Archive – Urban Mobility Reports: 1999-2019

CAUTION: Do not compare data or performance measures from different reports to identify trends.

Reports by Year:

2019
2015
2012
2011
2010
2009
2007
2005
2004
2003
2002
2001
1999

The methodology used for the Urban Mobility Reports and the data obtained from the Federal Highway Administration and the state Departments of Transportation have changed almost every year. The Report is an evolving effort; one goal is to provide the best estimate of congestion conditions and trends in every report. As such there have been several different versions of the information. The most recent report always includes the best estimates for the report year and all previous years. Data in previous reports may not match with current estimates and readers are cautioned to avoid comparing data or performance measures from two different reports to identify trends.

Some researchers have asked for access to previous reports, however, and we are happy to provide those. The links below provide you with information about the changes for particular years and the previous reports as they were published.

TTI’s 2019 Urban Mobility Report

Methodology Changes for the 2019 Urban Mobility Report

There are several methodology changes to the 2019 Urban Mobility Report. The largest changes have to do with the reliability measure (Planning Time Index), estimates of daily truck volumes, and the ever-increasing INRIX speed dataset. These changes are documented in more detail in the following sections of the Methodology. Here are brief summaries of what has changed:

Estimates of truck volume and truck travel speeds were expanded. This provides more information using the much more detailed truck speed data.
The measure of travel time variation from day-to-day now uses a more representative trip-based process. The Planning Time Index (PTI) is based on the idea that travelers want to be on-time for an important trip 19 out of 20 times; so one would be late to work only one day per month (on-time for 19 out of the 20 work days each month). For example, a PTI value of 1.60 indicates that a traveler should allow 32 minutes to make an important trip that takes 20 minutes in low traffic volumes (1.60 x 20).
Speeds supplied by INRIX are collected every 15-minutes from a variety of sources every day of the year on most major roads. The speeds are calculated by matching successive observations and calculating the speed using the time and distance covered between the two points. Many of the slow speeds formerly considered “too slow to be a valid observation” using instantaneous speed observations are now being retained in the INRIX dataset with more experience. Larger travel speed sample sizes have increased the confidence in the data.
The average vehicle occupancy found in the American Community Survey (3) data has increased since the 2008/9 economic recession. The UMR adjusts to this trend by increasing the average vehicle occupancy rate from the previous value of 1.25 persons per vehicle to the new value of 1.50 over the period from 2009 to 2012.

TTI’s 2019 Urban Mobility Report

Appendices

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TTI’s 2015 Urban Mobility Scorecard

What’s New

There are several changes to the UMS methodology for the 2015 Urban Mobility Scorecard. The largest changes have to do with the reliability measure (Planning Time Index), estimates of daily truck volumes, and the ever-increasing INRIX speed data set size. These changes are documented in more detail in the following sections of the Methodology. Here are brief summaries of what has changed:

Estimates of hourly truck volume were developed and incorporated. In past reports, trucks were assumed to have the same patterns as car travel.
The measure of the variation in travel time from day-to-day now uses a more representative trip-based process rather than the old dataset that used individual road links. The Planning Time Index (PTI) is based on the ideas that travelers want to be on-time for an important trip 19 out of 20 times; so one would be late to work only one day per month (on-time for 19 out of the 20 work days each month). For example, a PTI value of 1.80 indicates that a traveler should allow 36 minutes to make an important trip that takes 20 minutes in low traffic volumes.
Speeds supplied by INRIX are collected every 15-minutes from a variety of sources every day of the year on most major roads. Many of the slow speeds formerly considered “too slow to be a valid observation” are now being retained in the INRIX dataset. Experience and increased travel speed sample sizes have increased the confidence in the data.

TTI’s 2015 Urban Mobility Scorecard
TTI’s 2015 Urban Mobility Scorecard – with Appendix

CAUTION: Do not compare data or performance measures from different reports to identify trends (more details above).

TTI’s 2012 Urban Mobility Report

Key Aspects of the 2012 UMR

Speeds collected every 15-minutes from a variety of sources every day of the year on most major roads are used in the study.
The data for all 24 hours makes it possible to track congestion problems for the midday, overnight and weekend time periods.
A measure of the variation in travel time from day-to-day is introduced. The Planning Time Index (PTI) is based on the idea that travelers would want to be on-time for an important trip 19 out of 20 times; so one would be late only one day per month (on-time for 19 out of 20 work days each month). A PTI value of 3.00 indicates that a traveler should allow 60 minutes to make an important trip that takes 20 minutes in uncongested traffic. In essence, the 19th worst commute is affected by crashes, weather, special events, and other causes of unreliable travel and can be improved by a range of transportation improvement strategies.
Truck freight congestion is explored in more detail thanks to research funding from the National Center for Freight and Infrastructure Research and Education (CFIRE) at the University of Wisconsin.
Additional carbon dioxide (CO₂) greenhouse gas emissions due to congestion are included for the first time thanks to research funding from CFIRE and collaboration with researchers at the Energy Institute at the University of Wisconsin-Madison. The procedure is based on the Environmental Protection Agency’s Motor Vehicle Emission Simulator (MOVES) modeling procedure.
Wasted fuel is estimated using the additional carbon dioxide greenhouse gas emissions due to congestion for each urban area. For the first time, this method allows for consideration of urban area climate in emissions and fuel consumption calculations.

TTI’s 2012 Urban Mobility Report
TTI’s 2012 Urban Mobility Report – with Appendices

CAUTION: Do not compare data or performance measures from different reports to identify trends (more details above).

TTI’s 2011 Urban Mobility Report

Key Aspects of the 2011 Report

Hour-by-hour speeds collected from a variety of sources on every day of the year on most major roads are used in the 101 detailed study areas and the 338 other urban areas.
The data for all 24 hours makes it possible to track congestion problems for the midday, overnight and weekend time periods.
Truck freight congestion is explored in more detail thanks to research funding from the National Center for Freight and Infrastructure Research and Education (CFIRE) at the University of Wisconsin.
A new wasted fuel estimation process was developed to use the more detailed speed data. The procedure is based on the Environmental Protection Agency’s new modeling procedure-Motor Vehicle Emission Simulator (MOVES). While this model does not capture the second-to-second variations in fuel consumption due to stop-and-go driving, it, along with the INRIX hourly speed data, provides a better estimate than previous procedures based on average daily traffic speeds.
One new congestion measure is debuted in the 2011 Urban Mobility Report. Total travel time is the sum of delay time and free-flow travel time. It estimates the amount of time spent on the road.

TTI’s 2011 Urban Mobility Report – with Appendices

CAUTION: Do not compare data or performance measures from different reports to identify trends (more details above).

TTI’s 2010 Urban Mobility Report

What’s New

This Urban Mobility Report begins an exciting new era for comprehensive national congestion measurement. Traffic speed data from INRIX, a leading private sector provider of travel time information for travelers and shippers, is combined with the traffic volume data from the states to provide a much better and more detailed picture of the problems facing urban travelers. Previous reports in this series have included more than a dozen significant methodology improvements. This year’s report is the most remarkable “game changer;” the new data address the biggest shortcoming of previous reports.

INRIX anonymously collects traffic speed data from personal trips, commercial delivery vehicle fleets and a range of other agencies and companies and compiles them into an average speed profile for most major roads. The data show conditions for every day of the year and include the effect of weather problems, traffic crashes, special events, holidays, work zones and the other congestion causing (and reducing) elements of today’s traffic problems. TTI combined these speeds with detailed traffic volume data to present an unprecedented estimate of the scale, scope and patterns of the congestion problem in urban America.

The new data and analysis changes the way the mobility information can be presented and how the problems are evaluated. The changes for the 2010 report are summarized below.

Hour-by-hour speeds collected from a variety of sources on every day of the year on most major roads are used in the 101 detailed study areas and the 338 other urban areas.
An improved speed estimation process was built from the new data for major roads without detailed speed data.
The data for all 24 hours makes it possible to track congestion problems for the midday, overnight and weekend time periods.
A revised congestion trend has been constructed for each urban region from 1982 to 2009 using the new data as the benchmark. Many values from previous reports have been changed to provide a more accurate picture of the likely patterns.
Did we say 101 areas? Yes, 11 new urban regions have been added, including San Juan, Puerto Rico. All of the urban areas with populations above 500,000 persons are included in the detailed area analysis of the 2010 Urban Mobility Report.
Three new measures of congestion are calculated for the 2010 report from the TTI-INRIX dataset. These are possible because we have a much better estimate about when and where delay occurs.
- Delay per auto commuter – the extra travel time faced each year by drivers and passengers of private vehicles who typically travel in the peak periods.
- Delay per non-peak traveler – the extra travel time experienced each year by those who travel in the midday, overnight or on weekends.
- Commuter Stress Index (CSI) – similar to the Travel Time Index, but calculated for the worst direction in each peak period to show the time penalty to those who travel in the peak directions.
Truck freight congestion is explored in more detail thanks to research funding from the National Center for Freight and Infrastructure Research and Education (CFIRE) at the University of Wisconsin.

TTI’s 2010 Urban Mobility Report
TTI’s 2010 Urban Mobility Report (Chinese)
TTI’s 2010 Urban Mobility Report – with Appendices

CAUTION: Do not compare data or performance measures from different reports to identify trends (more details above).

2009 Urban Mobility Report

What’s New

Each Urban Mobility Report reviews procedures, processes, and data used to develop the best estimates of the costs and challenges of traffic congestion, improving them when possible. The methodology was revised in 2008/9 to improve the public transportation methodology. In addition, the benefits from operations treatments were estimated throughout the extent of the study database to improve the relevance of the long-term trends. This caused some numbers from previous reports to change. All of the congestion statistics in the 2009 Urban Mobility Report have been revised using the new calculation procedures for all years from 1982 so that true trends can be identified.

Changes to Congestion Methodology – Highlights

Public transportation – An improved method for transferring riders back into the roadway network to simulate the effect of eliminating public transportation service resulted in larger delay reduction benefits in the 2009 report. The new methodology was reapplied for all previous years as well. Improvements include using the transit modes in each region to determine the peak travel mileage and alternative routes.
Operations benefits – The 2009 report estimates the benefits from programs that reduce congestion without adding roadway lanes for every year since 1982. Previous reports included these programs only since 2000. There are fewer data for the pre-2000 period, but general trend information and project-specific reports were used to smooth out what had been a disruptive element in the urban area congestion trends.

2009 Urban Mobility Report
2009 Urban Mobility Report – with Appendices

CAUTION: Do not compare data or performance measures from different reports to identify trends (more details above).

The 2007 Urban Mobility Report

What’s New

Each year the Urban Mobility Report revises procedures and improves the processes and data used in the estimates. With sponsorship from the National Cooperative Highway Research Program of the Transportation Research Board, the methodology was significantly revised in 2006 and 2007 to take advantage of new studies and detailed data sources that have not been available in previous studies. Some key changes for this year and their general effects are summarized in Exhibit 2. All of the congestion statistics in the 2007 Urban Mobility Report have been revised for all years from 1982 so that true trends can be identified.

For almost all urban areas that were intensively studied, and for urban America as a whole, there was more delay, more wasted fuel and higher congestion cost in 2005 than in 2004. That is the conclusion of this report—congestion is worse in urban areas of all sizes.
The revised methodology described below, however, shows that the estimated speeds on the most congested freeways are better in the 2007 Report than in the 2005 Report. But the year-to-year congestion trends are still “up.”
The 2007 report also estimates congestion problems in all urban areas, instead of only 85 regions. The 352 added regions were mostly small areas with relatively low congestion levels. Their addition reduces the average congestion values for each person traveling in the peak period (i.e., a little more delay and a lot more people), but it also increases the total congestion estimates (i.e., a lot more people that each have a small amount of delay).
The benefits from operational treatments and public transportation likewise appear to decline compared to the 2005 report; the actual numbers increase if the same methods are used.

Exhibit 2. Summary – Changes to the *2007 Urban Mobility Report*
Change for 2007 Report	General Effect Compared to Previous Reports
Estimate of congestion in all 437 U.S. urban areas (individual urban area estimates were only developed for 85 urban areas)	Increase the total delay, fuel and cost of congestion values. Decrease the average “per traveler” congestion values.
Minor arterial street congestion estimate	Increase delay, fuel and cost values.
High-occupancy vehicle lane statistics	Better estimate of regional congestion
Improve freeway speed estimate	Reduce delay, fuel and cost values. Also caused lower benefits for operations treatments & public transportation service (lower initial delay results in lower delay benefits).
Improve population estimate in some regions	Better estimate of congestion effects on individuals
Use truck percentages for each road	Better estimate than previous 5 percent value for all regions
Use average of daily fuel prices for each state	Better estimate than previous sample of fuel prices
Seattle region moved to Very Large population group	All historical population group statistics revised to include Seattle in the Very Large group

Change Highlights—Additions to Congestion Estimates

National estimate of congestion and costs – The 352 areas that are not intensively studied were grouped together and congestion estimates were developed to describe the congestion problem in the nation’s 437 urban areas. Adding these urban areas increased the total number of peak-period travelers included in the analysis from 82.1 million in the 85 urban areas to 110.5 million in the 437 urban areas. This change increases the total delay but, because the smaller areas are much less congested than the large regions, it reduces the average hours of delay per traveler.
Minor arterial congestion – As major roads became congested, minor road traffic volumes have increased. The estimates of congestion are more complete with these streets included in the arterial category for the 2007 Urban Mobility Report.
HOV travel – Buses and carpools traveling in reserved lanes provide one solution that is successful in many urban corridors. In some cases these lanes can also be used by single travelers who pay a fee. The person volume and travel speed statistics from operational evaluations in 70 corridors have been included in the urban area congestion estimates.

Change Highlights—Changes to Congestion Methodology

Freeway speed estimate – Data from freeway operation centers have become available in many travel corridors over the last few years. While the data are not complete enough to use as a direct measure of congestion in all 85 areas, it was used to update the estimation procedures. In general, the very low speeds used in previous studies are not sustained for an entire peak period in most freeway corridors. The detailed data show that freeways carry more vehicles at higher speeds than models previously estimated. In addition, traffic growth in the faster flowing off-peak direction has been greater than growth in the slower speed peak direction. The average traffic speed for all lanes, therefore, has not declined as much as previous models predicted. The congestion estimates for all urban areas are lower because of this change, but in most cases the trends have not changed from previous studies.
Population estimate – Urban area populations are not updated by all state departments of transportation (DOTs) every year in every region. As better estimates are prepared by local planners, they are incorporated into the Urban Mobility Report database, even if data from previous years must be changed.
Truck percentages for each road – Freight congestion has become a separate issue in some communities with its own set of solutions. Truck travel estimates included in the state and local datasets have improved over the years and have replaced the previous estimate of 5 percent trucks on all urban roads.
Average of daily fuel price – The recent fluctuations in gas prices suggested a need to include more than a small sample of fuel prices. An average of daily prices in each study state has been developed.
Seattle region – Regions are grouped according to population. Seattle’s population is now above 3 million and its statistics are now included in the Very Large group. As with similar past changes, the Large and Very Large averages for each statistic and every year have been recalculated with the new urban area groupings.

The 2007 Urban Mobility Report
The 2007 Urban Mobility Report – with Appendices

CAUTION: Do not compare data or performance measures from different reports to identify trends (more details above).

The 2005 Urban Mobility Report

What’s New

Each year the Urban Mobility Report revises procedures and improves the processes and data used in the estimates. In doing so, the report also revises all previous estimates so that true trends can be developed whenever possible. Some key changes for this year are:

Four urban areas moved into a new population group in 2003. All historical statistics were updated with these changes. Atlanta and Phoenix were moved into the “Very Large” group.
Providence was moved into the “Large” group. Allentown-Bethlehem was moved into the “Medium” group.
The researchers have refined the numerous equations and calculations used to produce the Urban Mobility Report. Minor changes to the computer programs have been made and the
historical trend data reflect the new information and procedures. Additional changes are anticipated at the conclusion of the study.
The calculation methodology has been changed to provide an improved estimate of fuel wasted during congested conditions. The new values show the amount of wasted fuel as
approximately half of the previous total. The year-to-year trend is the same—increasing fuel consumption and fuel costs.
The operational treatment effects are included for 2000, 2001, 2002 and 2003 mobility estimates. The data provide a better picture of the travel conditions in those four years.
Unfortunately, the long-term trend analysis for years before 2000, does not yet include this information.

The 2005 Urban Mobility Report
The 2005 Urban Mobility Report – with Appendix
Methodology for 2005 Urban Mobility Report (Appendix B)

CAUTION: Do not compare data or performance measures from different reports to identify trends (more details above).

The 2004 Urban Mobility Report

What’s New?

An increase from 75 to 85 areas studied. The new urban areas mean that all urbanized areas in the U.S. with a population greater than 500,000 and all of the top 70 urbanized
population areas are included in the report database.
Five urbanized areas in the 2003 report were combined into two areas for the 2004 report. The US Census Bureau combined Fort Lauderdale, West Palm Beach and Miami into one
urban center of 5.0 million persons and Tacoma was combined with Seattle for a total population of 2.7 million persons.
The value of truck delay cost is lower than estimated in previous reports, which lowers the total congestion cost. The new values include the efficiencies gained by the trucking
industry in the last 20 years, rather than a trend based on the Consumer Price Index.
Arterial street access management programs were added to the operational treatment list. These elements smooth traffic flow and reduce collisions through a variety of treatments
such as deceleration lanes, restricting turns across medians and combining driveways.
The operational treatment effects are included for 2000, 2001 and 2002 mobility estimates. The data provide a better picture of the travel conditions in those three years. Unfortunately, the long-term trend analysis does not yet include this information.
The delay per traveler measure uses the number of persons beginning their travel using a motorized mode during the peak periods (6 to 9 a.m. and 4 to 7 p.m.). This is a more
appropriate mobility measure than the previous delay per capita statistics.
The Annual Report seeks to provide the best estimate of travel conditions for each year. This year, as in other years, some previous statistics were slightly modified based on better understanding of trends and updated

The 2004 Urban Mobility Report
2004 Urban Mobility Report: Six Congestion Reduction Strategies and Their Effects on Mobility

CAUTION: Do not compare data or performance measures from different reports to identify trends (more details above).

The 2003 Annual Urban Mobility Report

What is New for this Year?

We have added the effect of three widely implemented operational treatments, public transportation service and high-occupancy vehicle lanes to the 2000 and 2001 mobility estimates. This change allows an examination of the effect of more types of improvements, allows a more thorough use of the available data and is another step in improving the mobility statistics. A separate report details these analyses and is posted on the Mobility Report website.
We have chosen to present the data in population groups to better illustrate the mobility trends for areas of similar population. The mobility levels that might be expected in urban areas are more related to cities of similar size than to the full group of 75 cities. The statistics and methodology descriptions are still included along with much more information in Appendix A and on the website: http://mobility.tamu.edu.
We present more information about the reliability side of urban mobility. This is not a comprehensive treatment, and more information is available in the 2001 Mobility
Monitoring Report (http://mobility.tamu.edu/mmp) (1). The variation in travel times is an important element of congestion, and might be a more solvable problem than the regular overcrowding of roadways. Data to inform this discussion, however, is not as available as it is for average or estimated conditions.
We have improved the speed estimation procedure and the incident delay factors. New computer simulations have been used to estimate the effect of vehicle breakdowns and collisions. Future changes in estimating the effects of operational improvements (see #2) will also likely affect the methods we use to estimate speeds and delay in the next several years. But simplifying assumptions and estimating procedures will be needed until more data collection programs are deployed.
Delay per person and the travel time index indicate somewhat different conclusions about mobility. This trend will be watched to see if it continues and the potential causes will be examined, but it appears that there are some differences that are the result of actions and policies rather than random occurrences.

The 2003 Annual Urban Mobility Report
2003 Urban Mobility Report: Volume 2

CAUTION: Do not compare data or performance measures from different reports to identify trends (more details above).

The 2002 Urban Mobility Report

What is New for this Year?

We have chosen to emphasize the mobility ISSUES and what we might be able to learn from the data this year. The statistics and methodology descriptions are still
included along with much more information on the website: http://mobility.tamu.edu. Issues and trends dominate this year’s report, however. The 19-year history of the database and the coming re-authorization of the federal transportation legislation provided the impetus to move away from a simple reporting of the numbers, to a slightly deeper attempt to understand what the data say.
We have examined the “SOLUTION” side of urban mobility in more detail than in the past. This is not a comprehensive treatment, and more information will be published in the fall of 2002. Operational improvements and high-occupancy vehicle treatments are included in this report. The Fall 2002 report will provide a more integrated look at the urban transportation system—incorporating the effects of many improvement types into the Travel Time Index statistics.
We have improved the SPEED ESTIMATING procedure. Using the new computer models that simulate traffic conditions and the more extensive traffic monitoring data we have collected, the relationships between traffic volume and speed are closer to the real world experience. Future changes in estimating the effects of operational improvements (see #2) will also likely affect the methods we use to estimate speeds and delay in the next several years. But simplifying assumptions and estimating procedures will be needed until more data collection programs are deployed.
The improved speed estimates have resulted in LESS DELAY than we have previously estimated. This does not mean that congestion is not a problem; in fact, the trend remains the same—congestion increasing in every city size category. It means that the time penalty for peak period trips is not as great as previously estimated. This is primarily the result of the large volume of trips using the off-peak direction of the roadway to travel at speeds close to the speed limit. The measures for all years of the study are recalculated with the new trends.
DELAY PER PEAK TRAVELER is a new mobility measure. We have used commuting surveys to estimate the number of travelers using the roads during the peak period, and divided the annual delay estimates by those people. This provides a more realistic idea of the amount of extra time that motorists spend traveling during peak hours.

The 2002 Urban Mobility Report

CAUTION: Do not compare data or performance measures from different reports to identify trends (more details above).

The 2001 Urban Mobility Report

What is Different About This Year’s Report?

The layout of the report is somewhat different this year. This report will focus less on the data tables and more on the issues addressed by the data. Many of the “issues” associated with urban mobility are discussed with some important trend or magnitude information shown by the data tables. The individual urban area information—all of the tables included in past reports—are included in an appendix to this report with links from each “issue” to the relevant tables.

New Measure

One important additional measure used in this report is the Travel Time Index (TTI)—a comparison of total travel time in the peak to travel time in free flow conditions. The TTI is different from the Travel Rate Index (TRI) because it includes delay from both heavy traffic demand and roadway incidents. The TRI only focuses on delay caused by heavy traffic demand. The TTI and TRI each illustrate the effect of a range of transportation improvements and address a central concern of urban residents—time it takes to travel in the peak periods.

New Methodology

A 1986 report (1) from FHWA summarized an analysis package to calculate freeway delay using the Highway Performance Monitoring System (HPMS) database. The program used travel and roadway information from each urban area to calculate both the recurring and incident delay that would result from the traffic levels on the roadways. The program simulated delay conditions on an urban freeway by generating incidents based on incident pattern data from a few U.S. cities from the 1960s and 1970s. The traffic incidents generated in the program could range from a breakdown on the roadway shoulder to a full freeway closure for an hour or more. The ratios of incident delay to recurring delay calculated in the FHWA report were used in previous Urban Mobility Study reports. In the latest Urban Mobility Study report, the FHWA program has been replicated so that the ratios can be updated annually with the current travel and roadway information. Thus, any changes in roadway configuration—such as more or fewer freeway breakdown lanes—will be reflected in the incident delay in the report.

The 2001 Urban Mobility Report

CAUTION: Do not compare data or performance measures from different reports to identify trends (more details above).

The 1999 Annual Mobility Report

1999 Annual Mobility Report — What’s New This Year?

This report broadens the coverage of previous reports by including more information on mobility measures. The study began several years ago with a few measures, a few urban areas and a focus on roadway congestion measures. All of these have been expanded to more completely address urban mobility in the U.S.

One significant change in this report is the use of the Travel Rate Index (TRI)—a comparison of travel time in the peak to travel time in free flow conditions—instead of the Roadway Congestion Index (RCI). The TRI can illustrate the effect of a broader range of transportation improvements and addresses a central concern of urban residents—the time it takes to travel in the peak periods.
For the first time in this report series, the effectiveness of an operational improvement (HOV lanes in Houston) was included in the analysis. Additionally, the effectiveness is shown at both the areawide and individual freeway levels. The versatility of the new methodology is also shown in the case study of the Houston HOV lanes with speed data collected from the freeways in Houston substituted for estimated speeds. The hope for future reports is that with more and better travel speed data being collected, the speed data collected from operating freeways can be used to replace estimated data.
The most significant change to the current methodology is the addition of a fifth congestion level labeled “extreme.” Because of the inclusion of the extreme category, some shifts in the estimated speeds for each of the congestion levels have occurred. These new estimated speeds have caused the average calculated speed to fall from previous levels in some areas and to increase in others, depending on the traffic density of the sections of roadway within each urban area.
The congestion estimation methods have particular importance when urban boundaries are redrawn due to realignments or when local agencies update the boundary to account for urban growth. These changes may significantly change the size of the urban area, which also causes a change in system length, travel and mobility estimates. When the urban boundary is not altered every year in fast growth areas, some data items take on a “stair-step appearance.” Significant changes that are caused by the data compilation methods are addressed by altering statistics to present a trend closer to actual experience for each year. This may cause some areas to move up or down in the rankings in some of the measures.

The 1999 Annual Mobility Report

CAUTION: Do not compare data or performance measures from different reports to identify trends (more details above).