Methodology

Source Data & Analysis

The focus of this report is the calendar year 2010. In some cases, calendar year 2006-2009 data is utilized to enable year over year comparisons.

The raw data comes from the historical traffic data warehouse of the INRIX Smart DriverNetwork. Since 2006, INRIX has acquired tens of billions of discrete “GPS-enabled probe vehicle” reports from vehicles traveling the nation’s roads – including taxis, airport shuttles, service delivery vans, long haul trucks, and consumer vehicles.

INRIX has developed efficient methods for interpreting probe vehicle reports that are provided in real-time to establish a current estimate of travel patterns in all major cities in the United States. These same methods can aggregate data over periods of time (annually in this report) to provide reliable information on speeds and congestion levels for segments of roads. With the nation’s largest probe vehicle network, INRIX generates the most comprehensive congestion analysis to date, covering the nation's 100 largest metropolitan areas and essentially all of the nation's major roadways.

Analysis Time Period

The focus of this report is the calendar year 2010. In some cases, calendar year 2006, 2007, 2008 and 2009 data is utilized to enable year over year comparisons.

Metropolitan Area & Roads/Segments Analyzed

The US Census Bureau definition of Core Based Statistical Areas is used to define metropolitan areas. This report uses the latest 2008 census estimates to identify the top 100 areas.

This report focuses on the major limited access roads in the United States. In all of its products, INRIX utilizes an common industry convention known as “TMC location codes” developed and maintained by the nation’s leading electronic map databases vendors to uniquely define road segments. The typical road segment is the interchange and the portion of linear road leading up to the interchange across all lanes in a single direction of travel. The length of a segment will depend upon the length of the distance between interchanges. For this report, over 110,000 road miles in over 48,000 discrete road segments have been analyzed.

Road Segment Data

• Reference speed (RS): For each road segment, all probe vehicle reports obtained in overnight hours (where congestion is usually unlikely) in 2009 are analyzed.
• Hourly average speed (HS): All probe vehicle reports for each road segment are grouped by hour of day, day of week (e.g. Monday from 3 to 4pm) and an “average speed” for each time slot is established for each road segment. Thus, each segment has 168 corresponding hourly average speed values – representing 24 hours of each day times the seven days in a week.

Overall Congestion by Metropolitan Area

• Travel Time Index (TTI): TTI is the ratio of peak period travel time to free flow travel time. The TTI expresses the average amount of extra time it takes to travel in the peak relative to free-flow travel. A TTI of 1.3, for example, indicates a 20-minute free-flow trip will take 26 minutes during the peak travel time periods, 6-minute (30 percent) travel time tax (T³). For each road segment, a TTI is calculated for each hour of the week, using the formula TTI = RS/HS.
• “Drive Time" Congestion: To assess and compare congestion levels year to year and between metropolitan areas, only “peak hours” are analyzed. Consistent with similar studies, peak hours are defined as the hours from 6 to 10 am and 3 to 7 pm, Monday through Friday – 40 of the 168 hours of a week.
• For each Metropolitan Area, an overall level of congestion is determined for each of the 40 peak hours by determining the extent and amount of average congestion on the analyzed road network. This is easy to compute once TTI’s are calculated for each segment:
  • STEP 1: For each of the 40 peak hours, all road segments analyzed in the CBSA are checked. Each segment where the TTI > 1 is contributing congestion, and it is analyzed further.
  • STEP 2: For each segment contributing congestion, the amount the TTI is greater than 1 is multiplied by the length of the segment, resulting in a congestion factor.
  • STEP 3: For a given hour, the overall metropolitan congestion factor is the sum of the congestion factors calculated in STEP 2.
  • STEP 4: To establish the Metropolitan Travel Time Index for a given hour, the metropolitan congestion factor from STEP 3 is divided by the number of road miles analyzed.
  • STEP 5: A peak period Metropolitan Travel Time Index is determined by averaging the hourly Metropolitan Travel Time Indices from STEP 4.

INRIX also presents in the report a variant of the Travel Time Index in this 2009 Annual Update as a means of communicating more directly the impact of congestion – the Travel Time Tax™. While all calculations driving the Scorecard continue unchanged as described above, the Travel Time Tax, or T³, takes the portion of the TTI above 1.00 and turns it into a percentage. For example, a TTI of 1.25 equates to a T³ of 25%. Much like a sales tax, T³ can be considered that additional cost of travel above uncongested conditions. Throughout the report, T³ is being utilized where TTI was utilized, previously.

Bottlenecks

Each road segment’s bottleneck factor can be compared with others in a metropolitan area and against all bottlenecks nationally. It can also be compared year-to-year, as we have in this Scorecard.

Congestion – and how to measure it – can be in the eye of the beholder. Is congestion defined as how bad a road segment is at its worst or is it how often the segment gets “congested” (and what is the threshold for “congestion” anyways – tapping the brakes, stop and go conditions, etc.)? INRIX has developed a method that combines both the amount of time a road segment is congested with the intensity of congestion during those periods.

• The same RS and HS values are utilized as in the overall congestion by metropolitan area portion of the study;
• All 168 hours of the week are considered, not just the 40 “peak hours.” As will be evident in the data, severe bottlenecks aren’t just limited to peak hours;
• For each hour of the week that the average speed is less than 50% of the reference speed, the hour is considered “congested;”
• For all “congested” hours, the average intensity of the congestion is determined by establishing an average travel time ratio;
• The total Bottleneck factor equals the number of hours of congested by the average travel time ratio.
• Each road segment’s Bottleneck factor can be compared with others in a metropolitan area and against all Bottlenecks nationally. It can also be compared year-to-year, as we will do going forward.

Congested Corridors

A new feature in this year’s scorecard expands on the bottleneck analysis by linking neighboring congested road segments into “Congested Corridors.” The following approach is used to determine and then rank corridors. 2010 bottlenecks data was used to determine the corridors, using the following criteria:

• The corridor must be comprised of multiple segments.
• The corridor must have at least one segment that is congested ten hours a week or more on average
• All road segments in the corridor must have at least four hours a week of congestion on average.
• To prevent inadvertently breaking up logical corridors, in cases where one or two short segments do not meet the four hour minimum, exceptions are made. However, they must be in the middle of a corridor, not at the start or end.

Once the corridors were identified (341 in all), another analysis determined several different travel time statistics that are used to describe and rank each corridor. The following steps were used to analyze and rank the corridors:

For each corridor:

• The uncongested/free flow travel time is calculated (from the RS of each road segment in a corridor).
• Average travel times for both peak periods (AM and PM) are determined.
• The highest peak period travel time is compared to the uncongested/free flow travel time, resulting in both an average peak period delay and peak period Travel Time Tax.
• To illustrate how bad a corridor is at its most congested, the worst hour delay and Travel Time Tax is computed.

To rank corridors:

• A corridor congestion factor is determined for each corridor by multiplying average delay by the Travel Time Tax for the worse of the AM or PM peak periods.
• Each corridor’s congestion factor can be compared to and ranked against others in a metropolitan area and against all corridors.

Scorecard Relationship with Other Studies

While the Scorecard shares some common elements with other reports, it also has several unique features.

Common elements

• The Scorecard adopts the common convention of peak period drive time hours of 6 – 10 AM and 3 – 7 PM, Monday through Friday.
• The Travel Time Index concept is now a standard metric to measure conditions relative to uncongested, free flow situations.

Unique features

Given the myriad of ways to calculate congestion and the wide range of raw data that is utilized, it is natural that different reports can have different results, rankings and indexes. When comparing differences between the Scorecard and other reports, it could be due to one or more of the following reasons:

• While some other reports weight results by traffic volume and/or factor in the number of lanes on roadways; the Scorecard does not.
• Travel Time Index calculations are from a road user perspective based on complete random trips, not weighted by volumes, lane miles, or origin/destination weighting.
• Travel Time Index values in the Scorecard seem lower than some other studies. This is likely for two reasons:
  • By using a data driven reference speed instead of a flat speed for free flow, such as 60 mph, results in lower uncongested speeds in most cases, meaning less congestion is calculated for the same average speeds; and
  • INRIX coverage extends throughout entire metropolitan areas including highways and commuting corridors far away from city centers that may contribute less to congestion than roads in the urban core, lowering the index.
• Studies may have different metropolitan areas, or aggregate some regions such as Washington, D.C. and Baltimore. The Scorecard approach could easily adjust market boundaries to aggregate results differently, but is presently based on the standardized, Census CBSA definition.
• The Scorecard is focused on mainline lanes of limited access highways; other studies may include ramps, interchanges and arterials.

• This report is based on data, technology and processes that have been designed to optimize very quick turnaround times between the end of the data collection period and the publishing of the Scorecard.
• Many of the reports utilize data that is many months or years old when published.
• The Scorecard is completely based upon real data – tens of billions of data points from real consumer and commercial vehicles traveling on real road segments. It is not limited by sensor coverage nor is it an interpolation of data.
• This is the first analysis to go to the detailed road segment level nationwide; it is also the first to look in depth by hour and day nationwide. Further, this report offers a unique opportunity to see trending by time, region or specific road segment.

References

The following list is but a few of the notable recent reports:

• 2010 Annual Urban Mobility Report— (Texas Transportation Institute): http://mobility.tamu.edu/ums
• Bottleneck Performance in the I95 Corridor: Baseline Analysis Using Vehicle Probe Data— (I-95 Corridor Coalition): http://www.i95coalition.org/i95/Portals/0/Public_Files/pm/reports/Final%20Report_Bottleneck%20Performance%20I-95_Baseline%20Analysis(Final).pdf
• Unclogging America’s Arteries: Effective Relief for Highway Bottlenecks 1999-2004— (American Highway Users Alliance): http://www.highways.org/pdfs/bottleneck2004.pdf
• Building Roads to Reduce Traffic Congestion in America’s Cities: How Much and at What Cost? ——(Reason Foundation): http://reason.org/files/ps346.pdf
• America’s Most Congested Cities ——(Forbes Life Magazine): http://www.forbes.com/2008/04/10/congested-commute-cities-forbeslife-cx_mw_0410realestate.html
• The Road...Less Traveled: An Analysis of Vehicle Miles Traveled Trends in the U.S. —(The Brookings Institution): http://www.brookings.edu/reports/2008/~/media/Files/rc/reports/2008/1216_transportation_tomer_puentes/vehicle_miles_traveled_report.pdf
• Freight Performance Measurement: Travel Time in Freight-Significant Corridors— (Federal Highway Administration): http://ops.fhwa.dot.gov/freight/freight_analysis/perform_meas/fpmtraveltime/index.htm
• An Initial Assessment of Freight Bottlenecks on Highways— (Federal Highway Administration): http://www.fhwa.dot.gov/policy/otps/bottlenecks/index.htm
• 2009 Bottleneck Analysis of 100 Freight Significant Highway Locations— (American Transportation Research Institute): http://www.atri-online.org/index.php?option=com_content&view=article&id=248&Itemid=75

Why does the Travel Time Tax/Index from the INRIX Scorecard Differ from the Travel Time Index in TTI’s Urban Mobility Report?

The recently published Urban Mobility Report (UMR) reported a national average Travel Time Index of 1.20 in 2009, whereas INRIX’s national Travel Time Index for 2009 rounds to 1.09. Since both reports are based on the same underlying speed data from INRIX, what accounts for the large difference? There are two simple reasons:

• UMR includes arterials and the Scorecard focuses only on limited access roadways—freeways, tollways, etc. With traffic signals, arterials naturally have more delay than freeways, while freeways when functioning as intended have no delays.
• UMR integrates and weights analyses with measured and estimated traffic volumes and the Scorecard doesn’t. Since recurring congestion and high traffic volume go hand-in-hand, the UMR gives increased weighting to congestion portions of the network and times of day, while the Scorecard weights all roads and times equally.

An analogy can be made to the stock market. The Scorecard and UMR are like the Dow Jones Industrial Average and the S&P 500 Index. Both are widely followed and offer a view of market conditions, and usually track each other, but they are not identical. The Scorecard’s Travel Time Tax is like the Dow Jones while the Urban Mobility Report’s Travel Time Index is like the S&P 500.

Acknowledgements

Rick Schuman, INRIX vice president of public sector, is the author of the INRIX National Traffic Scorecard and the driver behind the primary analysis of the metropolitan and bottleneck data.

INRIX works with data providers, technology partners, experts and our customers to address traffic issues in North America and Europe. Collaborating to create unique and important products is key to INRIX’s success. INRIX would like to thank several organizations and individuals who have assisted in one way or another in creating the approaches used in the annual Scorecard and Special Reports. Tim Lomax and Shawn Turner of the Texas Transportation Institute, Rich Margiotta of Cambridge Systematics and Mark Hallenbeck of the University of Washington aided in development of the original Scorecard methodology. Kevin Loftus of INRIX’s partner Clear Channel Total Traffic Network provided local market knowledge and assistance.