strategies in Three 

Urban Areas .............................................................................................4-10

Table 4.2 Comparison of Benefits and Costs of Fully Deploying 

Operational Strategies in Three Urban Areas .....................................4-11

Table 4.3 2001 Urban Mobility Improvement Techniques 

Existing Operations...................................................................................4-17

Table 4.4 2001 Mobility Improvement Techniques 

Full Deployment of Operations..................................................................4-18

Table 4.5 2003 Urban Mobility Report Added Roads 2001 Data .......................4-19

Table 5.1 Selected FHWA Congestion Relief Resources.......................................5-5

Table B.1 Potential Challenges to Accurately Assessing Congestion ................ B-5 

Table B.2 Comparison of the Relative Advantages and Limitations of 

Modeling Versus Measurement ............................................................. B-8 
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List of Figures 

Figure ES.1 Peak-Period Congestion (Travel Time Index) Trends by U.S. 

Population Group.................................................................................... 2

Figure ES.2 Weekday Peak-Period Congestion Has Grown in Several 

Ways in the Past 20 Years in Our Largest Cities ................................. 3

Figure ES.3 Travel Time Reliability Illustration ....................................................... 4

Figure ES.4 The Sources of Congestion National Summary ..................................... 6

Figure ES.5 Percentage of Highway Segments with over 10,000 Trucks 

Per Day Comparison of 1998 to 2020 ....................................................... 8

Figure 2.1 The Sources of Congestion National Summary ..................................2-4

Figure 2.2 Actual and Improved Peak-Period Travel Times on 

I-75 Southbound Central Atlanta, 2002...............................................2-7

Figure 2.3 Relationship Between Congestion Level and Reliability ..............2-10

Figure 3.1 Peak-Period Congestion (Travel Time Index) Trends by U.S. 

Population Group.................................................................................3-3

Figure 3.2 Weekday Peak-Period Congestion Has Grown in Several 

Ways in the Past 20 Years in Our Largest Cities................................3-4

Figure 3.3 How Many Rush Hours in a Day? .....................................................3-4

Figure 3.4 In-Vehicle Travel Times for Peak-Hour Trips Have Grown 

Substantially in Large Cities................................................................3-5

Figure 3.5 Travel Time Reliability Illustration ....................................................3-5

Figure 3.6 Projected Growth in Urban Freeway Congestion 

(Bottleneck-Related) under Different VMT Growth Rates, 

2002-2010 (Top 78 Metro Areas)............................................................3-8

Figure 3.7 Travel Time and Reliability Trends 2001-2002 .................................3-9

Figure 3.8 Percentage of Highway Segments with over 10,000 Trucks 

Per Day, Comparison of 1998 to 2020..............................................3-10

Figure 3.9 2020 Congestion Forecasts, No Trucks ............................................3-12

Figure 3.10 2020 Congestion Forecasts, With Trucks.........................................3-12

Figure 3.11 Effect of Trucks on Delay at the 50 Worst Urban Bottlenecks.....3-13

Figure 3.12 Travel Times in Central Atlanta, I-75 Southbound I-85 to I-20 ....3-14
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Figure 3.13 Central Atlanta, I-75 Southbound Weekdays between 

4:00 and 7:00 p.m..................................................................................3-15

Figure 3.14 Variations in Congestion by Time-of-Day ......................................3-16

Figure 3.15 Average Travel Times to Work 1980-2000 ......................................3-17

Figure 3.16 Economic Effects of Transportation.................................................3-19

Figure 4.1 A Variety of Strategies, When Used in Combination, Can 

Effectively Deal with Congestion.......................................................4-2

Figure 4.2 Proportion of Benefits Value of Full Operations Deployment 

in Tucson..............................................................................................4-11

Figure 4.3 Comparison of Potential Annual Costs in Tucson, Cincinnati, 

and Seattle ...........................................................................................4-12

Figure 4.4 Observed Benefits of Ramp Meters in the Twin Cities .................4-13

Figure 4.5 Reported Changes in Traffic Conditions after the Shutdown.....4-14

Figure 4.6 The T-Rex Project in Denver, Colorado...........................................4-16

Figure B.1 General Taxonomy of Mobility-Based Performance Measures .... B-4 

Figure B.2 Modeling Versus Measurement – When Should They 

Be Used?................................................................................................ B-9 

Figure D.1 Congestion Trends on Minneapolis Freeways ................................D-2 

Figure D.2 Congestion Trends on Atlanta Freeways.........................................D-2 

Figure D.3 Congestion Trends on Los Angeles Freeways.................................D-3 

Figure D.4 Congestion Trends on Seattle Freeways ..........................................D-3 

Figure D.5 Reliability Trends in Four Cities, 2000-2002 ....................................D-4 
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Executive Summary 

The Traffic Congestion and Reliability: Linking Solutions to Problems Report provides 

a snapshot of congestion in the United States by summarizing recent trends in 

congestion, highlighting the role of unreliable travel times in the effects of congestion, and describing efforts to curb congestion. In particular, the Report

develops a framework for understanding the various sources of congestion, the 

ways to address congestion by targeting these sources, and performance measures for monitoring trends in congestion. 

Much of the Report is devoted to measuring recent trends in congestion. One of 

the key principles that the Federal Highway Administration (FHWA) has promoted is that the metrics used to track congestion should be based on the travel 

time experienced by users of the highway system. While the transportation profession has used many other types of metrics to measure congestion (such as 

“level of service”), travel time is a more direct measure of how congestion affects 

users. Travel time is understood by a wide variety of audiences – both technical 

and nontechnical – as a way to describe the performance of the highway system. 

All of the congestion metrics used in the Report are based on this concept. 

The different aspects of congestion are discussed using a variety of data sources, 

with perhaps the newest aspect being the role of reliability in the congestion 

problem. The variation in travel times is now understood as a separate component of public and business sector frustration with congestion problems. Average travel times have increased and the Report discusses ways to reduce them. 

But the day-to-day variations in travel conditions pose their own challenges and 

the problem requires a different set of solution strategies. 

TRENDS IN NATIONAL CONGESTION

Is congestion getting worse? Yes. There are several statistics that point to worsening congestion levels. Congestion extends to more time of the day, more 

roads, affects more of the travel, and creates more extra travel time than in the 

past. And congestion levels have risen in cities of all sizes since 1982, indicating 

that even the smaller areas are not able to keep pace with rising demand. 
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Figure ES.1 illustrates trends for 75 major urban areas tracked in the Texas 

Transportation Institute’s Annual Mobility Report.1 Congestion levels have risen 

to levels experienced by the next largest population group every 10 years – in 

2001, cities between 500,000 and one million people experienced the congestion 

of cities between one and three million in 1992. 

Figure ES.1 Peak-Period Congestion (Travel Time Index) Trends by U.S. 

Population Group 

1.00

1.10

1.20

1.30

1.40

1.50

1.60

1982 1986 1990 1994 1998 2002

Very Large Large Medium Small

Population Group Travel Time Index

Source: Reference (1). The Travel Time Index is a measure of the total amount of congestion. It is 

the ratio of the weekday peak-period travel time to the travel time under ideal conditions. A Travel 

Time Index value of 1.3 indicates that peak-period travel takes 30 percent longer than under ideal 

conditions. Population groups are: Very Large ( greater than three million); Large (one to three 

million); Medium (500 thousand to one million); and Small (less than 500 thousand). 

Congestion has clearly grown. Congestion used to mean it took longer to get 

to/from work in the “rush hour.” But congestion now affects more trips, more 

hours of the day and more of the transportation system. Figure ES.2 shows the 

growth in several key dimensions of the congestion problem in cities of more 

than one million persons. 

 

1 Schrank, D. and Lomax, T., 2003 Annual Urban Mobility Report, Texas Transportation 

Institute. This methodology measures congestion conditions on individual highway 

segments using roadway-based data. Alternate ways of measuring congestion exist, 

such as monitoring the travel times of entire trips with household surveys. One such 

survey, the National Household Travel Survey (NHTS) has been conducted 

periodically since 1969. Recent data from the NHTS suggest that commute times have 

not increased as fast compared to roadway-based congestion data, such as is used by 

the Urban Mobility Report. This may be due to people changing their residential and 

employment locations. The net result is that for individual travelers, congestion is 

probably not getting as worse as fast – highway segments may be more congested but 

the ability to move around the metropolitan landscape provides a “cushion” for some 

individuals. 
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• The average weekday peak-period trip takes almost 40 percent longer than 

the same trip in the middle of the day, compared to 13 percent longer in 1982. 

• Sixty-seven percent of the peak-period travel is congested compared to 

33 percent in 1982. Travelers in 75 urban areas spent 3.5 billion hours stuck 

in traffic in 2001, up from 0.72 billion in 1982. 

Figure ES.2 Weekday Peak-Period Congestion Has Grown in Several Ways 

in the Past 20 Years in Our Largest Cities 

Duration4.5 hours per day

Extent

33% of travel

39% average delay

Intensity

Intensity

13% average delay

Congestion 

In 2001

Extent

67% of travel

7 hours per day

Duration

Congestion 

In 1982

Source: Analysis of data used in 2003 Annual Urban Mobility Report, Texas Transportation Institute. 

• Fifty-nine percent of the major road system is congested during peak hours 

compared to 34 percent in 1982. 

• The number of hours of the day when weekday travelers might encounter 

congestion has grown from 4.5 hours to 7 hours. 

These are just the average conditions. Many cities have a few places where any 

daylight hour might see “stop-and-go” traffic. Weekend traffic delays have 

become a problem in recreational areas, near major shopping centers or sports 

arenas, and on some constrained roadways (for example, bridges). 

Travel time reliability is also a growing problem. The variation in travel time 

from day to day is a significant characteristic of the congestion problem 

(Figure ES.3). The extra travel time and amount of the day and system affected 

by travel delays is not the same every day. It affects not only commute trips, but 

any trip during the peak travel periods, and is a significant concern of large and 

small businesses in all parts of the economy. Very detailed data from some 

urban freeways allow agencies to identify the extra travel time that must be 

budgeted – or buffer time – above the average travel time. The time that 
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shippers, carriers, business travelers, commuters, and households have to plan 

for is a real consequence of congestion. 

As an example of how travel time reliability affects highway users, consider the 

following (Figure ES.3). 

Figure ES.3 Travel Time Reliability Illustration 

1982 Average

In 1982, if your commute was 

20 minutes at midday, it took 

23 minutes in the peak and 

you would spend an extra 15 

hours on the road each year.

By 2001, that 20 

minute off-peak trip 

took 28 minutes 

in the peak.

And if you have an

important meeting, the 

reliability problems mean that 

you should allow 40 minutes 

for the same trip.

2001 Average 2001 Planning

Source: Reference (1).

• 1982 – If your midday trip took 20 minutes, it would take you 23 minutes in 

the peak. Although no reliability statistics exist from that long ago, analysis 

of recent data suggest that you would have had to add an additional nine 

minutes to that trip to guarantee on-time arrival at your destination; a total of 

32 minutes would be planned for that trip. 

• 2001 – By 2001, that 20-minute free-flow trip took 28 minutes. 

• 2001 (Planning Time) – And if on-time arrival was important you should 

allow 40 minutes for that trip. 

The future holds more of the same. Population and employment growth in 

America’s large cities are expected to continue rising by around two percent each 

year, resulting in longer periods of congestion on more of the transportation 

system. Forecasts of population and economic activity – strong determinants of 

transportation activity – along with forecasts of system-level transportation 

activity indicate that compared to year 20002: 

• By 2025, the U.S. population will grow by 26 percent; 

• By 2025, the Gross National Product will double; 

 

2 Sources: (1) U.S. Department of Transportation, Bureau of Transportation Statistics, 

http://www.eia.doe.gov and (2) FHWA Freight Analysis Framework, 

http://www.ops.fhwa.dot.gov/freight/freight_news/FAF/talkingfreight_faf.htm. 
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• By 2025, passenger-miles (all modes, including highway, air, and transit) will 

grow by 72 percent; and 

• By 2020, intercity truck tonnage will grow by 75 percent. 

THE SOURCES OF CONGESTION 

AND UNRELIABLE TRAVEL

Congestion is a lot more complex than simply “too many vehicles trying to use 

the road at the same time,” although that is certainly a major part of the problem. 

Congestion results from the interaction of many different factors – or sources of 

congestion. Congestion has several root causes that can be broken down into 

two main categories: 

1. Too much traffic for the available physical capacity to handle – Just like a 

pipe carrying water supply or the electrical grid, there are only so many 

vehicles that can be moved on a roadway for a given time or so many transit 

patrons that can be accommodated in a given number of buses or trains. 

Transportation engineers refer to this as the physical capacity of the highway 

system. Physical bottlenecks are locations where the physical capacity is 

restricted, with flows from upstream sections (with higher capacities) being 

funneled into smaller downstream segments. This is roughly the same as a 

storm pipe that can carry only so much water – during heavy rains the excess 

water floods the streets and houses behind the pipe. However, the situation 

is even worse for traffic. Once traffic flow breaks down to stop-and-go conditions, capacity is actually reduced – fewer cars can get through the bottleneck because of the extra turbulence. Bottlenecks can be very specific 

chokepoints in the system, such as a poorly functioning freeway-to-freeway 

interchange, or an entire highway corridor where a “system” of bottlenecks 

exists, such as a closely spaced series of interchanges with local streets. 

Physical capacity can be reduced by the addition of “intentional” bottlenecks, 

such as traffic signals and toll booths. Bottlenecks can also exist on long 

upgrades and can be created by “surges” in traffic, as experienced around 

resort areas. 

2. Traffic-influencing events – In addition to the physical capacity, external 

events can have a major effect on traffic flow. These include traffic incidents 

such as crashes and vehicle breakdowns; work zones; bad weather; special 

events; and poorly timed traffic signals. When these events occur, their main 

impact is to “steal” physical capacity from the roadway. Events also may 

cause changes in traffic demand by causing travelers to rethink their trips 

(e.g., snow and other types of severe weather). 

The level of congestion on a roadway is determined by the interaction of physical 

capacity with events that are taking place at a given time. For example, the effect 

of a traffic incident depends on how much physical capacity is present. Consider 

a traffic crash that blocks a single lane on a freeway. That incident has a much 
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greater impact on traffic flow if only two normal lanes of travel are present than 

if three lanes are present. Therefore, strategies that improve the physical capacity of bottlenecks also lessen the impacts of roadway events such as traffic incidents, weather, and work zones.

Only recently has the transportation profession started to think of congestion in 

these terms. Yet it is critical to do so because strategies must be tailored to 

address each of the sources of congestion, and they can vary significantly from 

one highway to another. Nationally, a composite estimate of how much each of 

these sources contribute to total congestion is depicted in Figure ES.4.3

Figure ES.4 The Sources of Congestion 

National Summary 

Bottlenecks (40%)

Traffic Incidents (25%)

Work Zones (10%)

Bad Weather (15%)

Poor Signal Timing (5%)

Special Events/Other (5%)

What Causes Travel Times to be Unreliable? The interaction of all the sources 

of congestion produce unreliable travel times. Travel time reliability can be 

defined in terms of how travel times vary over time (e.g., hour-to-hour, day-today). The event-related sources (e.g., traffic incidents, weather, and work zones) 

that contribute to total congestion also conspire to produce unreliable travel 

times, since events and demand volumes vary day to day. The problem is worse 

when events are added on top of existing capacity-related congestion. When 

traffic flow has already broken down to stop-and-go conditions, any additional 

disturbance causes a large increase in congestion. 

What Are the Benefits of Making Travel Times More Reliable? If it is possible 

to reduce the impact of these events on travel, a double benefit is realized: not 

 

3 http://www.ops.fhwa.dot.gov/aboutus/opstory.htm; these estimates are a composite 

of many past and ongoing congestion research studies and are rough approximations. 
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only are conditions made more “reliable” (that is, less variable), but overall delay 

is reduced as well. This is because extreme events, especially in combination, 

lead to high congestion. Making improvements in both the congestion level and 

reliability is significant for a number of reasons: 

• Reducing total congestion saves time and fuel, and leads to decreased vehicle 

emissions; 

• Reducing congestion at international border crossings leads to lower transportation costs and benefits the national economy as a whole. Further, 

reducing congestion on U.S. highways for freight moving between Canada 

and Mexico fosters international trade. In essence, congestion on U.S. highways can be thought of as an international problem as well as a national 

one; 

• Improving reliability leads to more predictable and consistent travel, something that all travelers seek: they do not have to budget as much extra time 

in order to arrive on time at their destinations. This is particularly important 

for truckers and shippers because many activities (e.g., manufacturing, sales) 

are now closely timed to the arrival of shipments. Many types of personal 

travel – such as getting to business appointments and child care pickup on 

time – are also sensitive to unreliable travel times; and 

• Treating three major components of unreliable travel – traffic incidents, bad 

weather, and work zones – also leads to safer highways. By reducing the 

duration of these events, we are reducing how long travelers are exposed to 

less safe conditions. 

What Value Does Providing Reliable Travel Times Have? Commuters as well 

as freight carriers and shippers are all concerned with travel time reliability. 

Variations in travel time can be highly frustrating and are valued highly by both 

groups. Previous research4 indicates that commuters value the variable component of their travel time between one and six times as much as average travel 

time. Adoption of just-in-time (JIT) manufacturing processes has made a reliable 

travel time almost as important as an uncongested trip. Significant variations in 

travel time will decrease the benefits that come from lower inventory space and 

the use of efficient transportation networks as “the new warehouse.” Therefore, 

in both the passenger and freight realms, evidence suggests that travel time reliability is valued at a significant “premium” by users. 

What is Freight’s Role in Congestion? Demand for freight transportation in the 

United States, which is expected to grow substantially over the next 15 years 

(Figure ES.5), is a major contributing factor to congestion. The expected growth 

 

4 Cohen, Harry, and Southworth, Frank, On the Measurement and Valuation of Travel Time 

Variability Due to Incidents on Freeways, Journal of Transportation Statistics, Volume 2, 

Number 2, December 1999, http://www.bts.gov/jts/V2N2/vol2_n2_toc.html. 
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in truck travel is being driven by economic and population growth. The most 

striking growth is expected to be on rural Interstate highways, indicating the 

potential for congestion to spread outside of metropolitan areas. Since 1992, traffic has grown substantially on rural highways and at a faster pace than on metropolitan highways. National data shows that between 1992 and 2002, traffic on 

rural Interstates increased 36 percent compared with an increase of 25 percent on 

urban Interstates. Further analysis shows that traffic volume per lane (a measure 

of traffic density) increased by 35 percent on rural Interstates compared with 

21 percent on urban Interstates. 

Figure ES.5 Percentage of Highway Segments with over 10,000 Trucks 

Per Day 

Comparison of 1998 to 2020

0%

10%

20%

30%

40%

50%

60%

70%

Urban- IS Urban-NHS Rural- IS Rural - NHS

1998

2020

Source: Analysis of data from FHWA’s Freight Analysis Framework. 

What Are the Costs of Congestion? Congestion has real costs for all travelers, 

including truckers (both long-haul and local pickup and delivery), household 

and business service providers (such as plumbers, computer technicians, police, 

and ambulance services), and personal travel (such as commuters, vacationers, 

and shoppers). Congestion causes more fuel to be used and more emissions to 

be produced. The extra time spent in congestion causes service providers to 

make fewer calls per day, leading to higher prices for consumers; this is particularly important for emergency medical, fire, and police services which may be 

unnecessarily delayed from attending to medical, crime, and disaster situations. 

Companies with production schedules timed to take advantage of trucks delivering components to an assembly line as they are needed must instead plan for 

items to arrive early. This consumes space and inventory, expending resources 

that could otherwise be spent on productive activity. For personal travelers, 
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congestion “steals” time that could be put to better use in the workplace or for 

social or recreational purposes. 

The congestion costs to freight interests are significant. Freight transportation 

has gone through many changes over the past 20 years as it has adapted to 

changes in business practices. Within this new operating environment, freight 

operations and productivity have been optimized to work closely with other 

aspects of business activity. Deregulation has resulted in excess capacity being 

eliminated from the highway and rail freight systems. Intermodal services and 

facilities have revolutionized international trade. Ports and airports have seen 

services and demand grow rapidly. Freight services are now more efficient and 

in many cases lower in cost (in constant dollars) than in previous decades. But 

the elimination of excess capacity has resulted in systems with less redundancy 

and less ability to withstand shocks or disruptions. Congestion is growing on 

many key freight segments of the transportation system, and congestion can 

drastically reduce the productivity of the overall freight network. The delay 

caused by congestion could vastly increase the costs of those freight movements 

that are today managed to exacting schedules. 

Time is literally money for shippers and trucking interests. A direct linkage 

exists between transportation investment, travel conditions (congestion and reliability) and economic productivity. For trucking, two key trends identified 

above will have a substantial impact on the total cost of moving freight: 

1. As congestion spreads into the midday period, which is the peak travel 

period for trucks, more direct costs will be incurred; and 

2. Reliability – For trucks, the ability to hit delivery windows predictably will 

decrease and will add even more costs as firms struggle to optimize delivery 

schedules. This is especially a problem for truckers who must meet “just-intime” delivery schedules set by shippers, manufacturers, and retailers. 

All of this adds up to a staggering amount of costs imposed on travelers by congestion. The Texas Transportation Institute estimates that in 75 of the largest 

U.S. cities in 2001, $69.5 billion dollars are wasted in time and fuel costs.5 (The 

costs are a composite of automobile and truck travel costs in urban areas.) The 

time value costs for trucks are conservative – they include only the cost of truck 

operating time, primarily the cost of drivers’ wages and equipment. The value of 

the cargo and the response of firms to transportation costs is not included, yet 

recent work suggests these costs can be significant. These costs include: 

 

5 Schrank, D. and Lomax, T., 2003 Annual Urban Mobility Report, Texas Transportation 

Institute. 
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• Foregone Investment Opportunities – Higher transportation costs due to 

congestion reduce a firm’s ability to invest in making more products, 

improve product quality, and introduce new products; and 

• Decreases in Regional Employment or Decreases in the Rate of Growth of 

Regional Income – Higher transportation costs are passed onto other sectors 

of the economy and hinder general economic efficiency.6

WHAT CAN WE DO ABOUT TRAFFIC CONGESTION? 

Transportation engineers and planners have developed a variety of strategies to 

deal with congestion – a toolbox for managing congestion. The strategies can be 

grouped as follows: 

1. Adding more capacity for highway, transit and railroads; 

2. Operating existing capacity more efficiently; and 

3. Encouraging travelers to use the system in less congestion-producing ways. 

Each of these congestion reducing strategies has a role in major cities. More 

accurately, they all have a role in some locations and corridors within major cities. Implementing the strategies involves consideration of the size and type of 

problem, funding, and public approval, environmental and social consequences. 

The decisions resulting from all these factors will be different, diverse and reflect 

local, state, and national priorities. When used in combination, however, the 

strategies can have a powerful impact on congestion growth. Also, when 

applying these strategies, agencies need to think and act regionally about solutions to congestion problems. In fact, FHWA is promoting the concept of 

regional partnerships as a means to implementing effective operations. These 

partnerships provide a platform for interagency coordination and joint delivery 

of operations-based services. 

Specifically, each of the three major categories of congestion management strategies entails the following: 

1. Adding More Capacity – Increasing the Number and Size of Highways and 

Providing More Transit and Freight Rail Service. Adding more lanes to 

existing highways and building new ones has been the traditional response 

to congestion. In some metropolitan areas, however, it has become difficult 

to undertake major highway expansions because of funding constraints, 

increased right-of-way and construction costs, social effects and environmental constraints and opposition from local and national groups. However, 

it is clear that adding new physical capacity to highways, transit systems, and 

 

6 ICF Consulting, HLB Decision Economics, and Louis Berger Group, Freight Benefit/Cost 

Study: Capturing the Full Benefits of Freight Transportation Improvements: A Non-Technical 

Review of Linkages and the Benefit/Cost Analysis Framework, May 11, 2001. 
Traffic Congestion and Reliability: Linking Solutions to Problems 

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railroads is an important strategy for alleviating congestion. This often 

means that highway designers must find creative ways to incorporate new 

designs that accommodate all stakeholders’ concerns. Since the worst highway bottlenecks tend to be major freeway interchanges, advanced design 

treatments that spread out turning movements and remove traffic volumes 

from key merge areas have been developed, often by using multilevel structures that minimize the footprint of the improvement on the surrounding 

landscape. 

Key Strategies to Address Congestion 

– Adding travel lanes on major freeways and streets (including truck 

climbing lanes on grades); 

– Adding capacity to the transit system (buses, urban rail or commuter rail 

systems); 

– Closing gaps in the street network; 

– Removing bottlenecks; 

– Overpasses or underpasses at congested intersections; 

– High-occupancy vehicle (HOV) lanes; and 

– Increasing intercity freight rail capacity to reduce truck use of highways. 

2. Operating Existing Capacity More Efficiently – Getting More Out of What 

We Have. In recent years, transportation agencies have embraced strategies 

that deal with the operation of existing highways, transit systems, and freight 

services, rather than just building new infrastructure. Collectively referred to 

as Intelligent Transportation Systems (ITS), real-time control of transportation operations involves making changes from minute to minute and take 

many forms. In addition to ITS, other Transportation System Management 

and Operations (TSM&O) strategies that improve the efficiency of the 

existing road system include minor widening projects, changing the 

operating methods or the policies that govern the use of the roadway, and 

monitoring transit vehicles in real-time. There are numerous operationsbased congestion mitigation strategies that are enhanced by the use of 

advanced technologies or ITS. 

Key Strategies to Address Congestion 

– Metering traffic onto freeways; 

– Optimizing the timing of traffic signals; 

– Faster and anticipatory responses to traffic incidents; 

– Providing travelers with information on travel conditions as well as alternative routes and modes; 

– Improved management of work zones; 
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– Identifying weather and road surface problems and rapidly targeting 

responses; 

– Providing real-time information on transit schedules and arrivals; 

– Monitoring the security of transit patrons, stations, and vehicles; 

– Anticipating and addressing special events that cause surges in traffic; 

– Better freight management, especially reducing delays at border 

crossings; 

– Reversible commuter lanes; 

– Movable median barriers to add capacity during peak periods; 

– Restricting turns at key intersections; 

– Geometric improvements to roads and intersections; 

– Converting streets to one-way operations; and 

– Access management. 

3. Encouraging Travel and Land Use Patterns that Use the System in Less 

Congestion Producing Ways – Travel Demand Management (TDM), NonAutomotive Travel Modes, and Land Use Management. Another key 

approach to the problem of congestion involves managing the demand for 

highway travel. These strategies include providing a variety of options that 

result in more people traveling in fewer vehicles, trips made during less congested times, or trips not made (at least in a physical sense). A major barrier 

to the success of demand management strategies is that they may require 

changes in traditional decisions about where, when and how to travel, live 

and work. Flexible scheduling, for example, is not possible for a large number of American shift schedule workers. Still, when considered as part of an 

overall program of transportation investments, demand management and 

non-automotive modes of travel can contribute substantially to a metropolitan area’s transportation system. 

The historical cycle of suburban growth has led to an ever increasing demand 

for travel. Suburban growth was originally fueled by downtown workers 

who moved from city centers to the urban fringe to take advantage of lower 

land prices and greater social amenities. In the past 20 years, businesses also 

have moved to the suburbs to be closer to their employees. This in turn 

allows workers to live even further away from city centers, thereby perpetuating suburban expansion. Strategies that attempt to manage and direct 

urban growth to influence these processes have been used in several metropolitan areas. The main problem with many of these strategies is that they 

can be contrary to market trends, burdening consumers with extra costs and 

dampening economic efficiency, at least in the short term. Unless a truly 

regional approach is followed – with cooperation of all jurisdictions within 

the region – and the policies are considered as part of a package of 
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development options, sprawl may simply be attracted into areas not conforming to growth policies. 

Key Strategies to Address Congestion 

– Programs that encourage transit use and ridesharing; 

– Curbside and parking management; 

– Flexible work hours; 

– Telecommuting programs; 

– Bikeways and other strategies that promote non-motorized travel; 

– Pricing fees for the use of travel lanes by the number of persons in the 

vehicle and the time of day; 

– Pricing fees for parking spaces by the number of persons in the vehicle, 

the time of day or location; 

– Land use controls or zoning; 

– Growth management restrictions such as urban growth boundaries; 

– Development policies that support transit-oriented designs for homes, 

jobsites, and shops; and 

– Incentives for high-density development, such as tax incentives. 

NEXT STEPS

Is Success Possible Against Congestion? Yes, but past successes tends to be 

localized. Multiple and systematic strategies for addressing congestion are 

required, given that demand is increasing on an already stressed highway and 

transit system. All of the strategies covered in this Report have been successfully 

implemented – the key to future progress is deploying and using them in a more 

comprehensive and aggressive manner. It also requires cooperation between 

transportation agencies, businesses, elected officials, and the public. Since we are 

all affected by congestion, it is important that we all work together to address the 

congestion problem. Here are some ways that transportation agencies, businesses, elected officials, and the public can collaborate to mitigate congestion. 

1. Take Ownership – The first step is for all parties to recognize they have a 

stake in the congestion problem. Public agencies are in the business of 

serving customers the same way that any private firm is – except that the 

customers (the public and businesses) are buying efficient and safe travel. 

The public, elected officials, and businesses are more than just consumers – 

they are shareholders too. These consumers also should examine their own 

decisions and policies to identify changes that can improve their quality of 

life while recognizing that the agencies cannot solve the problem by themselves. The ongoing transportation planning process, which has been successfully used in major metropolitan areas for the past 40 years to address 
Traffic Congestion and Reliability: Linking Solutions to Problems 

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transportation problems, provides an excellent framework for promoting 

ownership of congestion problems. A major part of the transportation planning process is establishing a Vision that outlines what the future transportation system should look like. The Vision leads to more specific statements of 

desired actions to achieve these states or characteristics. The Vision is also an 

opportunity to educate all stakeholders on the nature of congestion in your 

area and the importance of mitigating it. 

2. Identify the Congestion Problems and Opportunities – Both technical 

analyses and anecdotal information from the public are useful in identifying 

where the major congestion problems are, where they will be, and what 

causes them. The existing transportation planning process in metropolitan 

areas can be tapped as a resource for this purpose. Thoroughly analyze and 

provide realistic assessments on what can reasonably be done in each case, 

and what the expected improvements might be. FHWA supports a wealth of 

information on expected improvements from operational strategies, such as 

the ITS Benefits and Cost Database.7 The process should include considerations of: 

– Strategies – What types of treatments should be considered? 

– Coverage – How much area does the treatment cover? 

– Density – How well is congestion treated? 

– Congestion Target – What aspect of congestion is treated? 

– Effect – What is the delay reduction effect? Are there secondary effects, 

such as on safety? What are the spillover effects on other facilities and 

neighborhoods? 

3. Develop Plans, Programs, Policies, and Projects – Congestion solutions can 

take a variety of forms. Think broadly – no single tool will be highly effective 

against the congestion problem. But when used in combination – and tailored to specific circumstances – packages of congestion mitigation strategies 

can be successful. The strategies should include action elements – things we 

can accomplish in a short timeframe and at low cost. But longer-term actions 

also should be developed – consider all types pf strategies including adding 

new highway and rail capacity, improved operations, and better land use 

planning. Recognize that many transportation and community plans already 

exist and should be tapped as mechanisms for carrying out the Vision. In 

fact, acting on a list of “things we can do now” should help galvanize support 

for congestion mitigation over the long term. 

4. Plan, Manage, and Operate the Transportation System Proactively and 

Regionally. Focus on addressing system reliability by targeting capital and 

 

7 http://www.mitretek.org/its/benecost/BC_Update_2003/index.html. 
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operations strategies to specific conditions. Anticipate problems and take 

corrective actions early. Also, regional and multimodal cooperation is key to 

the success of deploying effective operations – many different agencies have 

a stake in the congestion problem. Therefore, a broad perspective should be 

taken in applying capital and operations strategies – avoid a narrow, facilityoriented view. 

5. Use Performance Measures to Track Progress – One of the main actions that 

transportation agencies can contribute is the tracking of congestion trends 

and the effect of improvements over time. Trends provide a basis for determining how well your actions are working and can identify changes in the 

underlying congestion problem (e.g., traffic crashes may become more 

important in your area). Use of performance measures also brings an element of accountability to the process – what we are really getting for our 

investments – just as businesses do. 
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DETAILED DEFINITIONS

Travel Time Index (TTI) is a comparison between the travel conditions in the 

peak period to free-flow conditions. It uses the units of travel rate (the inverse of 

speed) due to the ease of mathematical calculation and availability of data elements in both traffic surveillance and roadway inventory databases. The equation below presents the calculation of the travel time index for areawide 

applications. 

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎝

⎛

+

⎟

⎟

⎟

⎟

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎜

⎜

⎜

⎜

⎝

⎛

+ ×

⎟

⎟

⎟

⎟

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎜

⎜

⎜

⎜

⎝

⎛

×

=

Peak Period VMT

ArterialStreet

Principal

VMT

Peak Period

Freeway

VMT

Peak Period

ArterialStreet

Principal

Free - flow Rate

ArterialStreet

Principal

Travel Rate

ArterialStreet

Principal

VMT

Period

Peak

Freeway

Rate

Free - flow

Freeway

Travel Rate

Freeway

Time Index

Travel

The index can be applied to various system elements with different free-flow 

speeds. The travel time index compares measured travel rates to free-flow conditions for any combination of freeways and streets. Index values can be related 

to the general public as an indicator of the length of extra time spent in the transportation system during a trip. 

The Buffer Time Index (BTI) expresses the amount of extra “buffer” time needed 

to be on-time 95 percent of the time (late one day per month). Indexing the 

measure provides a time and distance neutral measure, but the actual minute 

values could be used by an individual traveler for a particular trip length. The 

index is calculated for each road segment and a weighted average is calculated 

using vehicle-miles of travel as the weighting factor. 

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎣

⎡

= × 100%

(in minutes per mile)

AverageTravel Rate

(in minutes per mile)

AverageTravel Rate - (in minutes per mile)95th Percentile Travel Rate

(Using VMT)

AllSections

Average of

Weighted

Time Index

Buffer

The Planning Time Index (PTI) is simply the 95th percentile travel time index. It 

is used as a supplemental measure for reliability. Because reliability is related to 

the distribution of travel rates, the 95th percentile indicates an excessively high 

travel rate, one that only five percent of all travel rates exceed for the time period 

under consideration. 

Delay is the amount of extra time spent in congestion compared to the time it 

would take under ideal or free-flow conditions. For example, if a trip takes 10 

minutes under ideal conditions, and during the peak it takes 15 minutes, the total 

amount of delay is five minutes. 
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To Access the Full Report on Traffic Congestion and Reliability – To gain 

access to the full report and detailed appendices, go to the FHWA Office of 

Operations web site at: http://www.ops.fhwa.dot.gov/. 
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1.0 Introduction 

Mitigating congestion is a high priority for the Federal Highway Administration 

(FHWA), which has established congestion as a key focus area.8 The Traffic 

Congestion and Reliability: Linking Solutions to Problems Report supports this effort 

by providing a review of congestion in the United States. The emphasis of the 

Report is on measuring trends in travel time reliability and making travel more 

reliable through initiatives in Transportation System Management and 

Operations (TSM&O). The topic of congestion is clearly much broader than this 

focus. While the broader context of congestion is discussed, the Report spends 

most its effort on defining and measuring travel time reliability, and highlighting 

TSM&O strategies to address it. Among the important features of this report are: 

• Focus on travel time reliability. The Report extends the total delay-based statistics in the Texas Transportation Institute’s Urban Mobility Report9 with 

statistics on travel time reliability, a topic of increasing interest in the 

transportation community.

• Investigate the root causes (sources) of congestion. Understanding the 

causes of congestion leads to more effective strategies for dealing with it.

• Explore the impact of congestion on freight and the economy. Congestion 

not only affects individual travelers (primarily commuters) but also truck 

travel and costs as well as the economy as a whole.

• Present performance measurement as a process for making things better. 

How the transportation system performs is significant not just at the global 

level presented in the Report – it also can be used to make changes in day-today activities like traffic incident management and to develop more effective 

transportation investments. 

• Highlight the effects that both operational improvements and physical 

capacity expansion can have on congestion. As we present later, operations 

versus capacity expansion is not an “either/or” proposition. Rather, these 

activities are complementary and each has an important role to play in alleviating congestion. This is especially true given that recent history has taught 

us that no single strategy can effectively address congestion – only through a 

combination of strategies can congestion be controlled.

 

8 Federal Highway Administration, FY 2003 Performance and Accountability Report, 

http://www.fhwa.dot.gov/reports/2003performance/index.htm. 

9 Schrank, D. and Lomax, T., 2003 Annual Urban Mobility Report, Texas Transportation 

Institute, http://mobility.tamu.edu. 
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• Identify how congestion affects everyday life in the U.S. The Report develops a series of “vignettes” showcasing the effects of congestion on the daily 

activities of the public.

Much of the Report is devoted to measuring recent trends in congestion. One of 

the key principles that the Federal Highway Administration (FHWA) has promoted in congestion measurement is that the metrics used to track congestion 

should be based on the travel time experienced by users of the highway system. 

While the transportation profession has used many other types of metrics to 

measure congestion (such as “level of service”), travel time is a more direct 

measure of how congestion affects users. Travel time is understood by a wide 

variety of audiences – both technical and nontechnical – as a way to describe the 

performance of the highway system. All of the congestion metrics used in the 

Report are based on this concept. 
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2.0 Background to the 

Congestion Problem 

2.1 THE SOURCES OF CONGESTION

Congestion results from the interaction of many different factors – or sources of 

congestion. Congestion has several root causes that can be broken down into 

two main categories: 

1. Too much traffic for the available physical capacity to handle. Just like a 

pipe carrying water supply or the electrical grid, there are only so many 

vehicles that can be moved on a roadway for a given time. Transportation 

engineers refer to this as the physical capacity of the highway system. Physical capacity is determined by such things as: how many lanes are available to 

carry traffic, the curvature of the highway, side clearance, and interchange 

and intersection design (for example, length and position of on-ramps and 

exclusive turning lanes at intersections). Physical bottlenecks are locations 

where the physical capacity is restricted, with flows from upstream sections 

(with higher capacities) being funneled into them. This is roughly the same 

as a storm pipe that can carry only so much water – during floods the excess 

water just backs up behind it, much the same as traffic at bottleneck locations. 

However, the situation is even worse for traffic. Once traffic flow breaks 

down to stop-and-go conditions, capacity is actually reduced – fewer cars can 

get through the bottleneck because of the extra turbulence. Bottlenecks can 

be very specific chokepoints in the system, such as a poorly functioning 

freeway-to-freeway interchange, or an entire highway corridor where a system of bottlenecks exists, such as a closely spaced series of interchanges with 

local streets.10 Physical capacity can be reduced by the addition of “intentional” bottlenecks, such as traffic signals and toll booths. Opportunities to 

improve the operation of “intentional” bottlenecks can have the effect of 

boosting physical capacity. 

2. Traffic-influencing events. In addition to the physical capacity, external 

events can have a major effect on traffic flow. These include traffic incidents 

such as crashes and vehicle breakdowns; work zones; bad weather; special 

 

10 In rural areas, physical bottlenecks take a different form. Highway grades can be a 

significant problem for large trucks in rural areas. Also, surges in travel to resorts 

during the “peak” season and travel around holidays can overwhelm physical capacity 

for some time. However, since traffic is lower in these cases or only lasts for a short 

periods of time, the total amount of delay on a yearly basis is far less than in major 

urban areas, where just about every weekday of the year is congested. 
Traffic Congestion and Reliability: Linking Solutions to Problems 

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events; and poorly timed traffic signals. When these events occur, their main 

impact is to “steal” physical capacity from the roadway. Events may also 

cause changes in traffic demand by causing travelers to rethink their trips 

(e.g., snow and other types of severe weather). 

Physical bottlenecks have been the focus of transportation improvements – and 

of travelers’ concerns – for many years. On much of the urban highway system, 

there are specific points that are notorious for causing congestion on a daily 

basis. These locations – which can be a single interchange (usually freeway-tofreeway), a series of closely spaced interchanges, or lane-drops – are focal points 

for congestion in corridors; major bottlenecks tend to dominate congestion in 

corridors where they exist. Many acquire nicknames from local motorists such 

as: 

• “Spaghetti Bowl” in Las Vegas; 

• “Hillside Strangler” in Chicago; and 

• “Mixmaster” in Dallas. 

How bad congestion becomes at a bottleneck is related to its physical design. 

Some bottlenecks were originally constructed many years ago using designs that 

are now considered to be antiquated. Others have been built to extremely high 

design specifications and are simply overwhelmed by traffic. A recent examination of national bottlenecks identified the worst physical bottlenecks in the 

country and examined the positive effects that improving them could have on 

travel times, safety, emissions, and fuel consumption. Table 2.1 provides a 

ranking of these bottlenecks. 

Table 2.1 The Worst Physical Bottlenecks in the United States 

2002 

Rank City Location 

Annual Hours 

of Delay 

(Hours in Thousands) 

1 Los Angeles U.S. 101 (Ventura Freeway) at I-405 Interchange 27,144 

2 Houston I-610 at I-10 Interchange (West) 25,181 

3 Chicago I-90/94 at I-290 Interchange (“Circle Interchange”) 25,068 

4 Phoenix I-10 at SR 51/SR 202 Interchange (“Mini-Stack”) 22,805 

5 Los Angeles I-405 (San Diego Freeway) at I-10 Interchange 22,792 

6 Atlanta I-75 south of the I-85 Interchange 21,045 

7 Washington 

(D.C.-MarylandVirginia) 

I-495 at I-270 Interchange 19,429 

8 Los Angeles I-10 (Santa Monica Freeway) at I-5 Interchange 18,606 

9 Los Angeles I-405 (San Diego Freeway) at I-605 Interchange 18,606 

10 Atlanta I-285 at I-85 Interchange (“Spaghetti Junction”) 17,072 

11 Chicago I-94 (Dan Ryan Expressway) at I-90 Skyway Split (Southside) 16,713 
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Rank City Location 

Annual Hours 

of Delay 

(Hours in Thousands) 

12 Phoenix I-17 (Black Canyon Freeway) at I-10 Interchange (the 

“Stack”) to Cactus Road 

16,310 

13 Los Angeles I-5 (Santa Ana Freeway) at SR 22/SR 57 Interchange 

(“Orange Crush”) 

16,304 

14 Providence I-95 at I-195 Interchange 15,340 

15 Washington 

(D.C.-MarylandVirginia) 

I-495 at I-95 Interchange 15,035 

16 Tampa I-275 at I-4 Interchange (“Malfunction Junction”) 14,371 

17 Atlanta I-285 at I-75 Interchange 14,333 

18 Seattle I-5 at I-90 Interchange 14,306 

19 Chicago I-290 (Eisenhower Expressway) between Exits 17b and 23a 14,009 

20 Houston I-45 (Gulf Freeway) at U.S. 59 Interchange 13,944 

21 San Jose U.S. 101 at I-880 Interchange 12,249 

22 Las Vegas U.S. 95 west of the I-15 Interchange (“Spaghetti Bowl”) 11,152 

23 San Diego I-805 at I-15 Interchange 10,992 

24 Cincinnati I-75, from Ohio River Bridge to I-71 Interchange 10,088 

Source: Unclogging America’s Arteries: Effective Relief for Highway Bottlenecks, American Highway 

Users Alliance, February 2004. Delay is the extra time it would take to travel through the 

bottlenecks compared to completely uncongested conditions. The report did not consider 

many severe bottlenecks from the New York City area. As most travelers know, congestion in and around the boroughs of New York can be significant. However, a very large 

amount of delay in the New York area is related to bridge and tunnel crossings into 

Manhattan, most of which are toll facilities. Also, while the New York metropolitan area 

is laced with Interstates, parkways, and expressways, they seldom reach the proportions 

seen in other major areas, except where multiple highways converge on bridge of tunnel 

crossings. (A typical lane configuration for a New York area freeway is six lanes, three in 

each direction. But there are many of these.) Toll facilities were excluded from the study 

because toll facilities are fundamentally different from other physical bottlenecks (such as 

freeway-to-freeway interchanges) that are prevalent around the country. Delay comparisons between toll facilities and other types of bottlenecks might not be consistent since different modeling techniques would be used. If objective field measurements of delay could 

be made at all locations around the country, several river crossings into Manhattan would 

no doubt be included in a list of the nation’s worst bottlenecks. 

Most of the worst bottlenecks identified in Table 2.1 are specific chokepoints: 

freeway-to-freeway interchanges where both freeways carry extremely high traffic volumes. However, several of them were identified as “bottleneck systems.” 

In such areas, a single dominant bottleneck does not exist. Rather, traffic flow is 

influenced by several smaller bottlenecks. 

The study noted that many of the bottlenecks are either under reconstruction or 

will be shortly. It also noted that the improvement “packages” undertaken by 

transportation agencies commonly combine reconstruction with other strategies 

such as operational improvements, high-occupancy vehicle lanes, transit service, 

and toll lanes. 
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However, just focusing on physical bottlenecks only tells a fraction of the congestion story. The transportation profession has come to recognize that the level 

of congestion on a roadway is determined by the interaction of physical capacity 

with events that are taking place at a given time. For example, the effect of a traffic incident depends on how much physical capacity is present. Consider a traffic crash that blocks a single lane on a freeway. That incident has a much greater 

impact on traffic flow if only two normal lanes of travel are present than if three 

lanes are present. Therefore, strategies that improve the physical capacity of 

bottlenecks also lessen the impacts of roadway events such as traffic incidents, 

weather, and work zones. 

Only recently has the transportation profession started to think of congestion in 

these terms. Yet it is critical to do so because strategies must be tailored to 

address each of the sources of congestion, and they can vary significantly from 

one highway to another. Nationally, an estimate of how much each of these 

sources contribute to total congestion is as follows and depicted graphically in 

Figure 2.1.11 These estimates are a composite of many past and ongoing research 

studies and are rough approximations. 

Figure 2.1 The Sources of Congestion 

National Summary

Bottlenecks (40%)

Traffic Incidents (25%)

Work Zones (10%)

Bad Weather (15%)

Poor Signal Timing (5%)

Special Events/Other (5%)

It is important to note that these global estimates of congestion sources do not 

necessarily hold for specific highway corridors. For example, some highways 

may have high crash rates, leading to a greater proportion of congestion due to 

 

11 http://www.ops.fhwa.dot.gov/aboutus/opstory.htm; estimates of the effects of 

variable traffic demand are not available. 
Traffic Congestion and Reliability: Linking Solutions to Problems 

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traffic incidents. Others may be dominated by a physical bottleneck, such as a 

narrow bridge, leading to a higher proportion of bottleneck-related congestion. 

Differences in congestion sources between rural and urban are particularly 

striking. In rural areas, just about any delay that occurs will be event-related 

rather than caused by bottlenecks (insufficient capacity). In fact, in rural areas, 

preliminary estimates suggest that traffic incidents and work zones alone cause 

80 to 90 percent of what delay that does occur. Of course, the total amount of 

delay in rural areas (about 400 million vehicle-hours of delay annually) is but a 

fraction of what occurs in urban areas (about 5.1 billion vehicle-hours of delay).12 

These distinctions are extremely important because they indicate what specific 

strategies should be implemented at any given location. 

2.2 THE GROWING IMPORTANCE OF TRAVEL 

TIME RELIABILITY

A Definition of Travel Time Reliability. The traveling public experiences large 

swings in congestion level, and their expectation or fear of unreliable traffic conditions affects both their view of roadway performance, and how and when they 

choose to travel. For example, if a road is known to have highly variable traffic 

conditions, a traveler using that road to catch an airplane routinely leaves lots of 

“extra” time to get to the airport. In other words, the “reliability” of this traveler’s trip is directly related to the variability in the performance of the route she 

or he takes. 

Reliability and variability in transportation are being discussed for a variety of 

reasons. The two terms are closely related, but are slightly different in their 

focus, how they are measured, and how they are communicated: 

• Reliability is commonly used in reference to the level of consistency in transportation service; and 

• Variability might be thought of as the amount of inconsistency in operating 

conditions. 

Measures can be developed to relate the reliability/variability concept to “average measures” of mobility (i.e., measures that capture average conditions such as 

average delay, average speed, etc.) and to identify differences in performance by 

time of day, assessing the methods to measure reliability for long and short trips, 

different trip purposes, trip locations, etc. 

 

12 Preliminary estimates from the Temporary Loss of Capacity project underway at Oak 

Ridge National Laboratory. Estimates of rural delay include traffic incidents, weather, 

work zones, and traffic signals. Rural delay also includes that due to highway grades 

and high demand in recreational areas, but these have not been addressed. 
Traffic Congestion and Reliability: Linking Solutions to Problems 

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With this discussion in mind, from a practical standpoint, travel time reliability 

can be defined in terms of how travel times vary over time (e.g., hour-to-hour, 

day-to-day). This concept of variability can be extended to any other travel timebased metrics such as average speeds and delay. For the purpose of this report, 

the terms travel time variability and reliability are used interchangeably. 

What Causes Travel Times to Be Unreliable? The sources previously identified 

(e.g., traffic incidents, weather, and work zones) that contribute to total congestion also conspire to produce unreliable travel times, since events and demand 

volumes vary day-to-day. To illustrate how travel times vary, we observed 

travel times on a 4.05-mile stretch of I-75 in central Atlanta, from the I-20 junction 

to the I-85 split (Figure 2.2). To develop this figure, we calculated travel times of 

trips made at 15-minute intervals for the three-hour period from 4:00 to 7:00 p.m. 

(Data were taken from the NaviGAtor traffic surveillance system for 2002). This 

corridor is extremely heavily traveled: one location on this stretch carries more 

than 320,000 vehicles per day. The variability in travel times is caused by the 

interaction of the physical capacity, traffic demand, and all the events that 

occurred on this roadway section in 2002. That is, the interaction of all the 

sources of congestion produce unreliable travel times, as shown in the distribution of existing travel times. 

What Are the Benefits of Making Travel Times More Reliable? If it is possible 

to reduce the impact of these events on travel, a double benefit is realized: not 

only are conditions made more “reliable” (that is, less variable), but overall delay 

is reduced as well. This is because extreme events, especially in combination, 

lead to high congestion. As a proof of principle, we examined what effect this 

would have on the Atlanta travel times. We reduced all of the abnormally high 

travel times (those greater than seven minutes for the 4.05-mile corridor) by an 

across-the-board 25 percent. As shown in Figure 2.2, the effect is to reduce delay 

and improve reliability. Making improvements in both the average congestion 

level and reliability is significant for a number of reasons: 

• Reducing total congestion saves time and fuel, and leads to decreased vehicle 

emissions. 

• Reducing congestion at international border crossings leads to lower transportation costs and benefits the national economy as a whole. Further, 

reducing congestion on U.S. highways for freight moving between Canada 

and Mexico fosters international trade. Therefore, congestion on U.S. highways has a large influence on the efficiency of international trade. 
Traffic Congestion and Reliability: Linking Solutions to Problems 

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Figure 2.2 Actual and Improved Peak-Period Travel Times on 

I-75 Southbound 

Central Atlanta, 2002 

0

50

100

150

200

250

4 6 8 10 12 14 16 18 20 22

Travel Time (Minutes)

Number of 15-Minute Travel Times

Actual Travel Times

After Treatment (Simulated) 

Source: The data plotted above for “Actual Travel Times” came from the NaviGAtor system in 

Atlanta, Georgia. The highway segment covered by these data is 4.05 miles long. Average travel times for vehicles moving along this segment were computed at 15-minute 

intervals for the three-hour period from 4:00 to 7:00 p.m. on weekdays for 2002. (This time 

slice is usually called the “afternoon peak period.”) A total of 12 15-minute intervals are 

therefore present each weekday. If trips were able to be made at 55 mph, they would take 

4.4 minutes to complete. 

However, the average travel time for the 4:00 to 7:00 p.m. period is about nine minutes, 

indicating that (on average) peak-period trips are made at 27 mph on this segment. Many 

trips take much longer – a few took as long as 20 minutes (an average travel speed of 

about 12 mph). Others were made in shorter amounts of time. This large variability in 

observed travel times is due to traffic-influencing events occurring on different days, 

added on top of an already congested system. (The I-20 interchange at the south end of 

this stretch is a known bottleneck.) Traffic incidents may happen on some days but not on 

others. Bad weather and temporary work zones may compound the effect of traffic incidents or may happen by themselves. 

The “After Treatment” travel times show the effect of reducing all travel times above 

seven minutes by 25 percent (hypothetically). This was done to simulate the effect of 

reducing the impacts of traffic-influencing events on travel. The “tighter” (less spreadout) distribution for the “After Treatment” case indicates that reliability (variability) has 

been improved. Also, since the majority of trips now have lower travel times, total delay 

also is reduced. This is indicated by the “After Treatment” curve’s shift to the left. More 

specifically, the average travel time is now about seven minutes in the “After Treatment”

case, reduced from nine minutes. 

• Improving reliability leads to more predictable and consistent travel in the 

corridor, something that all travelers seek: they do not have to budget as 
Traffic Congestion and Reliability: Linking Solutions to Problems 

Cambridge Systematics, Inc. 2-8

much extra time in order to arrive on time at their destinations. This is particularly important for truckers and shippers. Many activities (e.g., manufacturing, sales) are now closely timed to the arrival of shipments. Businesses 

depend on shipments to show up at precise times rather than holding onto 

inventories. Many types of personal travel – such as getting to business 

appointments and child care pickup on time – are also sensitive to unreliable 

travel times. 

• Treating three major components of unreliable travel – traffic incidents, bad 

weather, and work zones – also leads to safer highways. By reducing the 

duration of these events, we are reducing how long travelers are exposed to 

less safe conditions. 

What Value Does Providing Reliable Travel Times Have? Commuters as well as 

freight carriers and shippers are all concerned with travel time reliability. Variations in travel time can be highly frustrating and are valued highly by both groups. 

Previous research13 indicates that commuters value the variable component of 

their travel time between one and six times as much as average travel time. And 

the increase in just-in-time (JIT) manufacturing processes has made a reliable 

travel time almost more important than an uncongested trip. Significant variations 

in travel time will decrease the benefits that come from lower inventory space and 

the use of efficient transportation networks as “the new warehouse.” Therefore, in 

both the passenger and freight realms, evidence suggests that travel time reliability is valued at a significant “premium” by users. 

How Do We Measure Travel Time Reliability? Formal measures of how 

“unreliable” travel times are can be derived from describing the size and shape of 

the distributions, such as those shown for Atlanta in Figure 2.2. A complete 

description of the different measures can be found in Appendix C, but all of 

these relate directly to describing the distribution of travel times as they occur 

throughout the year. Returning to the Atlanta example, reliability statistics can 

be computed from these data, for both actual conditions and the hypothetical 

case of reducing abnormally high travel times (Table 2.2). These statistics verify 

what we observed in Figure 2.2 – that both delay and reliability are improved by 

treating “extreme” events. Operational strategies – which treat these extreme 

events – therefore have the effect of not only improving reliability but reducing 

total congestion as well. 

 

13 Cohen, Harry, and Southworth, Frank, On the Measurement and Valuation of Travel Time 

Variability Due to Incidents on Freeways, Journal of Transportation Statistics, Volume 2, 

Number 2, December 1999, http://www.bts.gov/jts/V2N2/vol2_n2_toc.html. 
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Table 2.2 Effect of Treating Unreliable Travel Times on I-75 in 

Central Atlanta 

Hypothetical 

 Southbound, 4:00 to 7:00 p.m. 

Travel Time Measure Observed Travel Times 

Abnormally High Travel Times 

Reduced by 25 Percent 

Average Travel Time (minutes) 9.0 7.1 

95th Percentilea (minutes) 13.1 9.8 

Buffer Time Indexa 46% 39% 

a Reliability measures used by FHWA for performance monitoring. See Appendix C for definitions 

and discussion. The 95th percentile travel time is referred to as the “Planning Time Index” elsewhere in this report. The Buffer Time Index is the 95th percentile travel time normalized to the 

average travel time (see Appendix C). For both the 95th percentile travel time and the Buffer Time 

Index, high values indicate unreliable travel times. 

Does Congestion Level Influence Travel Time Reliability? In other words, if 

travelers on average can expect high congestion, are their travel times also more 

variable? In general, the answer is “Yes.” Figure 2.3 shows the Buffer Time 

Index plotted against the Travel Time Index for highway corridors in four cities. 

The plot reveals that as the average congestion level (Travel Time Index) 

increases, travel times become less reliable (as indicated by an increasing Buffer 

Time Index).14 Why does this occur? As congestion builds (i.e., the Travel Time 

Index increases above 1.0), the highway becomes more “vulnerable” to disruptions caused by events such as bad weather, work zones, and traffic incidents. 

That is, once traffic has broken down to stop-and-go conditions, throwing an 

additional event on top causes even greater problems. In other words, “congested highways also are unreliable highways.” 

 

14 A brief explanation of these terms appears in Figure 2.3. Appendix C has a more 

thorough discussion. 
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Figure 2.3 Relationship Between Congestion Level 

and Reliability 

0%

20%

40%

1.00 1.20 1.40 1.60 1.80 2.00 2.20 2.40

Travel Time Index (Congestion Level)

60%

80%

100%

120%

140%

160%

180%

Buffer Time Index (Reliability)

Atlanta Los Angeles

Minn-St. Paul Seattle

Trend Line

Source: Analysis of data from FHWA’s Mobility Monitoring Program (see Appendix A for a 

description of this data source). Each point on the graph came from individual freeway 

corridors in Atlanta, Minneapolis, Los Angeles, and Seattle. 

The Travel Time Index is a measure of the total amount of congestion. It is the ratio of 

the peak-period travel time to the travel time under ideal conditions. A Travel Time 

Index value of 1.3 indicates that peak-period travel takes 30 percent longer than under 

ideal conditions. Another way to think of this measure is as a “multiplier.” That is, the 

value of the Travel Time Index is the amount you would multiply the “ideal” travel time 

by to get the actual travel time you experienced. Thus, “Travel Time Multiplier” would be 

an alternate name for this term. 

The Buffer Time Index is a measure of reliability, or more appropriately, unreliability. As 

it increases, travel times become more unreliable. Although conditions vary from 

highway-to-highway and city-to-city, a general relationship between congestion level and 

reliability is present in these data – as congestion increases, so does unreliable travel. 

Many more performance measures can used to monitor congestion besides these. 

Appendix B has a discussion of performance measures. 
Traffic Congestion and Reliability: Linking Solutions to Problems 

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3.0 National State of Congestion: 

Congestion by the Numbers 

and Why It Matters 

3.1 TRENDS IN NATIONAL CONGESTION

Is congestion getting worse? Yes. The best single source for monitoring congestion trends is produced annually by the Texas Transportation Institute (TTI).15 

In their 2003 report, TTI’s researchers found that congestion levels in 75 of the 

largest metropolitan areas have grown continuously in almost every year in all 

population groups from 1982 to 2001, as exemplified by the following trends. 

Eleven-Year Average Trends (Urban Travel, 1990 to 2001) 

• Peak-period16 trips take an average of about 10 percent longer. 

• Travelers spend 51 extra hours per year in travel compared to 42 hours in 

1990. 

• The percent of freeway mileage that is congested has grown from 49 percent 

to 60 percent. 

Nineteen-Year Trends (Urban Travel, 1982 to 2001) 

• “Congestion extends to more time of the day, more roads, affects more of the 

travel, and creates more extra travel time than in the past. And congestion 

levels have risen in all size categories, indicating that even the smaller areas 

are not able to keep pace with rising demand.”

• Twenty-seven urban areas have a Travel Time Index17 above 1.30 compared 

with one such area in 1982. 

 

15 Schrank, D. and Lomax, T., 2003 Annual Urban Mobility Report, Texas Transportation 

Institute. 

16 In most metropolitan areas, the idea of “rush hour” is obsolete – congestion happens for 

multiple hours on both morning and evening weekdays. 

17 Travel Time index is the ratio of actual travel time for a trip compared to the “ideal”

travel time for a trip. Thus, a Travel Time Index of 1.3 indicates that the trip takes 30 

percent longer than it would under “ideal” or uncongested conditions. 
Traffic Congestion and Reliability: Linking Solutions to Problems 

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• Sixty-seven percent of the peak-period travel is congested compared to 

33 percent in 1982. Each traveler in the top 75 urban areas spent an average 

of 51 hours per year stuck in traffic in 2001, up from 17 hours in 1982. 

• Sixty percent of the major road system is congested compared to 34 percent 

in 1982. 

• The number of hours of the day when travelers might encounter congestion 

has grown from 4.5 hours to 7 hours. 

Why Is This a Problem? 

• Congestion isolates people from other activities, such as business, recreation, 

and family time. 

• Congestion results in a less productive work force. Except for the distracted 

drivers chatting on their cell phones, drivers are not very productive while 

commuting. And too often they are tense and frustrated when they get to 

work. 

• Service workers do not make as many calls per day if they spend extra time 

in stop-and-go traffic. This is particularly important for emergency medical, 

fire, and police services which may be unnecessarily delayed from attending 

to medical, crime, and disaster situations. 

• Congestion leads to increased fuel consumption and automobile emissions 

(because vehicles are operating less efficiently). 

• Congestion caused by unexpected events leads to increased vehicle crashes. 

When traffic incidents occur, congestion often appears in places where 

motorists are not expecting it. Rubbernecking and conflicts with emergency 

vehicles just make the problem worse. Work zones present unexpected 

changes in highway alignment and other features. By reducing how long 

traffic incidents and work zones last, we are not only reducing congestion 

and improving reliability, but making it safer for travelers as well. 

• Companies with production schedules timed to take advantage of trucks 

delivering components to an assembly line as they are needed must instead 

plan for items to arrive early. This consumes space and inventory, expending 

resources that could otherwise be spent on productive activity. 

Congestion has clearly grown. Congestion used to mean it took longer to get 

to/from work in the “rush hour.” It used to be thought of as a big city issue or 

an item to plan for while traveling to special large events (Figure 3.1). Sure there 

was slower traffic in small cities, but it was not much more than a minor inconvenience. The problems that smaller cities faced were about connections to and 

between cities, manufacturing plants, and markets. 
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Figure 3.1 Peak-Period Congestion (Travel Time Index) Trends by U.S. 

Population Group 

1.00

1.10

1.20

1.30

1.40

1.50

1.60

1982 1986 1990 1994 1998 2002

Very Large Large Medium Small

Population Group Travel Time Index

Source: Reference (1). The Travel Time Index is a measure of the total amount of congestion. It is 

the ratio of the weekday peak-period travel time to the travel time under ideal conditions. 

A Travel Time Index value of 1.3 indicates that peak-period travel takes 30 percent longer 

than under ideal conditions. Population groups are: Very Large ( greater than three 

million); Large (one to three million); Medium (500 thousand to one million); Small (less 

than 500 thousand). 

As the economy and lifestyles have changed over the past two decades, congestion is an element that is more often thought of during the planning stages. 

Maybe that is the measure of the age we live in, with more meetings and child 

activities to arrange. Congestion effects also are reflected in decisions about 

business location and expansion, home and job sites, school, doctor visits, recreation, and social events and even who you date.18 But it also is due to the fact 

that congestion affects more trips, more hours of the day and more of the transportation system (Figure 3.2). Congestion is affecting not only weekday commuter travel but several other types of travel: weekend travel in suburban 

shopping areas, travel near major recreational areas, and travel related to special 

events (such as sporting events). 

Consider the following characteristics of congestion trends: 

• Congestion affects more of the system. You might encounter stop-and-go 

traffic on any major street or freeway. Congestion effects have spread to 

neighborhoods, where cities and residents have developed elaborate plans 

and innovative techniques to make it harder for commuters to use the streets 

where kids play as bypass routes for gridlocked intersections. 

• Congestion affects more time of the day. We are not just seeing these problems in the “rush hour.” Peak periods typically stretch for two or three hours 

 

18 Caitlin Liu. Los Angeles Times. “SigAlert on the Roadway to Love; Traffic Sometimes 

Dictates the Route of Romance in Los Angeles.” February 13, 2004. 
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in the morning and evening in metro areas around one million people 

(Figure 3.3). Larger areas can see three or four hours of peak conditions. 

These are just the average conditions. Many cities have a few places where any 

daylight hour might see stop-and-go traffic. Weekend traffic delays have 

become a problem in recreational areas, near major shopping centers or sports 

arenas and in some constrained roadways. 

Figure 3.2 Weekday Peak-Period Congestion Has Grown in Several Ways in 

the Past 20 Years in Our Largest Cities 

Duration4.5 hours per day

Extent

33% of travel

39% average delay

Intensity

Intensity

13% average delay

Congestion 

In 2001

Extent

67% of travel

7 hours per day

Duration

Congestion 

In 1982

Source: Analysis of data used in 2003 Annual Urban Mobility Report, Texas Transportation Institute. 

Figure 3.3 How Many Rush Hours in a Day? 

Cities 500K to 1 Million Cities more than 1 Million

Number of Peak Hours

0

1

2

3

4

5

6

7

8

1982

2001

Source: Analysis of data used in 2003 Annual Urban Mobility Report, Texas Transportation 

Institute. 
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• The extra travel time penalty has grown. It just takes longer to get to your 

destination (Figure 3.4). Not just work or school, but shopping trips, doctor 

visits and family outings are planned around the questions “How long do I 

want to spend in the car, bus or train?” and “Is it worth it?” Peak-period trips 

required 39 percent more travel time in 2001 than a free flow trip at midday, 

up from 28 percent 10 years earlier. 

Figure 3.4 In-Vehicle Travel Times for Peak-Hour Trips Have Grown 

Substantially in Large Cities 

0 10 20 30 40 50 60

1982

2001

1982

2001

500K-1 Million

1+Million 

Urban Population

In-vehicle Time

(in Minutes)

Source: Analysis of data used in 2003 Annual Urban Mobility Report, Texas Transportation Institute. 

Travel Reliability is getting worse. There really is a fourth characteristic to the 

congestion problem – Reliability. The extra travel time and amount of the day 

and system affected by travel delays is not the same every day. It may not even 

be as it was predicted 10 minutes ago (Figure 3.5). 

Figure 3.5 Travel Time Reliability Illustration 

1982 Average

In 1982, if your commute was 

20 minutes at midday, it took 

23 minutes in the peak and 

you would spend an extra 15 

hours on the road each year.

By 2001, that 20 

minute off-peak trip 

took 28 minutes.

And if you have an

important meeting, the 

reliability problems mean that

you should allow 40 minutes 

for the same trip.

2001 Average 2001 Planning

Source: Analysis of data used in 2003 Annual Urban Mobility Report, Texas Transportation Institute. 
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• 1982 – If your midday trip took 20 minutes, it would take you 23 minutes in 

the peak. Although no reliability statistics exist from that long ago, analysis 

of recent data suggest that you would have to add an additional nine minutes 

to that trip to guarantee on-time arrival at your destination; a total of 32 minutes would be planned for that trip. 

• 2001 – By 2001, that 20-minute free-flow trip took 28 minutes. 

• 2001 (Planning Time) – And if on-time arrival was important you should 

allow 40 minutes for that trip. 

Why is Congestion Getting Worse and Where Is It Headed? Highway travel in 

the United States has been increasing at about two to three percent per year for 

the past decade; barring serious upheavals in the economy, growth is expected to 

continue but perhaps at a slower pace. This growth comes on top of a highway 

system which in many cities already is congested. To understand how congestion grows, consider the following. As traffic volumes rise, speeds stay near the 

speed limit. It may feel more crowded, but drivers do not slow down. Capacity 

on a freeway lane is between 2,050 and 2,200 vehicles in an hour (allowing for 

some trucks); only the last few hundred cars result in any speed decline. As 

vehicles in an hour reach the upper limit of capacity, speeds drop to a stop-andgo condition. As if that was not enough of a problem, the number of cars carried 

on the road also drops. So, fewer cars and trucks can use each lane, and once 

they do begin traveling, it is at a slower speed. This is one of the reasons the 

economic price of congestion is so high. Congestion robs part of the value of 

highway investment by causing the highway’s capacity to be diminished below 

the capacity it is capable of conveying. Congestion, in other words, creates massive social and investment inefficiency by actually diminishing the performance 

capacity of an existing infrastructure asset. 

The future holds more of the same. Population and employment growth in 

America’s large cities are expected to continue rising by around two percent each 

year, resulting in longer periods of congestion on more of the transportation 

system. Tables 3.1 and 3.2 present forecasts of population and economic activity – strong drivers of transportation activity – along with forecasts of systemlevel transportation activity. The tables show that compared to year 2000: 

• By 2025, the U.S. population will grow by 26 percent; 

• By 2025, the Gross National Product will double; 

• By 2025, passenger-miles (all modes, including highway, air, and transit) will 

grow by 72 percent; and 

• By 2020, intercity truck tonnage will grow by 75 percent. 
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Table 3.1 Forecasts of Passenger Transportation System Activity 

Activity 2000 2010 2025 

Population (millions) 276 309 347 

GDP ($2,000 billion) 9,834 13,043 19,816 

Vehicle-Miles (billions) 2,424 3,041 4,173 

Licensed Drivers (millions) 213 244 274 

Vehicles (millions) 215 262 326 

Source: U.S. Department of Transportation, Bureau of Transportation Statistics, 

http://www.eia.doe.gov. 

Table 3.2 Forecasts of Freight Transportation System Activity 

Tons (Millions) 

Mode of Travel 1998 2010 2020 

Total 15,271 21,376 25,848 

Domestic 

Air 9 18 26 

Highway 10,439 14,930 18,130 

Rail 1,954 2,528 2,894 

Water 1,082 1,345 1,487 

Total, Domestic 13,484 18,820 22,537 

International 

Air 9 16 24 

Highway 419 733 1,069 

Rail 358 518 699 

Water 136 199 260 

Other (including pipeline) 864 1,090 1,259 

Total, International 1,787 2,556 3,311 

Source: FHWA Freight Analysis Framework, http://www.ops.fhwa.dot.gov/freight/ 

freight_news/FAF/talkingfreight_faf.htm. 

Keeping up with this increased demand for transportation services will be 

extremely challenging, and will require new ways of thinking about the problem. 

For highway travel alone, addressing the growth in demand will be particularly 

tricky. Figure 3.6 shows the effect of different growth rates for travel (as measured by vehicle-miles of travel) on congestion (as measured by the Travel Time 

Index, or TTI). The effect of adding increased demand on top of an already congested peak-period highway system can be easily seen – note the steep growth in 

congestion for the two percent per year growth compared to the 0.5 percent 

growth. These forecasts assume that no improvements to the transportation 

system will be made over the 2002 to 2010 period, which is clearly not the case. 
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However, they can be used to illustrate two significant points about congestion 

growth: 

1. The effect of “doing nothing” to the transportation system is probably intolerable. Under the two percent VMT growth rate – roughly a continuation of 

recent trends – peak-period congestion will worsen substantially. 

2. The effect of strategies aimed at controlling VMT growth – and controlling 

congestion in general – can have a dramatic impact on controlling congestion 

growth. Strategies that reduce VMT directly (such as demand management 

controls discussed in the next section) can lead to a substantial slowdown in 

congestion growth. Likewise, congestion mitigation strategies can have the 

same effect by increasing physical capacity, shifting demand, and improving 

roadway operations. In other words, congestion mitigation strategies can 

produce the same effect as reduced VMT growth. When used in combination, 

demand management and mitigation strategies can have a powerful impact 

on congestion growth. 

Figure 3.6 Projected Growth in Urban Freeway Congestion 

(Bottleneck-Related) under Different VMT Growth Rates, 2002-2010 

(Top 78 Metro Areas) 

1.30

1.35

1.40

1.45

1.50

1.55

1.60

1.65

1.70

2002 2003 2004 2005 2006 2007 2008 2009 2010

Year

Peak Period TTI

VMT Growth Rate

0.5%

1.0%

2.0%

Source: Analysis of 2002 HPMS data using the Surface Transportation Efficiency Analysis Model 

(STEAM) delay relationships (http://www.fhwa.dot.gov/steam/). The analysis assumes 

that no additional highway improvements are made. The Travel Time Index (TTI) is a 

measure of total congestion. It is the ratio of the peak-period travel time to the travel time 

under ideal conditions. A TTI value of 1.4 indicates that peak-period travel takes 

40 percent longer than under ideal conditions. 
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What Are the Trends in Reliability at the Metropolitan Level? Only recently 

have we defined reliability and started to measure it. Part of the problem is 

related to the availability of data. Because reliability is defined by how travel 

times vary, a continuous history of travel times is needed to measure it. 

Archived data from traffic management centers are starting to be used for this 

purpose. Figure 3.7 shows travel time and reliability statistics from 2001 and 

2002. Although it is difficult to note trends with only two years of data, this is 

just the beginning of our monitoring of reliability. As we begin to track more 

cities for more years, we will be able to observe long-term changes in reliability. 

Figure 3.7 Travel Time and Reliability Trends 

2001-2002 

Peak Period Index Value

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

2.00

Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov

Weekday (non-holiday)

2001

21 cities monitored

2002

23 cities monitored

Travel Time Index PTI TTI-Month PTI-month BTI BTI-month

Source: Analysis of data from FHWA’s Mobility Monitoring Program. The Travel Time Index (TTI) 

is a measure of total congestion. It is the ratio of the peak period travel time to the travel 

time under ideal conditions. A TTI value of 1.2 indicates that peak period travel takes 20 

percent longer than under ideal conditions. PTI is the Planning Time Index, the 95th percentile of the Travel Time Index. BTI is the Buffer Time Index. (See Appendix for definitions.) All are shown for individual days. The “-Month” indices are monthly averages 

and are shown to smooth out the trends. Although weekends and holidays are excluded, 

days next to holidays show light peak-period traffic characteristics (e.g., July 5). Note the 

upturn in peak period delay and unreliability in the Autumn months as vacationing travelers return to work and school. Note also that as the Travel Time Index increases, so does 

unreliable travel. 
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Is Congestion Growing in Medium and Small Cities as well as Rural Areas? 

For the time being, most of the congestion outside of cities is related to work 

zones, poor weather, and traffic incidents. This rural congestion is very small 

compared to that experienced in metropolitan areas. However, there are indications that this could change considerably. 

Demand for freight transportation in the United States is expected to grow substantially over the next 15 years. Figure 3.8 displays this dramatic expected 

growth. The most striking growth is expected to be on rural Interstate highways, 

indicating the potential for congestion to spread outside of metropolitan areas. 

The potential for non-metropolitan congestion is further indicated by the growth 

in total traffic on rural Interstates over the past decade. Since 1992, traffic has 

grown substantially on rural highways and at a faster pace than on metropolitan 

highways. Table 3.3 shows that between 1992 and 2002, traffic on rural 

Interstates increased 36 percent compared with an increase of 26 percent on 

urban Interstates. Further analysis shows that traffic volumes per available lane 

increased by 35 percent on rural Interstates compared with 23 percent on urban 

Interstates. 

Figure 3.8 Percentage of Highway Segments with over 10,000 Trucks 

Per Day 

Comparison of 1998 to 2020 

0%

10%

20%

30%

40%

50%

60%

70%

Urban- IS Urban-NHS Rural- IS Rural - NHS

1998

2020

Source: Analysis of data from the Freight Analysis Framework.
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Table 3.3 Growth in Interstate Highway Traffic 

1992-2002 

Traffic Statistic Rural Interstate Growth Urban Interstate Growth 

Total Daily Volume +36% +25% 

Daily Volume per Traffic Lane +35% +21% 

Source: Analysis of Highway Performance Monitoring System data. 

Forecasts of future traffic on the National Highway System demonstrate the 

magnitude of this problem, assuming that the existing highway system is not 

improved by 2020. Specifically, by comparing Figures 3.9 and 3.10 side-by-side, 

it is apparent that the congestion-causing potential for trucks is great. Without 

trucks, most congestion would reside within major metropolitan areas 

(Figure 3.9). When trucks are added to the highway system, congestion spreads 

into what are now essentially rural corridors (Figure 3.10).19

Analysis of major urban bottlenecks with regard to trucks also is revealing 

(Figure 3.11). This analysis used the bottleneck locations identified in the recent 

American Highway Users Alliance study along with the same truck forecasts 

used in Figures 3.9 and 3.10 at these locations. When trucks are removed from 

these bottlenecks, delay is substantially reduced, but is still present at relatively 

high levels. Since these bottlenecks are dominated by weekday commuter traffic, 

this is to be expected. Trucks can be expected to have a greater proportional 

effect on congestion where bottlenecks are located in smaller urban, urban fringe, 

and rural areas. 

In addition to these bottlenecks which are primarily related to mixing urban 

commuting and trucking, bottlenecks that primarily affect trucks also exist. 

These include border crossings with Canada and Mexico and local access highways to intermodal facilities, such as ports. In fact, a recent study of border 

crossings indicates that trucks can be delayed on average 30 minutes each time 

they try to cross from Mexico into the United States.20 Border delays – and the 

increased transportation costs they create – can seriously affect international 

trade between the United States and Canada and Mexico. Also, because trade is 

increasing between Canada and Mexico, delay to shipments on interior U.S. 

highways increases the costs of performing this trade. In essence, congestion on 

U.S. highways has become an international – not just a national – problem. 

 

19 The Freight Analysis Framework provided the data for Figures 3.9 and 3.10. 

20 Texas Transportation Institute and Battelle Memorial Institute, International Border 

Crossing Truck Travel Time for 2001, prepared for Office of Freight Management and 

Operations, FHWA, April 2002. 
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Figure 3.9 2020 Congestion Forecasts, No Trucks 

Figure 3.10 2020 Congestion Forecasts, With Trucks 
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Figure 3.11 Effect of Trucks on Delay at the 50 Worst 

Urban Bottlenecks 

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

Annual Delay (in Thousands)

With

Trucks

Without

Trucks

With

Trucks

Without

Trucks

2004

2020

Source: Unclogging America’s Arteries, American Highway Users Alliance, 2004 and analysis of 

Freight Analysis Framework data.

3.2 CONGESTION AND EVERYDAY LIFE

Transportation professionals tend to think of congestion in aggregate terms – 

congestion is a “system” problem that happens on our highway system. Yet 

every day, travelers are faced with the consequences of congestion and must 

manage their daily activities around it. In this section, we explore how congestion affects everyday life in the United States. 

1. A Traveler’s-Eye View of Reliability 

The statistics and measures presented earlier offer transportation professionals 

insight into the nature of travel time reliability. However, travelers probably 

take a slightly different view of the situation. Returning to the data from I-75 in 

central Atlanta used earlier in Section 2.0, we take a more traveler-oriented perspective on travel times in the corridor. Consider a traveler who has the flexibility to adjust his or her work schedule so that they can be traveling the corridor 

either at 3:00 p.m. or 5:30 p.m. on most days (Figure 3.12). 
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Figure 3.12 Travel Times in Central Atlanta, I-75 Southbound 

I-85 to I-20 

0

2

4

6

8

10

12

14

16

18

20

1/1/2002 2/20/2002 4/11/2002 5/31/2002 7/20/2002 9/8/2002 10/28/2002 12/17/2002

Date

Travel Time (minutes)

Start Time

3:00 P.M.

5:30 P.M.

Source: Analysis of data from FHWA’s Mobility Monitoring Program. The highway segment 

covered is 4.05 miles long. Travel times are for trips that begin at the northern end of the 

corridor starting at 3:00 p.m. and 5:30 p.m., traveling south. Trips that begin at 3:00 p.m. 

take less time to make it through the corridor AND are more reliable (less “noise” in their 

day-to-day fluctuations). 

From these data, it is clear that travel at 3:00 p.m. is both less congested and more 

reliable (less variable). In other words, the traveler can better predict what travel 

time will be at 3:00 p.m. than at 5:30 p.m. Why is this important? If a commuter 

has a routine activity that must occur every day – such as picking up children 

from day-care – it means that at 5:30 p.m. they must plan on an extra amount of 

trip time just to be sure they do not arrive late. The same goes for local trucking 

firms engaged in pickup and delivery of goods. 

Savvy commuters also understand that some days predictably are worse than 

others. Figure 3.13 shows the average travel and the 95th percentile travel time 

(the Planning Time Index) by day of week for this four-mile corridor. Fridays are 

clearly the worst, and if travelers must plan on a travel time of 14 minutes (average speed of 17 mph) just to be reasonably safe. It is also easy to predict that 

congestion and unreliable travel times will be worse than normal on days when 

adverse weather (snow, rain, fog, etc.) occurs. 
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Figure 3.13 Central Atlanta, I-75 Southbound 

Weekdays between 4:00 and 7:00 p.m.

Travel Time (Minutes)

Day of the Week

11.9

8.1

Monday

13.8

9.3

Tuesday

13.0

9.2

Wednesday

12.4

8.9

Thursday

14.0

9.5

Friday

0

2

4

6

8

10

12

14

95th Percentile Travel Time Average Travel Time

Source: Analysis of data from FHWA’s Mobility Monitoring Program. The 95th percentile travel 

time indicates what travel time is on some of the “worst traffic days.”

2. Local Businesses Learn to Deal with Congestion 

Travel time reliability problems only get worse as the system gets more congested. People react by trying to alter their trip departure time or route. Businesses might increase the number of delivery trucks, or move their product 

overnight or in the middle of the day to avoid traffic tie-ups. But at some point, 

the inefficiencies take a toll. Manufacturers must devote more plant space to 

storing inventory because they cannot be as sure of the delivery schedule. Service providers need more staff, vehicles, and equipment to cover the same area. 

This might mean more jobs, but each job does not produce as much income when 

work time is spent “in-route” rather than “on-the-job.” Figure 3.14 dramatically 

displays the problem. Shippers must base their plans by adding a “buffer” to 

normal travel times to account for unpredictability in the transportation system. 

In other words, they must use the Planning Time Index, not the Travel Time 

Index which only describes average conditions. 
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Figure 3.14 Variations in Congestion by Time-of-Day 

1.00

1.20

1.40

1.60

1.80

2.00

2.20

0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00

Time-of-Day

Index Value

Travel Time Index Planning Time Index

Source: Analysis of data from FHWA’s Mobility Monitoring Program. The Planning Time Index is 

the 95th percentile Travel Time Index. 

3. Changing Lifestyles, Changing Travel 

As people move through life, two significant choices they make are where to live 

and where to work. These choices can change over time and influence where, 

when, and how much congestion occurs on highways. For example, a young 

couple just entering the job market may decide to live in an urban setting while 

older couples with children may choose to live in the suburbs. Often these suburbs are in formerly rural areas on the urban fringe, and are significant distances 

away from traditional downtown employment areas. The decision to move into 

these fringe areas is influenced by a number of factors – affordable housing, 

newer schools, more open space, and, in some cases, avoiding congestion close in 

to cities. In response, many employers are following, moving their business 

locations closer to where prospective employees live and taking advantage of 

lower land prices. 

This ability of both residents and employers to move around the urban landscape 

has effects on congestion. First, by housing and jobs moving further out into the 

suburbs, individuals can keep their travel times to work relatively stable. However, these new travel patterns also result in congestion on roadways that were 

previously only lightly used. Of course, some folks will still choose the longer 

commute into central areas – this will cause an increase in congestion on those 

already crowded roads.
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The stability in work trip travel times may be a temporary condition. Recent 

evidence from the decennial Census suggests that even those who live and work 

in the suburbs may be experiencing increased travel times to work (Figure 3.15). 

Figure 3.15 Average Travel Times to Work 

1980-2000 

18

19

20

21

22

23

24

25

26

1980 1990 2000

Year

Travel Time to Work (in Minutes)

Source: Census Journey-to-Work data, as compiled by Alan Pisarski. 

Between 1980 and 1990, work travel times were relatively stable, but have 

increased by three minutes per trip between 1990 and 2000. While three minutes 

per trip (six minutes per day for trips to and from work) may not seem like a lot, 

it is 25 extra hours each year. The increase indicates that we may not be able to 

escape congestion just by spreading our activities to larger areas. Also, as shown 

earlier, as congestion grows, so does the unreliability of travel times. Therefore, 

as congestion builds, commuters have to budget even more time onto their trips 

if they do not want to be late. 

4. Congestion and Trucking: How Congestion Costs Us All 

Congestion has real costs for all travelers, including truckers (both long-haul and 

local pickup and delivery), household and business service providers (such as 

plumbers, computer technicians, police, and ambulance services), and personal 

travel (such as commuters, vacationers, and shoppers). Congestion causes more 

fuel to be used and more emissions to be produced. The extra time spent in congestion causes service providers to make fewer calls per day, leading to higher 

prices for consumers; this is particularly important for emergency medical, fire, 

and police services which may be unnecessarily delayed from attending to medical, crime, and disaster situations. Companies with production schedules timed 
Traffic Congestion and Reliability: Linking Solutions to Problems 

Cambridge Systematics, Inc. 3-18

to take advantage of trucks delivering components to an assembly line as they 

are needed must instead plan for items to arrive early. This consumes space and 

inventory, expending resources that could otherwise be spent on productive 

activity. For personal travelers, congestion “steals” time that could be put to 

better use in the workplace or for social or recreational purposes. 

In recent decades, the freight transportation system has been in transition from 

an era focused primarily on system construction toward an era that will focus 

increasingly on system optimization. Past efforts at developing the transportation infrastructure were oriented around system construction. The railroads in 

the 19th century and the highway system in the twentieth century connected all 

parts of the nation, and opened up markets for moving freight and passengers. 

Today, these systems are extensive and mature. Ports, airports, and other freight 

systems have been developed, and technological and management change has 

increasingly focused on optimizing the use of available physical assets. Those 

mature assets require large expenditures of resources to maintain and preserve 

asset conditions, meaning that fewer resources are available to build new 

capacity. 

Freight transportation has gone through many changes over the past 20 years as 

it has adapted to changes in business practices. Within this new operating environment, freight operations and productivity have been optimized to work 

closely with other aspects of business activity. Deregulation has resulted in 

excess capacity being eliminated from the highway and rail freight systems. 

Intermodal services and facilities have revolutionized international trade. Ports 

and airports have seen services and demand grow rapidly. Freight services are 

now more efficient and in many cases lower in cost (in constant dollars) than in 

previous decades. But the elimination of excess capacity has resulted in systems 

with less redundancy and less ability to withstand shocks or disruptions. Congestion is growing on many key freight segments of the transportation system, 

and congestion can drastically reduce the productivity of the overall freight network. The delay caused by congestion could vastly increase the costs of those 

freight movements that are today managed to exacting schedules. 

Time is literally money for trucking interests. As shown in Figure 3.16, a direct 

linkage exists between transportation investment, travel conditions (congestion 

and reliability) and economic productivity. For trucking, two key trends identified above will have a substantial impact on the total cost of moving freight: 

1. As congestion spreads into the midday period, which is the peak travel 

period for trucks, more direct costs will be incurred; and 

2. Reliability – for trucks, the ability to hit delivery windows predictably will 

decrease and will add even more costs as firms struggle to optimize delivery 

schedules. This is especially a problem for truckers who must meet “just-intime” delivery schedules set by shippers and manufacturers. 
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Figure 3.16 Economic Effects of Transportation 

Transportation system investment Transportation system investment

Transportation system efficiency Transportation system efficiency

Travel time Travel time Cost Cost

Productivity Productivity Labor and market access Labor and market access

Competitiveness Competitiveness

Economic growth Economic growth

Reliability Reliability

In 1999, purchases of transportation-related goods and services accounted for 

10.6 percent ($980 billion) of GDP.21 The diverse and extensive list of purchases 

includes the services of for-hire freight carriers, vehicles, parts, maintenance, and 

fuel. Only housing, health care, and food account for a greater share of GDP. 

Transportation accounts for a share of the final price of a product, ranging from 

one percent to 14 percent, depending on the commodity and distance moved.22 

Thus, changes in the physical condition and operating characteristics of the 

highway system can have a major effect on the final price of goods and services. 

Improvements in freight transportation productivity (such as reduced costs due 

to congestion) contribute to the economy by helping the United States remain 

competitive in international trade. 

All of this adds up to a staggering amount of costs imposed on travelers by congestion. The Texas Transportation Institute estimates that in 75 of the largest 

U.S. cities in 2001, $69.5 billion dollars are wasted in time and fuel costs.23 (The 

costs are a composite of automobile and truck travel costs in urban areas.) The 

time value costs used for trucks in this calculation are conservative – they include 

only the cost of truck operating time, primarily the cost of drivers’ wages and 

equipment. The value of the cargo and the response of firms to higher 

 

21 Status of the Nation’s Highways, Bridges, and Transit: 2002 Conditions and 

Performance Report.

22 U.S. Department of Transportation, Bureau of Transportation Statistics, Transportation 

Satellite Accounts. 

23 Schrank, D. and Lomax, T., 2003 Annual Urban Mobility Report, Texas Transportation 

Institute. 
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transportation costs are not included, yet recent work suggests these costs can be 

significant. These costs include: 

• Foregone Investment Opportunities – Higher transportation costs due to 

congestion reduce a firm’s ability to invest in making more products, 

improve product quality, and introduce new products; and 

• Decreases in Regional Employment or Decreases in the Rate of Growth of 

Regional Income – Higher transportation costs are passed onto other sectors 

of the economy and hinder general economic efficiency.24

 

24 ICF Consulting, HLB Decision Economics, and Louis Berger Group, Freight Benefit/Cost 

Study: Capturing the Full Benefits of Freight Transportation Improvements: A Non-Technical 

Review of Linkages and the Benefit/Cost Analysis Framework, May 11, 2001. 
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4.0 How Can We Deal 

with Congestion? 

4.1 THE TOOLBOX FOR CONGESTION RELIEF

What Can We Do About Traffic Congestion? 

The Atlanta experience described above is a good synopsis of potential strategies 

that can be used against congestion. Transportation engineers and planners have 

developed a variety of strategies to deal with congestion (Figure 4.1). These fall 

into three general categories: 

1. Adding More Capacity – Increasing the Number and Size of Highways and 

Providing More Transit and Freight Rail Service. Adding more lanes to 

existing highways and building new ones has been the traditional response 

to congestion. In some metropolitan areas, however, it is becoming 

increasingly difficult to undertake major highway expansions because of 

funding constraints, increased right-of-way and construction costs, and 

opposition from local and national groups. However, it is clear that adding 

new physical capacity for highways, transit, and railroads is an important 

strategy for alleviating congestion. In many locations, it is the lack of physical capacity that contributes the most to congestion. In such locations, the 

addition of new capacity is critical. Further, the addition of new capacity 

presents an excellent opportunity to combine it with other types of strategies. 

This often means that highway designers must think “outside the box” and 

find creative ways to incorporate new designs that accommodate all stakeholders’ concerns. Since the worst highway bottlenecks tend to be freewayto-freeway interchanges, advanced design treatments that spread out turning 

movements and remove traffic volumes from key merge areas have been 

developed, often by using multilevel structures that minimize the footprint of 

the improvement on the surrounding landscape. 

Adding new freeways or additional lanes to existing freeways will add large 

amounts of capacity to the roadway network. However there are other components of the transportation system that can be enhanced that will alleviate 

congestion, albeit in a more localized area. Widening arterial roads, 

providing street connectivity, provide grade separations at congested intersections and providing high-occupancy vehicle (HOV) lanes all will help to 

mitigate congestion. Also, adding capacity to the transit system, whether it is 

to the bus system, urban rail system or commuter rail system will assist in 

relieving congestion on the roadway network. Finally, adding capacity to the 

intercity rail system can reduce the use of highways by trucks. 
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Figure 4.1 A Variety of Strategies, When Used in Combination, 

Can Effectively Deal with Congestion 

Demand

Management

Alternate hours of travel

Alternative work schedules

Telecommuting

Pedestrian/bicycle facilities

Alternative fare strategies

Public education campaign on driving

“Smart Growth” policies

Pedestrian/bicycle connections

Transit stop/station design

Transit

Parking strategies

Travel

Alternatives

Land Use

Pricing

HOV

Transit

High occupancy toll lanes (HOT)

Activity center pricing

Parking pricing

Rideshare matching

Transportation Management Associations (TMAs)

Vanpools

Priority parking for HOVs

Parking cashout

Guaranteed ride home program

Instant ridesharing

Subsidized fares

Transit-oriented design

Enhanced transit stops/stations

Trip itinerary planning

Transportation Management Associations (TMAs)

Transit security systems

Freight

Transit-oriented design

Truck only toll lanes (TOT)

Time-of-day pricing

Lane restrictions

Delivery restrictions
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Figure 4.1 A Variety of Strategies, When Used in Combination, 

Can Effectively Deal with Congestion (continued) 

Roadside electronic screening/clearance programs

Operational

Improvements Highway

Vehicle tracking (AVL)

Advanced scheduling/run cutting

Signal priority for buses

Bus ramp bypass

Real -time transit information

Express bus service

Demand responsive bus service

Fare strategies

Vehicle tracking (AVL)

Real -time freight information

New freeways/arterials

Widen freeways/arterials

Street connectivity

New toll roads

Grade separations

HOV/managed lanes

Multimodal corridors

Arterial

Freeway

Geometric improvements

Intersection improvements

One-way streets

Access management

Advanced signal systems

Signal retiming/optimization

Changeable lane assignments

HOV ramp bypass

Incident management

Event management

Real-time traveler information

Parking restrictions

Transportation Management 

Center operations

Incident management

Event management

Ramp metering

Lane controls

Managed lanes

Real-time traveler information

Electronic toll collection

Work zone management

Road weather information systems

Variable speed limits

Ramp closures

Bottleneck removal

New rail lines

New bus routes

New busways/BRT

Additional service on existing lines/routes

Neighborhood/activity center circulator routes

Park/ride lots

Truck only lanes

Rail improvements

Additional

Capacity

Transit

Freight

Highway

Transit

Freight

Highway

-

Fare strategies

Vehicle tracking (AVL)

Real -time freight information

Arterial

Freeway

-

Additional

Capacity

Road weather information systems
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2. Operating Existing Capacity More Efficiently – Getting More Out of What 

We Have. (“Operational Improvements” in Figure 4.1). In recent years, 

transportation engineers and planners have increasingly embraced strategies 

that deal with the operation of existing highways, rather than just building 

new infrastructure. The philosophy behind Transportation System 

Management and Operations (TSM&O) is to mitigate the effects of roadway 

events and to manage short-term demand for existing roadway capacity. 

TSM&O includes the application of advanced technologies using real-time 

information about highway conditions to implement control strategies. Collectively referred to as Intelligent Transportation Systems (ITS), real-time 

control of highway operations through a transportation management center 

(TMC) has become a major activity undertaken by transportation agencies. 

ITS control strategies take many forms: metering flow onto freeways, 

dynamically retiming traffic signals, managing traffic incidents, and 

providing travelers with information about travel conditions, alternative 

routes, and other modes. ITS is also used to improve transit service and 

freight activities. In addition to ITS, other TSM&O strategies to improve the 

efficiency of the existing road system have been implemented, including 

reversible commuter lanes, movable median barriers to add capacity during 

peak periods, and restricting turns at key intersections. There are numerous 

congestion mitigation strategies that are enhanced by the use of advanced 

technologies or ITS. These strategies are highlighted in italics in Figure 4.1. 

There are several other effective strategies that do not rely on advanced technology, including geometric improvements to roads and intersections, converting streets to one-way operations and access management. 

The idea behind TSM&O strategies is to increase the efficiency of the existing 

transportation infrastructure. That is, roadway events essentially “steal”

roadway capacity and TSM&O seeks to get it back. The deployment of 

TSM&O strategies and technologies is increasing and evaluations have 

shown their impact to be highly cost-effective. However, relying on TSM&O 

alone is a limited approach to addressing the congestion problem. A sound 

base infrastructure already must exist before TSM&O can be used. Also, only 

so much extra efficiency can be squeezed out of an already stressed highway 

system. 

Improving the efficiency and reliability of the freeway, street, transit, and 

freight systems is an aspect of the transportation program that in many cases 

can be accomplished in shorter time, with more public support and at a lower 

cost than some other strategy groups. The size of the benefits from any single 

project may not be of the magnitude of a new freeway lane or rail transit line, 

but the cost and implementation time also are not as high. One key to understanding the benefits from operational projects is to think of these strategies 

as enhancing the return on investment in the infrastructure projects. 
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3. Encouraging Travel and Land Use Patterns that Use the System in Less 

Congestion Producing Ways – Travel Demand Management (TDM), NonAutomotive Travel Modes, and Land Use Management. (“Demand 

Management” in Figure 4.1.) Other approaches to the problem of congestion 

involve managing the demand for highway travel. These strategies include 

putting more people into fewer vehicles (through ridesharing or dedicated 

highway lanes for high-occupancy vehicles), shifting the time of travel 

(through staggered work hours), and eliminating the need for travel altogether (through telecommuting). The major barrier to the success of TDM 

strategies is that they require an adjustment in the lifestyles of travelers and 

the requirements of employers. Flexible scheduling is simply not possible for 

a large number of American workers, which limits the effectiveness of TDM 

strategies. Investing in non-automotive modes of travel – such as rail and 

bus transit systems and bikeways – is another strategy for reducing the number of personal use vehicles on the highway system. These approaches can 

be an excellent supplement to the highway system, particularly for commuter 

trips. However, in most metropolitan areas, the level of investment required 

to meet transportation demand solely through these means is massive and 

infeasible. Another approach that is being recently considered in many 

urban areas is managing demand through pricing schemes. Pricing strategies 

include charging for the use of HOV lanes either by the number of persons in 

the vehicle or by time of day or both and variable parking charges depending 

on location, vehicle occupancy, or time of day. Still, when considered as part 

of an overall program of transportation investments, TDM and nonautomotive modes of travel can contribute substantially to a metropolitan 

area’s transportation system. 

Land use management is another type of strategy that can influence congestion. The historical cycle of suburban growth has led to an ever increasing 

demand for travel. Suburban growth was originally fueled by downtown 

workers who moved from city centers to the urban fringe to take advantage 

of lower land prices and greater social amenities. In the past 20 years, businesses also have moved to the suburbs to be closer to their employees and to 

take advantage of lower rents. This in turn allows workers to live even further away from city centers, thereby perpetuating suburban expansion. To 

influence these processes, strategies that attempt to manage and direct urban 

growth have been used in several metropolitan areas. These include land use 

controls (zoning), growth management restrictions (urban growth boundaries and higher development densities), development policies (transitoriented design, which provides land use densities and forms to favor transit 

use) and taxation policy (incentives for high-density development). The 

main problem with many of these strategies is that they often are contrary to 

market trends, burdening consumers with extra costs and dampening economic efficiency, at least in the short term. Unless a truly regional approach 

is followed – with cooperation of all jurisdictions within the region – sprawl 

may simply be pushed into areas not conforming to growth policies. 
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4.2 USING VISION-ORIENTED PLANNING AND 

REGIONAL COLLABORATION TO ADDRESS 

CONGESTION

Perhaps the most important first step to addressing congestion is to have a 

transportation “vision” for the region that is easily understood, communicated 

and a part of a consensus-building effort across the region. The traditional 

transportation planning process, which has been used successfully in metropolitan areas for the past 40+ years, provides a starting place for this activity. Transportation planning begins with establishing a vision, which is then translated 

into a series of more specific goals. In turn, projects and policies are developed 

to meet the goals. There may be different goals and solutions for each part of the 

region, but places that have made progress have had broad support from business, elected officials, and the public for a set of strategies and the funding package to enact them. 

Atlanta, Georgia, metropolitan Washington, D.C., and the eight larger cities in 

Texas are creating vision-oriented long-range plans that add information to the 

public debate about how transportation investments should be made. In addition to questions like which projects to pursue and what the future growth rate 

will be, these areas are broadening the financial discussion. These plans are not 

justifications for additional public spending. They identify a broad range of 

funding options and the benefits of providing a mix of projects, programs, and 

policies to improve transportation, along with consideration of the implications 

for communities and the environment. The goal of these efforts is for the public 

to understand the choices between funding and the condition of the transportation services. Taxpayers and travelers can decide on the mix of spending and 

congestion levels that match their expectations. 

Incorporating “visioning” into the planning process is nothing new, but what 

distinguishes these recent efforts is that they are more accelerated, more visible, 

more inclusive of stakeholders, and focus not just on the long term but on shortterm activities that try to achieve “early successes.”

In Atlanta’s “Aspiration Plan,” a key part of this process is the re-examination of 

near-term improvements and interactions between some plan elements. Some 

examples include: 

• Seeking solutions to bottlenecks in the road and transit networks. These are 

places where improving a constriction can allow remaining portions of the 

system to accept more traffic, and can be done over the next few years. 

• Is there enough traffic incident management funding and are the responders 

clearing crashes and vehicle breakdowns as rapidly as possible? Are there 

good working relationships and defined procedures among the responders? 

Atlanta has a reputation for extensive operations, but are there other actions 
Traffic Congestion and Reliability: Linking Solutions to Problems 

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that, with some additional attention and funding, could improve the services 

and system performance? 

• Coordinate the traffic signals. This will require local agencies to work 

together, which will have other benefits. It also is an improvement that is 

relatively quick to enact and has good public support. It may, however, 

require a reallocation of operating budgets and may lead to an increase in the 

Aspirations Plan funding levels, as well. 

Atlanta’s Aspiration Plan highlights an important aspect of Vision-Oriented 

Planning – the need to think regionally about solutions to congestion problems. 

In fact, FHWA is promoting the concept of regional partnerships as a means to 

implementing effective operations. These partnerships provide a platform for 

interagency coordination and joint delivery of operations-based services. These 

partnerships focus on convening a wide variety of stakeholders. They address 

activities that cross functional and jurisdictional boundaries such as traffic incident management programs, real time traveler information services, response to 

weather events, and emergency management. Regional partnerships emphasize 

the importance of linking operations to the existing transportation planning 

process. Examples include:25

• Baltimore Regional Operations (B-ROC) Project – more than 20 jurisdictions 

and agencies are participating in B-ROC with the goals of enhancing operational coordination for traffic incident management and to develop a regional 

framework for operations. Transportation agencies as well as fire and police 

agencies are all cooperating in B-ROC. 

• Cross-Jurisdictional Signal Coordination in Phoenix – was initiated to 

improve the efficiency of commuting across the Phoenix region. Multiple 

jurisdictions participated in sharing data and signal control plans that made 

commuting in long corridors relatively seamless to travelers. 

• Capital Wireless Integrated Network (CapWIN) – is an integrated transportation and criminal justice wireless network instituted in the Washington, 

D.C. region. With CapWIN, agencies will be able to communicate directly 

with each other and access information for use in planning and implementing 

traffic control during major incidents. 

 

25 Federal Highway Administration, Regional Transportation Operations Collaboration and 

Coordination: A Primer for Working Together to Improve Transportation Safety, Reliability, 

and Security, http://www.itsdocs.fhwa.dot.gov//JPODOCS/REPTS_TE//13686.html. 
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4.3 THE DIVERSITY IN FUTURE 

CONGESTION SOLUTIONS

From all that has happened in the past decades regarding congestion, a fundamental principle emerges: No single strategy can adequately address the problems of metropolitan congestion. However, a balanced, comprehensive 

approach to traffic congestion can lessen the stifling gridlock found on many of 

our highways. Metropolitan areas are engaged in similar processes of 

identifying the benefits of additional transportation capacity, operations 

improvements, changes in demand patterns and land use arrangements, and the 

costs of achieving those. The outcome of these processes, however, is not similar 

and it is important to recognize the many reasons for the differences – they go to 

the heart of local decision-making. 

Cities have always had to balance issues of economic development, environmental and social effects, population and employment growth and mobility. 

What might affect decisions in the future is the evolution in the job market. If the 

U.S. economy continues a shift away from manufacturing and toward service 

and information jobs, location may not be as important as it has been in the past. 

Cities may compete for jobs and population on the basis of quality-of-life issues 

especially among workers and companies who could locate anywhere. 

The decision variations also are present within metropolitan regions: 

• More established areas that are not projected for population and employment 

growth may not have as much need for capacity expansions as developing 

regions. 

• There will always be a role for using what system we have as efficiently as 

possible. Aggressively managing the transportation system and ensuring 

that travelers know their travel options are good business practices. 

• There are some corridors that will be targeted for treatment because they are 

important to regional and national freight shipment or person movement. 

• Land use pattern changes in redeveloping areas or new growth areas also are 

part of the solution set. These decisions will vary by jurisdiction. 

• All areas will program their funds and efforts toward a variety of goals that 

include congestion or mobility. But transportation efficiency will not be the 

only goal and the solutions implemented in each area will reflect that diversity in focus. 

With congestion increasing in cities of all sizes and a focus on identifying the best 

congestion reducing treatments, there is a need for analysis techniques and data 

to provide some perspective on possible solutions. The move in some large metropolitan areas, such as Atlanta, Washington, D.C., and the large cities in Texas, 

to craft vision-oriented plans will provide more informed decision-making. 

Estimating the amount of projects, programs, and policies required to achieve 
Traffic Congestion and Reliability: Linking Solutions to Problems 

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congestion reduction can help focus the public discussions on alternative 

investment levels and their benefits. 

Congestion treatments in the three groups of strategies discussed above have 

situations that allow their attributes to be cost-effectively used to address congestion problems, quality of life goals and other factors that urban residents, 

businesses, and travelers desire. The objective of transportation plans and agencies is to identify the right mix of programs, policies, projects, and plans that can 

be funded by agencies or in partnership with others. Some strategies require 

planning time, significant funding, and long construction periods, while others 

can be deployed or enacted in a week. An effective, comprehensive program will 

include some of the components from each group. 

4.4 THE POSITIVE EFFECTS OF OPERATIONAL AND 

COMBINED STRATEGIES ON CONGESTION

Is Success Possible Against Congestion? Yes, but past success tends to be 

localized. Multiple and systematic strategies for addressing congestion are 

required, given that demand is increasing on an already stressed highway 

system. The following four examples illustrate how success can be achieved by 

aggressively pursuing a variety of congestion mitigation options. 

Results of a Study of the Benefits of Full Operational Deployment 

Background 

Deployments of operations strategies have been shown to produce significant 

benefits. Furthermore, these individual operations and ITS improvements can be 

tied together to achieve even greater benefit than they can alone. Recognizing 

that the whole is often greater than the sum of its parts, the U.S. DOT and 

numerous local agencies have launched initiatives to encourage deployment and 

integration of these systems in order to maximize their potential benefits.26

The U.S. DOT selected Tucson, Cincinnati, and Seattle for case studies representing small, medium, and large metropolitan areas, respectively. Hypothetical 

scenarios were identified comprised of a wide range of operations and ITS 

deployments at an appropriate, logical scale for each area. These scenarios were 

then evaluated to estimate the regionwide benefits and costs. 

Although the deployments analyzed did not include the full range of strategies 

discussed in this report, they did contain a large number of freeway, arterial, 

traffic incident, and transit management systems and traveler information components. These included ramp metering, integrated traffic incident management, 

 

26 Publication of this research is forthcoming. 
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and coordination of traffic signals as well as many other types of strategies. The 

findings of this study illustrate the potential of operations strategies to help mitigate congestion.27

What Were the Benefits 

In all three regions, the analysis of the full operations and ITS deployment 

showed positive impacts on all performance measures studied, including: 

• Decreased travel times; 

• Increased vehicle speeds; 

• Decreased delay; 

• Decreased number and severity of crashes; and 

• Decreased environmental impacts (reduced emissions and fuel use). 

Table 4.1 shows examples of the impacts estimated in the three regions. 

Table 4.1 Impacts of Fully Deploying Operational Strategies in Three 

Urban Areas 

 Tucson Cincinnati Seattle 

Delay -15% -18% -15% 

Travel Time -2% -4% -4% 

Fatal Crashes -7% -9% -8% 

Injury and Property Damage Crashes -3% -3% -3% 

Emissions -10% to -16% -18% to -25% -16% to -21% 

Fuel Use -11% -24% -19% 

As Table 4.1 shows, the regionwide impacts were significant in all three regions. 

Further, the positive impacts tended to be greatest during congested peak commute periods, and on major roadways and transit facilities that were the focus of 

many of the deployments. When dollar values were applied to the estimated 

impacts, the annual benefits due to the operational deployments were estimated 

to be $455 million $1,160 million and $1,610 million for Tucson, Cincinnati, and 

Seattle respectively. These benefits were broad-based as shown in Figure 4.2, 

which illustrates the proportion of benefits that were estimated for the Tucson 

region. Similar distribution of benefits also were observed in Cincinnati and 

Seattle. 

 

27 The ITS Deployment Analysis System (IDAS) model was used to study the effects of 

fully deploying operation strategies in the three cities by the year 2020. Costs were also 

derived using the IDAS model. The results are therefore simulated, rather than based 

on direct measurements. 
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Figure 4.2 Proportion of Benefits Value of Full Operations Deployment 

in Tucson 

Travel Time (23%)

Incident Delay (29%)

Crashes (12%)

Fuel Consumption (18%)

Public Agency Efficiency (1%)

Other (7%)

Emissions (10%)

When the annual benefits deployments were compared with their costs, the 

investment in operations strategies was shown to be very efficient, returning 

$6.30 to $12.20 in benefits for every dollar invested, as shown in Table 4.2. 

Table 4.2 Comparison of Benefits and Costs of Fully Deploying 

Operational Strategies in Three Urban Areas 

Region Annual Benefit Annual Costs Benefit/Cost Ratio

Tucson $455 $72 6.3 

Cincinnati $1,160 $98 11.8 

Seattle $1,610 $132 12.2 

A substantial amount of the benefits were due to assuming the deployment of a 

fully integrated traffic incident management system. By addressing the traffic 

incident portion of total congestion, total delay to motorists was decreased and 

travel reliability increased. This demonstrates the point made earlier that by 

treating roadway events that cause unreliable travel, the double benefit of 

decreased delay and increased reliability can be achieved. 

What Will It Cost? 

Many transportation agencies have found it difficult to add system capacity to 

keep pace with growing congestion. Many times, traditional approaches to congestion mitigation, such as building a new lane or adding a new transit service, 

are too expensive to be considered as viable options. Operational strategies have 

been increasingly used to squeeze more efficiency out of the existing system. 

These strategies can often be implemented at a fraction of the cost of capacity 
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improvements. This is not to say that these strategies do not have significant 

costs. 

In the case studies of full operational deployments, discussed above, the costs of 

deploying the operations strategies at the specified level of intensity in Tucson, 

Cincinnati, and Seattle would cost $78 million, $98 million, and $131 million 

respectively (Figure 4.3). These costs represent a hypothetical deployment of 

operations strategies at an intensity greater than currently deployed in these 

regions, and represent the full cost of implementing the strategies from the 

ground up. In reality, many of these implementation costs already have been 

made by the regional transportation system managers. 

Figure 4.3 Comparison of Potential Annual Costs in Tucson, Cincinnati, 

and Seattle

0

20

40

60

80

100

120

140

Tucson Cincinnati Seattle

Dollars (in Millions)

Ramp Metering Strategies in the Twin Cities 

In the fall of the year 2000, a unique experiment was conducted in the 

Minneapolis/St. Paul (MSP) region of Minnesota to answer questions regarding 

the ability of particular operational strategies to reduce congestion and improve 

travel safety in the region.28 The findings from this experiment provide a tangible example of success in the campaign against congestion. The MSP region was 

one of the first in the nation to deploy the strategy, and since that time the system 

has expanded to be one of the most comprehensive systems in the nation – covering over 430 individual ramps by the year 2000. 

 

28 Cambridge Systematics, Inc., Twin Cities Ramp Meter Evaluation – Final Report, 

Prepared for Minnesota Department of Transportation, February 2001. 

http://www.dot.state.mn.us/rampmeterstudy/pdf/finalreport/finalreport.pdf. 
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Although the DOT maintained a strong belief that the ramp metering strategies 

provided significant benefits, only an isolated number of evaluations had been 

performed and the information on the regionwide impacts was very limited. The 

experiment called for the ramp meters to be turned off for a six-week period in 

order to evaluate how the system operated without ramp meters, as opposed to 

conditions when the meters were operating. 

What Were the Impacts of Turning Off the Ramp Meters? 

• When the ramp meters are operational, they are used to manage the number 

of cars entering the freeway. This lessens congestion on the freeway and 

makes it easier for cars entering the freeway to merge into traffic. 

• When the meters were turned off, cars were able to enter the freeway during 

peak congestion hours without being held on the ramps. This eliminated the 

time previously spent waiting on the ramp signal; however, it also meant that 

more cars were attempting to enter the freeway at the same time. Bottlenecks 

were created when people had more difficulty merging into traffic. This 

slowed traffic on the freeways creating longer freeway travel times. The traffic data showed that the time savings from eliminating the wait on the ramp 

was more than offset by the longer freeway travel times. This observed 

impact was consistent with the reported experiences of individuals participating in the surveys. 

• When the traffic conditions were compared, it was observed that there were 

26 percent fewer crashes on the freeways and ramps when the ramp meters 

were working. The freeways also were observed to operate at higher speeds 

with ramp metering. This resulted in more throughput and reduced travel 

times as a result of ramp metering as shown in Figure 4.4. 

Figure 4.4 Observed Benefits of Ramp Meters in the Twin Cities 

Accidents

-26%

+14%

Throughput

-22%

Travel Time

Note: Throughput is the number of vehicles passing over a stretch of freeway in a given time. 

Usually expressed in vehicles per hour, it indicates that the physical carrying capacity of 

the freeway has been increased due to using ramp meters.

• Travel times also were observed to be nearly twice as predictable when the 

ramp meters were operational compared when they were turned off. 
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• The results from the surveys and focus groups also revealed that most residents felt that the ramp metering system was beneficial to them. Figure 4.5 

shows the responses of survey participants who typically traveled on various 

regional freeway corridors when asked if traffic conditions were better or 

worse when the ramp meters were turned off. These responses show that 

more travelers felt that traffic conditions were worse when the meters were 

turned off than conditions had been when they were operational. The surveys also revealed that approximately 80 percent of the residents supported 

the use of ramp metering in the region following the experiment. 

Figure 4.5 Reported Changes in Traffic Conditions after 

the Shutdown 

0%

10%

20%

30%

40%

50%

60%

70%

I-494 I-35E I-35W I-94 Random

Better

Worse

Percent

I-25/I-225 Interchange Bottleneck Removal in Denver29

The Southeast Corridor has long been recognized as one of the Denver region’s 

highest priority travel corridors. The corridor follows I-25, the only north-south 

freeway in the State, for approximately 14 miles, and I-225, which provides 

access to I-70, the region’s major east-west freeway, for approximately four 

miles. The Southeast Corridor connects the two largest employment centers in 

the region: the Denver Central Business District, with approximately 112,000 

employees in the mid-1990s, and the Southeast Business District, with approximately 120,000 employees in the mid-1990s. With employment centers at both 

ends, the Southeast Corridor is the highest volume, most congested corridor in 

 

29 http://www.trexproject.com/. 
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the region. Located approximately in the middle of the corridor is the I-25/I-225 

interchange. According to Colorado DOT information, I-25 currently experiences 

“severe congestion” for several miles on either side of the interchange, and I-225 

experiences “moderate congestion.” Although several locations in this corridor 

are potential traffic bottlenecks, the I-25/I-225 interchange is a major one. 

Identified as the 14th worst bottleneck in a 1999 study of national bottlenecks, 

Colorado DOT has since undertaken a massive reconstruction project in the I-25 

corridor – nicknamed T-REX (for Transportation Expansion). The project was 

initiated in 2001 with an anticipated completion date of 2006. The T-REX Project 

is one of the most extensive multimodal transportation projects in the history of 

Colorado (Figure 4.6). 

T-REX exhibits of the principles being applied to complex bottleneck mitigation 

projects across the country: a single solution is rarely effective but when multiple strategies are applied, real progress can be made. T-REX is considered by 

CoDOT to be one their next generation of transportation projects. By combining 

light rail, highway, bike, pedestrian and other transit options, a multimodal 

approach is being used to address congestion and safety problems. The T-REX 

project involves: 

Transit Strategies 

• Adding 19 miles of double-track light rail connecting to the existing system at 

Broadway in Denver and extending along the west side of I-25 to Lincoln 

Avenue in Douglas County and in the median of I-225 from I-25 to Parker 

Road in Aurora; 

• Building 13 stations with park-and-rides at 12 of the stations; 

• Adding 34 light rail vehicles to RTD’s fleet; and 

• Constructing a new light rail maintenance facility in Englewood. 

Highway Strategies 

• Adding one through lane in each direction from Logan Street to I-225 (for a 

total of four lanes each way); 

• Adding two through lanes in each direction from I-225 to the C470/E470 

interchange (for a total of five lanes each way); 

• Reconstructing eight interchanges, including I-25/I-225; 

• Reconstructing and widen numerous bridges; 

• Adding and improving shoulders; 

• Improving ramps and acceleration/deceleration lanes; 

• Design and construct a quality project; and 

• Complete the project on or before June 2008. 
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Figure 4.6 The T-Rex Project in Denver, Colorado 

In 1999, it was estimated that the I-25/I-225 interchange causes 11.3 million hours 

of annual delay to motorists. When the proposed improvements are completed 

in 2006, it is estimated that this will be reduced to 2.0 million hours of delay 

without considering the positive effects of the light rail system (level of service 

“D” for the interchange). When light rail is factored in, the potential exists to 

almost entirely reduce delay due to the bottleneck.30

 

30 CoDOT estimates that the peak capacity of the light rail system is 4,500 people per hour 

in one direction. That is equivalent to more than one and half additional lanes of 

highway in one direction! 
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Aggressive Operations 

As part of TTI’s ongoing analysis of congestion trends, deployments of three 

operational treatments were studied for the top 75 urban areas (based on population) in 2001: freeway entrance ramp metering, freeway traffic incident 

management and arterial street signal coordination. These are three important, 

widely used and easily recognized improvements that reduce travel delay and 

improve the reliability of travel conditions. The effects of the three treatments 

also do not overlap, so there is little need to consider double-counting issues. 

The amount of the system treated, the road section congestion level and the 

delay reducing effect of the treatment are considered in the estimates. 

Table 4.3 identifies the deployment characteristics for the three operations treatments. Signal coordination is the most deployed with all of the cities having some 

coordination and an average of half of the street system treated. Traffic incident 

management is deployed in two-thirds of the urban areas and about half of the 

freeway miles within those areas. Entrance ramp metering is the least often 

deployed, with one-third of the areas having some treatment and an average of 

one-quarter of the freeway lane-miles being included in the treated group. 

Table 4.3 2001 Urban Mobility Improvement Techniques 

Existing Operations 

Delay Reduction 

Technique Number of Cities 

Percent of 

System in Cities 

Millions of 

Hours 

Percent of 

Delay 

Freeways 

Ramp Metering 26 23% 73 3.1% 

Incident Management 53 54% 117 5.0% 

Streets 

Signal Coordination 75 54% 16 1.4% 

Total 206 

Source: Schrank, D. and Lomax, T., 2003 Annual Urban Mobility Report, Texas Transportation 

Institute. 

The effect of integrated traffic incident management is the most significant, 

reducing freeway travel delay by 5.0 percent. This includes not only deployment 

of technologies to detect and verify traffic incidents, but more importantly, 

increased coordination and communication between transportation agencies and 

emergency responders. Ramp metering deployment has somewhat less effect on 

overall freeway delay owing to the less extensive implementation. Signal coordination has lower improvement effect for each arterial street, but current levels 

show a 1.4 percent decline in street delay in the 75 areas studied. 

If all the roads were treated with the three strategies, the delay deductions would 

be as shown in Table 4.4. A total of more than 500 million person-hours of delay 
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are estimated for this option, more than double the current operations treatment 

amount. This is equivalent to slightly more than three years of congestion 

growth at the current rate, or just less than 15 percent of total delay. 

Table 4.4 2001 Mobility Improvement Techniques 

Full Deployment of Operations 

 Delay Reduction 

Technique Number of Cities 

Percent of 

System in Cities 

Millions of 

Hours 

Percent of 

Delay 

Freeways 

Ramp Metering 75 100% 270 11% 

Traffic incident 

Management 

75 100% 215 9% 

Streets 

Signal Coordination 75 100% 34 3% 

Total 519 

Source: Schrank, D. and Lomax, T., 2003 Annual Urban Mobility Report, Texas Transportation 

Institute. 

Comparison with System Expansion Alternatives 

Method 1 

The current rate of system expansion in the 75 urban areas is approximately 640 

freeway lane-miles and 890 arterial street lane-miles per year. The best estimate 

we have developed indicates this is about half of what would be needed to stop 

the growth of congestion. This suggests that 1,530 lane-miles would alleviate an 

increase of 150 million hours of delay. 

If the current deployment percentages are increased from the current levels to 

100 percent of both freeways and streets, the role of operational treatments can 

be compared to the system expansion alternatives. The 300 million hours of 

delay difference between current and 100 percent deployment rates, if it were 

allocated over five or 10 years, would suggest an annual rate of decrease between 

30 million and 60 million hours of delay. This would be 20 percent to 40 percent 

of the current congestion growth rate. This might suggest that operational 

treatment deployments would be responsible for the equivalent of 300 to 600 

lane-miles of capacity expansions each year for a total of more than 3,000 lanemiles of freeway and street. 

Method 2 

Another estimate of the operational treatment deployment benefits can be developed by modeling an expansion of the entire road system in the 75 urban areas. 

This analysis adds both freeway and street lanes to the road systems, and performs congestion analyses on the resulting expanded system. Table 4.5 illus-
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trates that a two percent increase in road mileage would come close to matching 

the 300 million hours of delay savings growth between current and 100 percent 

deployment. This is equivalent to 1,590 freeway lane-miles and 1,630 lane-miles 

of arterial streets. 

Table 4.5 2003 Urban Mobility Report Added Roads 

2001 Data 

Percent Added Roadways 

Total Delay 

(Millions of Hours) 

Delay Reduction 

(Millions of Hours) 

Base Condition 3,600 – 

+2% 3,320 280 

+3% 3,190 410 

+4% 3,070 530 

Note: Greater than 530 million hours of delay reduction would require 3,180 Freeway LM and 

3,260 Principal Arterial LM. 

Source: Schrank, D. and Lomax, T., 2003 Annual Urban Mobility Report, Texas Transportation 

Institute. 

Three operational treatments, if deployed on all major roads in 75 urban areas 

would develop a delay savings of more than 500 million hours each year. With 

current delay estimates in the range of 3.5 billion hours, these savings would be 

very significant. To achieve a similar delay reduction with system expansion 

alternatives would require 3,100 freeway lane-miles and 3,200 arterial street lanemiles. The 6,300 lane-mile savings would be a one-time savings and would not 

add to the total each year. But if the operations programs were expanded from 

current levels to 100 percent of the system over the next five to 10 years, they 

would significantly change the congestion growth rate, reducing the projected 

rate of growth by 20 percent to 40 percent per year. 

4.5 SUMMARY

Success against congestion is achievable. Numerous strategies exist in our 

Toolbox to battle congestion. Previous experience suggests that to be effective, 

however, two basic principles should be followed: 

1. Strategies should be targeted at specific problems. Knowing the nature and 

extent of congestion problems in an area or corridor is the first step toward a 

solution; and 

2. Strategies should be used in combination, rather than individually. Because 

many strategies are complementary, using them together provides synergy. 

This is particularly true of operations strategies – when used in conjunction 

with reconstruction, they can provide additional congestion relief by 

targeting other aspects of congestion besides physical capacity (e.g., incidents). In some cases where bottlenecks are a significant problem, there will 

be no getting around the need to add new capacity. 
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5.0 Next Steps 

5.1 HOW CAN EVERYONE PITCH IN AGAINST 

CONGESTION? 

Success against congestion requires not only attacking it on multiple fronts with 

strategies from our Tool Box. It also requires cooperation between transportation 

agencies, businesses, and the public. Since we are all affected congestion, it is 

important that we all work together to address the congestion problem. Here are 

some ways that transportation agencies, businesses, and the public can collaborate to mitigate congestion. 

Take Ownership 

The first step is for all parties to recognize they have a stake in the congestion 

problem. Public agencies are in the business of serving customers the same way 

that any private firm is – except that the customers (the public and businesses) 

are buying efficient and safe travel. The public, elected officials, and businesses 

are more than just consumers – they are shareholders too. These consumers also 

should examine their own decisions and policies to identify changes that can 

improve their quality of life while recognizing that the agencies cannot solve the 

problem by themselves. The ongoing transportation planning process, which has 

been successfully used in major metropolitan areas for the past 40+ years to 

address transportation problems, provides an excellent framework for 

promoting ownership of congestion problems. A major part of the 

transportation planning process is establishing a Vision that outlines what the 

future transportation system should look like. The Vision leads to more specific 

statements of desired actions to achieve these states or characteristics. The Vision 

is also an opportunity to educate all stakeholders on the nature of congestion in 

your area and the importance of mitigating it. 

Identify Where the Congestion Problems and Opportunities Are 

Both technical analyses and anecdotal information from the public are useful in 

identifying where the major congestion problems currently are and what causes 

them. Discuss where the problems are likely to occur in the next five, 10, and 20 

years. The existing transportation planning process in metropolitan areas can be 

tapped as a resource for this purpose. Provide realistic assessments on what can 

reasonably be done in each case, and what the expected improvements might be. 

FHWA supports a wealth of information on expected improvements from 
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operational strategies, such as the ITS Benefits and Cost Database.31 The process 

should include considering: 

• Strategies – What types of treatments should be considered? 

• Coverage – How much area does the treatment cover? 

• Density – How well is congestion treated? 

• Congestion Target – What aspect of congestion is treated? 

• Effect – What is the delay reduction effect? Are there secondary effects, such 

as on safety? What are the spillover effects on other facilities and 

neighborhoods? 

Develop Plans, Programs, Policies, and Projects 

Solutions that effectively address congestion can take a variety of forms, as 

shown in the Tool Box in Section 4.1. Think broadly – no single tool will be 

highly effective against the congestion problem. But when used in combination – 

and tailored to specific circumstances – congestion mitigation strategies can be 

successful. The strategies should be action-based – things we can actually 

accomplish in a reasonable timeframe and at a reasonable cost. Consider all 

types pf strategies including adding new highway and rail capacity, improved 

operations, and better land use planning. For congestion, both immediate and 

long-term actions should be developed. Recognize that many transportation and 

community plans already exist and should be tapped as mechanisms for carrying 

out the Vision. In fact, acting on a list of “things we can do now” will help galvanize support for congestion mitigation over the long term. 

Operate the Transportation System Proactively and Regionally 

Focus on addressing system reliability by targeting capital and operations strategies to specific conditions. Anticipate problems and take corrective actions early. 

Also, regional and multimodal cooperation is key to the success of deploying 

effective operations – many different agencies have a stake in the congestion 

problem. Therefore, a broad perspective should be taken in applying capital and 

operations strategies – avoid a narrow, facility-oriented view. 

Use Performance Measures to Track Progress 

One of the main actions that transportation agencies can contribute to the process 

is the tracking of congestion trends over time. Trends provide a basis for determining how well your actions are working and can identify changes in the 

underlying congestion problem (e.g., traffic incidents may become more 

important in your area). Use of performance measures also brings an element of 

accountability to the process – what we are really getting for our investments – 

 

31 http://www.mitretek.org/its/benecost/BC_Update_2003/index.html. 
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just as private firms do. Several principles may be followed in establishing a performance monitoring program: 

• Sound Information Leads to Sound Decisions. By their nature, operational 

strategies require continuous involvement in the day-to-day, hour-to-hour 

activities of the transportation system. Continuous involvement in the 

transportation system requires feedback at a detailed level so that strategies 

can be adjusted. In the era of TSM&O, we can no longer afford to be “flying 

blind.”

• When You Measure, Measure Like You Mean It. Production of congestion 

trends is a valuable tool for self-assessment and public relations. However, 

to realize its full potential, performance measurement must be taken to the 

next level: active use in decision-making. Once performance measurement is 

embedded in agency culture and procedures, increased attention will be 

focused on the data, yielding higher quality and greater coverage. Evaluate 

projects you’ve done using your measurement process. Determine if the 

project produced the expected improvements in congestion and if not, why 

not? Identify aspects of the project that could be improved next time. 

• Measure Where You Can, Model Everything Else. Performance measurements based on real-time operations data represent the best combination of 

accuracy and detail, but they do not cover all major roads in urban areas. 

However, transportation agencies have many other data and modeling 

resources that could be used in performance monitoring. Do not wait for 

perfect data – start performance monitoring now and improve data as you 

go.

5.2 ACTIVITIES AT THE FEDERAL HIGHWAY 

ADMINISTRATION

Future Reports on Congestion Trends 

This report is the first in a series of planned annual reports on congestion trends, 

effects, and solutions. Several years ago, FHWA embarked on a support and 

outreach program to address the many causes of congestion and to improve 

highway safety through increased use of operational strategies. We are constantly learning more about the basic nature of congestion, where it is going, its 

impacts, and what can be done about it. As we learn more, additional information will be woven into future reports. Some of what we are learning will come 

from programs as outlined below. 

Congestion Monitoring Activities 

Part of the effort to improve support and outreach for operations was 

establishing national-level performance programs. The programs started small 

and have built to the point that enough data exists for enough cities to report 
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trends; this report has presented some of these data. Future reports in this series 

will continue to use these sources. 

The Urban Congestion Report (UCR) effort yields a monthly snapshot of roadway congestion in 10 urban areas and three national composite measures. UCR 

utilizes efficient, automated data collection procedures (colloquially known as 

“screen scraping” or “web mining”) to obtain travel time directly from traveler 

information web sites and archives them at five-minute intervals on the weekdays when these services are available. Concurrent with the travel time data 

collection, other UCR acquisition programs obtain web-based data on weather 

conditions, traffic incidents, and work zone activity. 

The Mobility Monitoring Program (MMP) calculates system performance metrics based on data archived at traffic management centers (TMC). These data are 

highly detailed measurements from roadway surveillance equipment installed 

for operational purposes; data from spot locations (volumes and speeds) are 

used as well as travel time estimates from probe vehicles (where available). For 

each participating city, the MMP develops congestion metrics at both the corridor and area levels; 23 cities participated in 2002 and close to 30 are reporting 

data for 2003. The concepts, performance measures, and data analysis techniques developed and used in the MMP are being considered for adoption and 

implementation by several state and local agencies. 

The Intelligent Transportation Infrastructure Program (ITIP) is an ongoing 

program designed to enhance regional surveillance and traffic management 

capabilities in up to 21 metropolitan areas while developing an ability to measure operating performance and expanding traveler information through a 

public/private partnership involving the FHWA, participating state and local 

transportation agencies, and Mobility Technologies. Under this partnership, 

Mobility Technologies is responsible for deploying and maintaining traffic surveillance devices, and integrating data from these devices with existing traffic 

data to provide a source of consolidated real-time and archived data for the participating metropolitan areas. Deployment has been completed in Philadelphia, 

Pittsburgh, Chicago, and Providence, and is under way in Boston, Tampa, San 

Diego, the Washington D.C. region, Phoenix, Los Angeles and San Francisco. 

Negotiations are currently active in 10 additional cities. 

Congestion Resources and Research 

FHWA continues to develop and compile information for transportation agencies and the public on how improved operations can effectively manage congestion. Table 5.1 provides an overview of these activities, which are organized 

around the components of congestion as identified in this report. By addressing 

congestion by its root causes, both overall congestion levels and reliability are 

targeted. 
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Table 5.1 Selected FHWA Congestion Relief Resources 

Congestion Strategy Action Example Resources 

Traffic Incident Management • NCHRP – Synthesis – Safe and Quick Clearance of Traffic 

Incidents 

• Quick Clearance and “Move-It” Best Practices – I-95 Corridor 

Coalition 

• National Traffic Incident Management Self Assessment 

Work Zone Management • National Traffic and Road Closure Information 

• QuickZone (Traffic Impact Analysis) Tool and Training 

• Work Zone Self-Assessment Tool 

Road Weather Management • Maintenance Decision-Support System Project 

• Best Practices for Road Weather Management 

• Fundamentals of Road Weather Management Training 

Course 

Reducing Non-Recurring 

Congestion 

Special Events Traffic Management • Training course on Managing Travel for Planned Special 

Events 

• Managing Travel for Planned Special Events Handbook 

Freeway Management • Configuration Management for Transportation Management 

Systems 

• Freeway Management and Operations Handbook 

• Freeway Management and Traffic Operations Training Course 

Arterial Management • Access Management, Location and Design Training Course 

• Adaptive Urban Signal Control and Integration (AUSCI) 

Final Evaluation Report 

Corridor Traffic Management • Brochure, primer and handbook on managing and controlling 

traffic between freeways and surface streets 

Reducing Recurring 

Congestion 

Travel Demand Management • HOV Systems Training Course 

• ITS Professional Capacity Building Program 

• TDM Reference Guide – 2004 

• Brochure on Managing Demand Through Traveler Information 

Improving Day-to-Day 

Operations 

Operations Asset Management 

Real-Time Traveler Information 

Traffic Analysis Tools 

• Changeable Message Sign O&M Handbook 

• Resource 511 Web Site 

• Portable Changeable Message Sign Handbook 

• ATIS Standards 

Regional Transportation Operations 

Collaboration and Coordination 

(RTOCC) 

• Guidance on Regional Collaboration 

• Guidance on Linking Planning and Operations 

• Advancing TSM&O Executive Session 

• Advancing TSO&M Course 

• Guidance on Regional Concept for Transportation Operations 

Performance Measurement • Technical guidance and case studies in performance 

measurement 

Creating a Foundation for 

21st Century Operations 

Facilitating Integrated ITS 

Deployment 

• Standards development 

• Architecture compliance 

Freight Analysis • Freight Analysis Framework 

Freight Professional Development • Integrating Freight in the Transportation Planning Process 

Improving Global 

Connectivity by Enhanced 

Freight Management and 

Operations Intermodal Freight Technology 

Truck Size and Weight 

• Cargo*Mate Logistics Information Management 

• Electronic Intermodal Supply Chain Manifest – Freight 

Operational Test Evaluation Final Report 

• Border Wizard 

Emergency Transportation 

Operations (internal) 

Improving Mobility and • Public Safety and Security Program Brochure 

Security through Better 

Emergency Management Emergency Transportation 

Operations (external) 

• FHWA Order 5181.1, Emergency Notification and Reporting 

Procedures 

Source: http://ops.fhwa.dot.gov/. Many more examples are provided by this reference. 
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A. Data Sources 

Data Sources Used in This Report 

This report draws on several current efforts to produce a composite of the 

national congestion picture. These include the following efforts. 

The Urban Mobility Study (UMS) has been in existence since 1982 and is 

sponsored by a consortium of state DOTs and private interest groups.1 The 

study is conducted by the Texas Transportation Institute. The UMS tracks 

congestion patterns in the 75 of the largest metropolitan areas and has been 

instrumental as both a source of trend information and development of the 

concepts and metrics for congestion monitoring, for example, the widely used 

Travel Time Index is an innovation of the UMS. The UMS relies on the Highway 

Performance Monitoring System (HPMS) as it source of information. It uses the 

average annual daily traffic (AADT) and number of lanes data in HPMS as a 

basis for its estimates; these are then translated into congestion metrics using 

predictive equations that have been developed and tested specifically for the 

UMS. Beginning in 2002, the UMS is also considering the positive effects that 

operational strategies have on system performance; these are accounted for as 

adjustments to the base performance predicted by AADT and number of lanes. 

The UMS has widespread visibility both within the transportation profession as 

well as with the general public; annual release of the UMS report generates a 

significant amount of media coverage. 

The Urban Congestion Report (UCR) sponsored by FHWA is an effort that 

yields a monthly snapshot of roadway congestion in 10 urban areas and three 

national composite measures. UCR utilizes efficient, automated data collection 

procedures (colloquially known as “screen scraping” or “web mining”) to obtain 

travel time directly from traveler information web sites and archives them at 

five-minute intervals on the weekdays when these services are available. Since a 

monthly report can be rapidly constructed (within 10 working days) UCR serves 

as an early warning system for changes in urban roadway congestion. 

Concurrent with the travel time data collection, other UCR acquisition programs 

obtain web-based data on weather conditions, traffic incidents, and work zone 

activity. This allows the UCR monthly report to include not only congestion 

level, but a range of possible contributing factors. A one-page overview tells the 

congestion story each month in a graphical manner for the analyst or 

administrator wanting a timely composite overview of congestion trends on a 

month-to-month basis. 

 

1 http://mobility.tamu.edu. 
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The Mobility Monitoring Program (MMP) sponsored by FHWA calculates 

system performance metrics based on data archived at traffic management 

centers (TMCs).2 These data are highly detailed measurements from roadway 

surveillance equipment installed for operational purposes; data from spot 

locations (volumes and speeds) are used as well as travel time estimates from 

probe vehicles (where available). For each participating city, the MMP develops 

congestion metrics at both the corridor and area levels; 23 cities participated in 

2002 and close to 30 will be analyzed for calendar year 2003. Early work from 

this project has provided a basis for measuring travel time reliability; the Buffer 

Index used by the UCR and several state efforts was first defined by the MMP, 

but was based on a concept identified precursor studies to the UCR effort. 

Beginning with 2002, traffic incident data is being collected from TMCs where 

these data exist. Also, continuous traffic data from signalized highways is being 

explored as a potential source for system performance monitoring. The concepts, 

performance measures, and data analysis techniques developed and used in the 

MMP are being considered for adoption and implementation by several state and 

local agencies. A few of these agencies have contacted the project team to 

request technical assistance or additional detailed information on performance 

monitoring or operations data archiving. Specifically, one of the two primary 

objectives of the Mobility Monitoring Program was to provide incentives and 

technical assistance for the implementation of data archiving systems to support 

performance monitoring. Several examples of these technology transfer and 

implementation activities are: 

• Data quality control procedures have been developed for archived TMC data. 

Many locally developed archives are now using these procedures. 

• Customized local analyses have been performed on a selective basis. As a 

way to promote local use of archived data, the MMP team has demonstrated 

how their data may be used to supplement traffic counting programs 

(Phoenix and Cincinnati) and as input to air quality models (Louisville and 

Detroit). 

• A database of TMC-generated data that has been quality controlled and put 

into a standard format is available for research and other FHWA purposes. 

For example, the data are being used now in FHWA’s Estimating the 

Transportation Contribution to Particulate Matter Pollution project and is being 

considered as a validation source for FHWA’s Next Generation Traffic 

Simulation Models project.

The Intelligent Transportation Infrastructure Program (ITIP) is an ongoing 

program designed to enhance regional surveillance and traffic management 

capabilities in up to 21 metropolitan areas while developing an ability to 

measure operating performance and expanding traveler information through a 

 

2 http://mobility.tamu.edu/mmp/. 
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public/private partnership involving the FHWA, participating State and local 

transportation agencies, and Mobility Technologies. Under this partnership, 

Mobility Technologies is responsible for deploying and maintaining traffic 

surveillance devices, and integrating data from these devices with existing traffic 

data to provide a source of consolidated real-time and archived data for the 

participating metropolitan areas. Deployment has been completed in 

Philadelphia, Pittsburgh, Chicago, and Providence, and is under way in Boston, 

Tampa, San Diego, the Washington D.C. region, Phoenix, Los Angeles and San 

Francisco. Negotiations are currently active in 10 additional cities. 

Part of ITIP is the production of performance measures on a routine basis. The 

metrics used to report performance are based on those in the Mobility 

Monitoring Program: annual person-hours of delay, percent congested travel, 

travel rate index, and buffer index. Performance measure reports are to be 

provided to the U.S. DOT on a monthly and annual basis. The monthly reports 

for each metropolitan area will be based on monthly data and will be presented 

with similar content, and in a format, consistent with the “city summary reports”

that are part of the Mobility Monitoring Program. 

The Freight Analysis Framework (FAF) sponsored by FHWA is a tool set 

developed to estimate trade flows on the Nation’s infrastructure, seeking to 

understand the geographic relationships between local flows and the Nation’s 

overall transportation system. The framework will help identify areas of 

improvement to increase freight mobility, including highlighting regions with 

mismatched freight demand and system capacity, and encouraging the 

development of multistate and regional approaches to improving operations. 

The FAF examines transportation for four key intermodal modes: highway, 

railroad, water, and air. A comprehensive database for different modes was 

developed from various government and private sector databases. To evaluate 

the effect of anticipated volumes on the network, the FAF includes economic 

forecasts for the years 2010 and 2020, assigned to the network and linked to 

transportation infrastructure databases. Current work in the FAF concentrates of 

truck flows on the highway system, and this is the information borrowed for this 

Report. 

The Travel Times in Freight Significant Corridors project undertaken by 

FHWA develops truck travel times and other performance measures in major 

intercity corridors that are heavily used by trucks. The study is prototyping the 

measurement of travel times using satellite tracking of selected trucks. Truck 

travel times indicate how well the intercity highway network is performing for 

all travelers. Data from this study can also be used to calibrate network 

assignment models and to understand the level of truck activity by time-of-day. 

The American Highway Users Alliance (AHUA) National Bottleneck Study

was a privately sponsored effort to identify the worst traffic bottlenecks in the 
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Cambridge Systematics, Inc. A-4

country and to estimate the benefits of improving them.3 The study surveyed 

state DOTs to identify their worst bottlenecks, then applied modeling methods 

used by FHWA in the Highway Economic Requirements System model to 

estimate delay, safety impacts, fuel consumption, and emissions. The study also 

examined the effect of improving the bottlenecks, using actual improvement 

plans where available. 

Additional Data Sources 

Other data sources besides those mentioned above can be used to monitor and 

measure congestion. The above sources are focused on measuring congestion 

conditions on highways. Another way to approach congestion measurement is 

to track how users experience entire trips, from their origin to their destination. 

This has been traditionally done through the use of surveys, but emerging 

technologies may allow trip-tracking on a real-time basis. 

At the national level, the National Household Travel Survey (NHTS) provides a 

long history for trends in congestion. The NHTS is the nation’s inventory of 

daily and long-distance travel. The survey includes demographic characteristics 

of households, people, vehicles, and detailed information on daily and longerdistance travel for all purposes by all modes. NHTS survey data are collected 

from a sample of U.S. households and expanded to provide national estimates of 

trips and miles by travel mode, trip purpose, and a host of household attributes. 

The daily travel surveys were conducted in 1969, 1977, 1983, 1990, and 1995. This 

data series provides a rich source of detailed information on personal travel 

patterns in the U.S. Longer-distance travel was collected in 1977 and 1995. The 

2001 NHTS collects both daily and longer-distance trips in one survey. 

 

3 http://www.highways.org. 
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B. State of the Practice: 

Performance Measurement for 

Congestion and Operations 

B.1 TRENDS IN THE USE OF CONGESTION AND 

OPERATIONS PERFORMANCE MEASUREMENT BY 

TRANSPORTATION AGENCIES

The use of performance measures has been growing in recent years, and ranges 

from site-specific operations analysis to corridor-level alternative investments 

analysis and to areawide planning and public information studies. In the past 

few years, the issue of performance monitoring has been elevated by 

transportation agencies to be responsive to the demands of the public and state 

legislatures and the Transportation Equity Act for the 21st Century’s (TEA-21). 

Reauthorization of Federal highway activities is likely to continue this emphasis 

on performance monitoring, particularly with regard to congestion and system 

operations and management. Simultaneously, the deployment of intelligent 

transportation systems (ITS) technologies has the potential to make a vast 

amount of data available for analysis. 

However, many challenges lie ahead before performance measurement becomes 

“standard practice” and is imbedded in the transportation decision-making 

process. These challenges include the following below. 

• The transportation profession is only beginning to define and measure 

performance in objective terms. 

• Based on what data are available, we can observe that congestion is growing 

in areas of every size. 

• Performance must be viewed from several perspectives; both the facility and 

the user perspectives are important, performance measures are useful in both 

planning and operations and homeland security issues must be addressed. 

• The concept of “reliability” is growing in importance. Measuring reliability 

requires continuous data, something that has been in short supply 

traditionally. While advances in performance concepts have been made, data 

limitations hamper their implementation. 

• In the short term, some combination of surveillance data, planning data, and 

modeling must be used to support performance measurement. 
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• Communication of performance monitoring results also is crucial. 

• How performance measures are to be used in the transportation decisionmaking process is still evolving.

Recent research in congestion and operations performance monitoring4 suggests 

eight principles that should be addressed when developing a performance 

measurement program. These are listed below. 

• Principle 1 – Mobility performance measures must be based on the 

measurement of travel time. 

• Principle 2 – Multiple metrics should be used to report performance. 

• Principle 3 – Traditional Highway Capacity Manual-based performance 

measures (V/C ratio and level of service) should not be ignored but should 

serve as supplementary, not primary measures of performance in most cases. 

• Principle 4 – Both vehicle-based and person-based performance measures are 

useful and should be developed, depending on the application. Personbased measures provide a “mode-neutral” way of comparing alternatives. 

• Principle 5 – Both mobility (outcome) and efficiency (output) performance 

measures are required for performance monitoring. Efficiency measures 

should be chosen so that improvements in their values can be linked to 

positive changes in mobility measures. 

• Principle 6 – Customer satisfaction measures should be included with 

quantitative mobility measures for monitoring “outcomes.”

• Principle 7 – Three dimensions of congestion should be tracked with 

mobility performance measures: source of congestion, temporal aspects, and 

spatial detail. 

• Principle 8 – The measurement of reliability is a key aspect of performance 

measurement and reliability metrics should be developed and applied. Use 

of continuous data is the best method for developing reliability metrics, but 

abbreviated methods should also be explored. 

Figure B.15 depicts the relationship between mobility or congestion (outcome) 

measures and operations or efficiency (output) measures. The output measures 

related to operations, which is usually described as the traffic incident 

management process, are at the bottom of the chart. The graphic indicates that 

incident duration, whether nonrecurring or planned, has influence on the 

outcome measures of travel time and throughput shown at the top of the chart. 

 

4 NCHRP 3-68 (Guide for Effective Freeway Performance Measures) Tasks 1 and 2 Draft Report. 

5 NCHRP 20-7(173) (Measuring and Communicating the Effects of Incident Management 

Improvements). 
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One concept that is clear is that performance must be viewed from several 

perspectives. A debate within the transportation profession has arisen over the 

proper perspective for measuring performance. With regard to mobility 

performance, some have suggested that it is the view of the user (traveler) that is 

the most appropriate while others argue it is the view from the facility that is the 

correct perspective. The authors have found this to be a phony argument: both 

perspectives are needed. The user perspective is important because that is how 

the system is experienced by transportation customers; this relates to 

characteristics of users’ trips. The facility perspective is important because 

transportation professionals mainly manage facilities; trips are also managed by 

such strategies as traveler information and demand management but to a lesser 

degree than facilities. 
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Appendix B 

Cambridge Systematics, Inc. B-4 Figure B.1 General Taxonomy of Mobility

-Based Performance Measures 

Overall Freeway 

Mobility Performance

Travel Time* Throughput

Nonrecurring

Congestion

Recurring

Congestion

Emergency

Response

Travel

Time*

Throughput Queue

Characteristics

Non-incidents Incidents Travel

Time*

Throughput

(for each source)

Queue

Characteristics

Travel

Time*

Incidents Weather Special Events

Type Extent Total

Duration

Frequency Lane

Closures

LinearExtent

Component

Times

Notification Arrival Clearance Queue Dissipation

Work Zones

Total Duration

* Travel time refers to any of the

travel time-based metricsdiscussed in this report.
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B.2 CHALLENGES AHEAD

B.2.1 Data 

In order to gain a better understanding of the current state of congestion and 

trends over time, additional analysis is required. Some of the most significant 

challenges to this effort are related to the availability, coverage, quality, and 

consistency of traffic data across the nation. This section examines each of these 

challenges. Table B.1 describes what challenges are created by these issues. 

Subsequent sections address these challenges in more detail. 

Table B.1 Potential Challenges to Accurately Assessing Congestion 

Issue Why Is It a Problem? 

Availability • Continuous streams of data are not readily available in many 

regions. The snapshot nature of data availability makes it 

difficult to analyze conditions during unique events or over time. 

Coverage • Data is only available for a portion of the transportation network. 

Therefore, it is difficult to accurately assess the entire impacts 

of congestion. 

Quality • Data sets often contain erroneous data or have gaps of missing 

data. The data sets need significant cleaning before they can 

be used and accuracy is compromised. 

Standards • Data is not consistently collected, analyzed, and stored across 

different regions, and often times within the same region. 

Standardization is needed to provide for the meaningful 

comparison of conditions in different regions. 

Availability 

Continuous streams of data, covering all periods and conditions, need to be 

made available to properly assess these conditions and allow for meaningful 

comparison of trends over time. However, data simply isn’t available to conduct 

many analysis, and even when it is has been collected, there are often problems 

that make the data unsuitable. 

Traffic data has historically been collected on a periodic basis providing snapshot 

views of congestion. Transportation planners have often planned data collection 

activities to avoid special events, inclement weather, and traffic incidents to 

provide information of conditions representative of a “normal” day. This 

provides an incomplete picture of the full range and characteristics of congestion. 

Even in areas that have continuous data collection capabilities built into their 

traffic management programs, specific data may be difficult to obtain. Many 

traffic management centers simply “spool off” the collected data for storage, with 

no real data management plan. The large files that are created make the data 

difficult to work with or inaccessible in many cases. 
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A potential solution to this challenge is the development of formal Archived 

Data Management Systems (ADMSs), which are currently under development in 

many regions around the country. ADMSs take a more formal approach to 

archiving data and making them accessible to a variety of users. 

Coverage 

The limited coverage of performance measurement restricts the usefulness of the 

data. Data coverage in many areas is limited to particular jurisdictions or 

facilities. Often, monitoring coverage is limited to several freeway corridors. 

This requires the analyst to interpolate performance measures for parts of the 

system that are not covered which increases the possibility of introducing errors 

to the data, limiting its accuracy. This partial coverage does not provide a 

complete picture of the nature and impacts of congestion. 

Greater data coverage is needed to provide a greater understanding of the full 

impacts of congestion. Fortunately, many initiatives are underway to increase 

the coverage by introducing performance monitoring to new jurisdictions, 

increasing the freeway coverage in existing jurisdictions, and expanding 

coverage to include signalized arterials and public transportation systems. The 

expansion of coverage of monitoring activities will increasingly provide a more 

accurate picture of the full nature of congestion. 

Quality 

The quality of data sets in many locals is often inadequate to perform meaningful 

assessments of congestion. If not corrected, these data errors can result in 

inaccurate measurement of congestion. 

The errors in the data sets can be caused by a number of sources including 

improperly calibrated or poorly maintained field equipment, and the lack of 

formal data management systems and processes. There is often very limited 

funding and resources for these critical tasks. 

These data quality problems can be alleviated or minimized through data 

cleaning and validation, increased data checking and quality control and the 

development of more formal data archiving and management programs. These 

activities will require that more resources and funding be provided to support 

these activities. 

Standards 

The lack of standards present problems for analyst attempting to compare 

different regions or identify trends. Different jurisdictions and agencies collect, 

analyze, and archive data differently based on their own needs. For example, 

traffic incident data in a region may be collected by a number of different 

agencies responsible for responding to traffic incidents (e.g., Fire Department, 

State Highway Patrol, Transportation Authority, or others). Each of these 

agencies may collect different data on the incidents to which they respond. This 
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Cambridge Systematics, Inc. B-7

lack of standardization limits the meaningful comparison of the data between 

agencies. 

Further, there is currently little consensus of the analysis methods and 

performance measures used to assess congestion on a national basis. Different 

regions often monitor and analyze different performance measures, and archive 

data in different formats than used in other regions. This creates difficulties in 

tracking trends and comparing performance between different regions. 

Initiatives to develop standards for archived data are gaining momentum. The 

success of these initiatives in promoting the adoption of standardization will 

provide for more meaningful analysis, especially in the comparison of trends 

across different regions. 

B.2.2 Modeling versus Measurement 

Most transportation agencies utilize some sort of modeling to analyze 

congestion. These models may be used to enhance field data measurement by 

providing predicative capabilities, or may be used in place of field data 

measurement when data is unavailable due to the challenges presented earlier. 

Recent advances in data management technology has provided improvements in 

the accuracy, functionality, and usefulness of both modeling and measurement 

processes. Future advances will likely provide further opportunities for 

improvement and integration of these tools. 

When Should They Be Used? 

A common rule of thumb that is suggested in analyzing congestion is “Measure 

where you can, model everything else.” This recognizes that measurement using 

operations data often represents the best combination of accuracy and detail. 

However, the use of measurement data is also not feasible due to lack of 

availability, coverage, quality, or standardization. In these situations, modeling 

may be the better option. In using one or both of the analysis processes it is 

important to understand that modeling and measurement each have their own 

relative strengths and weaknesses. In general: 

• Modeling provides an estimate of what would likely happen as a result of a 

particular change in the system assuming that individuals reacted similarly 

to past behaviors. 

• Measurement provides an accurate assessment of what has happened or what 

is happening (for real-time systems), but has less ability to draw conclusions 

about what will happen. 

Table B.2 provides additional detail on the relative advantages and limitations of 

these two approaches to analyzing congestion. 
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Table B.2 Comparison of the Relative Advantages and Limitations of 

Modeling Versus Measurement 

Advantages Limitations 

Modeling • Provides predictive capabilities. 

• Once developed, can provide rapid 

analysis of multiple scenarios. 

• Can be developed to provide microor macro-level analysis. 

• Technology advances in data 

management are providing for more 

advanced and accurate models. 

• Only as good as the data used 

to develop the models. 

• Provides only a simulation of 

what is happening in the realworld. Results must be 

validated against observed 

data. 

• Difficult to predict travelers 

reactions to unique conditions 

or innovative strategies. 

• Can be costly to develop initial 

models. 

Measurement • Provides a more accurate 

assessment of what is happening on 

the ground. 

• Can be used to analyze traveler 

reactions to specific conditions or 

unique events. 

• Technology advances in data 

collection and better data 

management are providing 

improved measurements. 

• Data availability and quality 

issues may limit usefulness of 

the data. 

• Can be costly to implement 

extensive data collection 

programs or systems. 

Since models are based on observed behaviors, they are most accurate when 

analyzing predictable conditions. Utilizing models to analyze extreme 

conditions, innovative operations strategies, or situations where traveler 

behaviors would be unpredictable is less advised. When the traffic conditions 

are extremely unpredictable, modeling should only be used if measurement is 

cost-prohibitive. 

Figure B.2 shows the tradeoffs between the relative cost of the analysis and the 

conditions being analyzed, demonstrating the general areas of strength for both 

models and measurement. 
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Figure B.2 Modeling Versus Measurement – When Should They Be Used? 

Are Modeling and Measurement Processes Compatible? 

Many agencies still view modeling and measurement as mutually exclusive 

processes with different end uses; however, many progressive agencies are 

increasingly integrating the processes to provide even more powerful tools for 

analyzing congestion. 

Examples of the benefits that can be achieved through the integration of 

measurement and models include: 

• Data sets obtained through measurement can be used in the development 

and validation of models; 

• Models can be tied to real-time data measurement to add the capability to 

predict future conditions based on current real-world conditions; 

• Models can be used to extrapolate localized measurement data to a regional 

scale; and 

• Data generated by models can also be used to provide sensitivity testing as a 

reality check on measurement tools and data sets in order to help identify 

potentially erroneous data or alert personnel of inoperative data collection 

equipment. 
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B.3 WHAT WE HAVE LEARNED

The trends and lessons learned presented in this section were originally 

presented in the report, Congestion and System Performance Activities at FHWA: 

Trends, Lessons, and Future Direction.

Traffic Data Trends 

Real-time traffic data collection and archiving processes have been developed 

independently in most of the cities and the details of these processes vary among 

the cities. As a general rule, TMCs at least have the capability to archive data 

from their surveillance systems. In a few cases, this capability is not used 

because of priorities elsewhere in the TMC, but it is clear that TMC software is 

being constructed with archiving as a function. However, the state of the 

practice in TMC archiving is still fairly primitive. The most common practice is 

to transfer the data to a storage device where they reside in simple file formats 

without an active information management system. Quality control is rarely 

performed at this level and access to the data is provided on a case-by-case basis 

without the benefit of a query or reporting structure – data are simply provided 

in whatever file formats are used to store them. 

There are several process steps that are relatively common to nearly all cities. 

The data collection and archiving process typically includes the following steps: 

• Data are collected by traffic sensors and accumulated in roadside controllers. 

These field measurements are collected for each individual lane of traffic. At 

20-second to two-minute intervals, the roadside controllers transmit the data 

to a central location, typically a TMC; 

• Some cities perform quality control on field-collected data, but this checking 

is simple and based on minimum- and maximum-range value thresholds; 

• Cities that use single inductance loop detectors as sensors can measure only 

volumes and lane occupancies directly. In these cases, speed estimation 

algorithms are used to compute speeds from volumes and lane occupancies. 

These speed estimation algorithms vary among cities; 

• Internal processes at the TMC aggregate the traffic data to specified time 

intervals for archival purposes. These time intervals vary from 20 seconds 

(no aggregation) to 15 minutes. In some cases, the data are also aggregated 

across all lanes in a given direction at a sensor location; and 

• The aggregated data are then stored in text files or databases unique to each 

TMC. CDs are routinely created at the TMCs to offload some of the storage 

burden and to satisfy outside requests for the data. 

Calibration and maintenance of field equipment and communications are 

universal problems. The main impediment is lack of resources to devote to these 

tasks; TMC budgets are limited and must be used to address a multitude of 

issues. Calibration – at least to very tight tolerances – is not seen as a priority, 
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given that operators focus on a broad range of operating conditions rather than 

precise volume and speed measurements. Or in some cases traffic managers 

may be willing to accept a certain level of data quality to satisfy only their 

current operations applications. This philosophy may be changing as a result of 

more stringent data requirements for traveler information purposes (e.g., travel 

time messages on variable message signs). However, we found the current data 

resolution used by TMCs to be quite coarse for supporting their traditional 

operations activities, such as traffic incident detection and ramp meter control. 

Maintenance is a problem (due primarily to funding limitations) even when 

loops are known to be producing erroneous or no data. The problem is 

exacerbated where loops are used because most agencies are reluctant to shut 

down traffic on heavily traveled freeways just for loop repair. This is not to say 

that faulty loops are never repaired, but maintenance is often postponed to 

coincide with other roadway activities, which helps spread the cost burden as 

well. 

Field checking of sensors is done periodically but no standardized procedures 

are used across all cities. If a detector is producing values that are clearly out of 

range, inspection and maintenance are usually performed. However, calibration 

to a known standard is rarely, if ever, performed. This means that more subtle 

errors may go undetected. Bearing in mind that TMCs typically do not require 

highly accurate data for most of their operations, this approach is reasonable and 

practical. Work zones exacerbate these problems and often contractors 

unknowingly sever communication lines or pave over inductance loops. 

Traffic Incident Data and Other Event Data Trends 

Archiving of traffic incident data is becoming more prevalent at TMCs. 

However, the nature of the data collected and the structure of the storage formats 

are extremely diverse. This is a larger problem than for traffic data, where the 

basic measurements are fairly well known and understood. By comparison, even 

the definition of an “incident” is subject to interpretation. The resulting 

inconsistency in reporting formats for traffic incidents limits analysis 

opportunities. 

The UCR has gained some experience and insights using traffic incident, 

weather, and work zone event data. For example, the month of February 2002 

featured a number of significant snowstorms in the northeast, affecting four of 

the 10 UCR cities (Pittsburgh, Philadelphia, Boston, and Cincinnati). The 

February storms featured large snowfall totals that rendered roadways largely 

impassable and suppressed travel demand for several days. The resulting UCR 

measures in these cities showed briefer periods of less intense congestion than on 

a typical workday in the days just following snowstorms. After the local roads 

had been cleared, however, and access to freeways opened, congestion was 

particularly long-lasting throughout the day and more intense than on a typical 

workday. The snowstorm example shows how weather and travel time data can 
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Cambridge Systematics, Inc. B-12

be utilized jointly to provide insight on the mobility impacts of weather or other 

capacity-reducing events. 

Data collected by UCR and MMP processes during traffic incident conditions 

tend to diminish the effect of the traffic incidents, while the UMS process 

incorporates a relatively simple estimate for the effect of traffic incidents. Just as 

in the snowstorm conditions, trips divert from the freeway mainlines when a 

traffic incident occurs. This diversion frequently results in traffic demand going 

to a major street that is not monitored by the data collection equipment. The 

effect of this diversion is to decrease the volume of traffic that appears to be 

affected by the traffic incident, and also decreases the amount of extra delay 

associated with the traffic incident. Until data is collected for more of the system, 

this problem will affect delay and traffic condition assessments. 

Local Use of Congestion Performance Measures and Archived Data 

As mentioned above, nearly all TMCs “spool off” their traffic data for storage, 

but formal data management rarely occurs. The resulting files are very large and 

relatively inaccessible, limiting the use of the data in many applications. 

However, several formal Archived Data Management Systems (ADMSs) are 

under development around the country. ADMSs take a more formal approach to 

archiving data and making them accessible to a variety of users. A variety of 

government and even private agencies are involved in ADMS development and 

operation. Universities are heavily represented in this category, but state DOTs 

and some metropolitan planning organizations (MPO) are also involved. 

In addition to archiving ITS-generated data, many states and MPOs have 

embraced the concept of performance measurement. This trend is developed a 

substantial amount of inertia and can no longer be seen as theoretical – 

transportation agencies are imbedding performance measurement into their dayto-day activities. Examples include: 

• Arizona – Both the Arizona Department of Transportation (ADOT) and the 

Maricopa Association of Governments (MAG) are supporting performance 

monitoring programs. ADOT has folded the implementation of a scaled 

down CMS (based on the MMP’s reliability index) into the Arizona state 

transportation plan (MoveAZ Plan). 

• Minnesota – The Minnesota Department of Transportation (MnDOT) is 

studying adoption of the primary performance measures in MMP, namely 

the travel time index as a mobility measure and the buffer time index as a 

reliability measure. The MMP team has worked with both MnDOT staff and 

their consultants as they have performed demonstration and feasibility 

projects for implementing these measures. 

• Oregon – The Oregon Department of Transportation (ODOT) Traffic 

Management Section is studying several of the measures used in the MMP 

reports. TTI is providing technical assistance as they conduct a 

demonstration project to study the travel time index, the buffer time index 
Traffic Congestion and Reliability: Linking Solutions to Problems 

Appendix B 

Cambridge Systematics, Inc. B-13

and other measures for local and statewide implementation. The goal is to 

use the archived data in combination with other, more widely available data 

to construct a method to evaluate operations on the entire roadway network. 

• California – The California Department of Transportation (Caltrans), with 

the technical support of the University of California-Berkeley, is in the 

process of developing and integrating a statewide data archive and 

performance monitoring system called PeMS (Freeway Performance 

Measurement System). The PeMS program has supplied data for Los 

Angeles for 2000 and 2001. 

• Virginia – The University of Virginia and the Virginia Transportation 

Research Council (VTRC), the research arm of Virginia DOT, have been 

designated the official data archive managers for the State of Virginia. Their 

staff has supplied data from Hampton Roads and Northern Virginia to the 

MMP team. Additionally, they have conducted several feasibility studies of 

the performance measures used in the MMP reports and are considering 

adoption of some of the mobility and reliability measures. 

• Washington – The Washington State DOT has a research and 

implementation effort to develop a set of mobility performance measures. 

The University of Washington, the primary analyst of archived data for 

WsDOT, is conducting the research and produces one of the premier annual 

congestion performance reports in the country. 
Traffic Congestion and Reliability: Linking Solutions to Problems 

Appendix C 

Cambridge Systematics, Inc. C-1

C. Delay and Reliability Measures 

Travel time index (TTI) is a comparison between the travel conditions in the 

peak period to free-flow conditions. It uses the units of travel rate due to the 

ease of mathematical calculation and due to the data elements included in the 

MMP database. The TTI could also use direct travel time comparisons for trips 

of the same length. The equation below presents the calculation of the travel 

time index. The travel time index is also similar to the travel rate index (TRI) 

used in the Texas Transportation Institute’s Annual Mobility Report. The TRI 

only includes the effect of recurring congestion, while the TTI includes recurring 

and nonrecurring congestion – the conditions measured with continuous data 

collection equipment. 

⎟

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+

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⎠

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+ ×

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⎠

⎞

⎜

⎜

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×

=

Peak Period VMT

ArterialStreet

Principal

VMT

Peak Period

Freeway

VMT

Peak Period

ArterialStreet

Principal

Free - flow Rate

ArterialStreet

Principal

TravelRate

ArterialStreet

Principal

VMT

Period

Peak

Freeway

Rate

Free - flow

Freeway

TravelRate

Freeway

Time Index

Travel

The index can be applied to various system elements with different free-flow 

speeds. The travel time index compares measured travel rates to free flow 

conditions for any combination of freeways and streets. Index values can be 

related to the general public as an indicator of the length of extra time spent in 

the transportation system during a trip. 

The Buffer Time Index (BTI) expresses the amount of extra “buffer” time needed 

to be on-time 95 percent of the time (late one day per month). Indexing the 

measure provides a time and distance neutral measure, but the actual minute 

values could be used by an individual traveler for a particular trip length. The 

index is calculated for each road segment and a weighted average is calculated 

using vehicle-miles of travel as the weighting factor. 

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⎥

⎥

⎥

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⎢

⎢

⎢

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= × 100%

(in minutes per mile)

Average Travel Rate

(in minutes per mile)

Average Travel Rate - (in minutes per mile)

95th Percentile Travel Rate

(Using VMT)

AllSections

Average of

Weighted

Time Index

Buffer

The Planning Time Index (PTI) is simply the 95th percentile travel time index. It 

is used as a supplemental measure for reliability. Because reliability is related to 

the distribution of travel rates, the 95th percentile indicates an excessively high 

travel rate, one that only five percent of all travel rates exceed for the time period 

under consideration. 
Traffic Congestion and Reliability: Linking Solutions to Problems 

Appendix D 

Cambridge Systematics, Inc. D-1

D. City Level Trends in 

Reliability 

To supplement the TTI analysis, we have conducted a series of case studies using 

data from the Mobility Monitoring Program for four cities: Los Angeles, 

Minneapolis, Seattle, and Atlanta. These data come from instrumented freeways 

which tend to be the higher congestion locations. Figures D.1 through D.4 

present congestion trends for the period between 2000 and 2002. In these figures, 

daily statistics (“travel time” and “planning time”) are shown as the faint and 

highly variable background lines. (Planning time is the 95th percentile travel 

time, a measure of reliability.) Monthly summaries are also shown as means to 

determine long-term trends. 

• Minneapolis showed a sharp upturn in both base congestion level and 

unreliability toward the end of 2000. Beginning in October, an experiment 

was conducted there to examine the effect of ramp metering by turning the 

meters off – this led to worsening of congestion and reliability. In December, 

the area was also hit with a series of winter storms. From 2001 to 2002, 

congestion eased very slightly. 

• Atlanta has shown a steady increase in congestion and unreliable travel times 

from 2002 to 2002. Some of this may be the fact that the surveillance system 

has expanded to new highways over the period, but the base system covered 

in 2000 already includes some of the worst areas (e.g., the central Atlanta 

section discussed in Section 2.0). 

• Congestion and unreliable travel times are on the increase in Los Angeles. 

• Congestion in Seattle was relatively flat for the period 2000-2002. However, 

an interesting seasonal pattern is evident: congestion and unreliable travel 

times are lower early in the year and increase toward the end of the year. 

Another way to measure reliability is to consider the Buffer Index computed for 

the entire year (Figure D.5). Except in Minneapolis – where the effect of the 

“ramp meter shutdown” and winter storms in late 2000 caused extreme 

unreliability – the cities show a slow increase in unreliable travel times from 2000 

to 2002. 
Traffic Congestion and Reliability: Linking Solutions to Problems 

Appendix D 

Cambridge Systematics, Inc. D-2

Figure D.1 Congestion Trends on Minneapolis Freeways 

1.00

1.20

1.40

1.60

1.80

2.00

2.20

2.40

2.60

2.80

3.00

Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov

Day of Year (weekdays, non-holidays only)

Index Value

Travel Time Planning Time Monthly Travel Time Monthly Planning Time

2000

190 freeway centerline-miles

2001

 190 freeway centerline-miles

2002

 190 freeway centerline-miles

Source: Analysis of data from FHWA’s Mobility Monitoring Program.

Figure D.2 Congestion Trends on Atlanta Freeways

1.00

1.20

1.40

1.60

1.80

2.00

2.20

2.40

Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov

Day of Year (weekdays, non-holidays only)

Index Value

Travel Time Planning Time Monthly Travel Time Monthly Planning Time

2000

40 freeway centerline-miles

2001

 53 freeway centerline-miles

2002

 73 freeway centerline-miles

Source: Analysis of data from FHWA’s Mobility Monitoring Program.
Traffic Congestion and Reliability: Linking Solutions to Problems 

Appendix D 

Cambridge Systematics, Inc. D-3

Figure D.3 Congestion Trends on Los Angeles Freeways

1.00

1.20

1.40

1.60

1.80

2.00

2.20

2.40

2.60

2.80

Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov

Day of Year (weekdays, non-holidays only)

Index Value

Travel Time Planning Time Monthly Travel Time Monthly Planning Time

2000

192 freeway centerline-miles

2001

 577 freeway centerline-miles

2002

 577 freeway centerline-miles

Source: Analysis of data from FHWA’s Mobility Monitoring Program.

Figure D.4 Congestion Trends on Seattle Freeways

1.00

1.10

1.20

1.30

1.40

1.50

1.60

1.70

1.80

1.90

2.00

Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov

Day of Year (weekdays, non-holidays only)

Index Value

Travel Time Planning Time Monthly Travel Time Monthly Planning Time

2000

68 freeway centerline-miles

2001

 116 freeway centerline-miles

2002

 116 freeway centerline-miles

Source: Analysis of data from FHWA’s Mobility Monitoring Program.
Traffic Congestion and Reliability: Linking Solutions to Problems 

Appendix D 

Cambridge Systematics, Inc. D-4

Figure D.5 Reliability Trends in Four Cities 

2000-2002 

gy ,

25%

46%

72%

28%

39%

44% 45%

29%

34%

49%

41%

31%

0%

10%

20%

30%

40%

50%

60%

70%

80%

Atlanta Los Angeles Minn-St. Paul Seattle

Buffer Index

2000 2001 2002

 Source: Analysis of data from FHWA’s Mobility Monitoring Program.