Kirkland, Washington – July 30, 2013 – The most recent INRIX Gridlock Index (IGI) shows traffic congestion in June rose by more than eight percent on a year-over-year basis, adding a positive note to the ongoing debate about the state of the U.S. economy. The June congestion data comes on the heels of higher-than-expected payroll additions , but also lower-than-expected consumer confidence readings for June 2013. It also follows sobering news that the U.S. economy grew at a slower pace in the first quarter than previously estimated.
June’s data also provided a stark contrast to the performance of just a year ago. “Last year at this time we saw a 19 percent year-over-year decrease in traffic congestion levels,” said Bryan Mistele, CEO of INRIX. “Yet we turned a corner in December. Aside from a slight pullback in March, we’ve seen higher levels of consumer spending and employment lead to dramatically higher levels of gridlock on our roads nationwide.”
National levels of traffic congestion increased by 8.3 percent from June 2012 to June 2013 to reach a composite IGI score of 7.2, meaning the average trip took drivers in the 100 most populated metro areas just over seven percent longer due to increased traffic.
June’s traffic congestion data also saw a shift in regional patterns.
• The Northeast displaced the Western U.S. to lay claim to the largest increase in year-over-year traffic congestion (12%). This was driven in part by strong gains in the metro areas of Boston (28% YoY) and Philadelphia (25% YoY). It also aligns with recent figures from the U.S. Commerce Department showing that new home sales in the region jumped by 18.5% from May to June.
• The West had the second highest gain in year-over-year traffic congestion (9.6% YoY), driven in part by two metro areas that have proven most resilient to economic downturns: Los Angeles (12% YoY) and San Francisco (14% YoY).
• The Midwest continued to decelerate. Its year-over-year gridlock increases of 14.4 percent in April and 9.1 percent in May were followed by a more subdued increase of 4.2 percent in June.
Increases in Milwaukee (17% YoY) and Minneapolis (16% YoY) were offset by declines in Toledo (-28% YoY), Columbus (-19% YoY) and Cincinnati (-4% YoY). The Commerce Department also confirmed that new home sales in the region fell by almost 12% from May to June . Some of this weakness may have been caused by regional storms and flooding.
• In some much welcome economic news following Detroit’s recent bankruptcy filing, the greater Detroit metro area bucked the Midwestern decline to register a seven percent year-over-year increase in traffic congestion. This development is likely due to the continued resurgence of the area’s auto industry as evidenced by Ford Motor Company’s recent positive earnings report.
• Meanwhile, traffic congestion in the South accelerated. Its 2.4 percent year-over-year increase in May was followed by an increase of 6 percent in June, driven by strong gridlock increases in cities like Miami (17% YoY) and Atlanta (10% YoY). This is in line with the Federal Reserve Bank of Atlanta’s recent report of modest economic expansion in the region.
The IGI draws data from the INRIX Traffic Data Archive http://scorecard.inrix.com/scorecard/, a historical traffic information database comprised of data collected from hundreds of public and private sources, including a crowd-sourced network of millions of vehicles and mobile devices.
Drawing on almost three years of trend data, INRIX has developed methods to interpret real-time traffic data to establish monthly and annual averages of traffic patterns in all major U.S. metropolitan areas. These same methods can aggregate data over periods of time to provide reliable information on speeds and congestion levels for given segments of roads. Using this proprietary data collected from INRIX’s extensive network, the IGI analyzes and measures traffic trends in 100 of the top metropolitan areas in the U.S. The metropolitan areas used in the IGI are defined by the Core-Based Statistical Areas (CBSA), as determined by the United States Census Bureau.
There are two key building blocks for the analysis used in the IGI:
• Reference Speed (RS): An uncongested “free-flow” speed is determined for each road segment using the INRIX Traffic Data Archive.
• Calculated Speed (CS): Speed data from the INRIX Traffic Data Archive is analyzed to determine the “calculated speed” for each 15-minute period of each day, for each road segment every month (e.g. Monday from 06:00 to 06:15 for April 2012). Thus, each road segment has 672 corresponding calculated speed values per week – representing four 15-minute time windows for each hour of the day, multiplied by seven days in a week.
To assess congestion across a metropolitan area, INRIX utilizes and adapts several concepts that have been used in similar studies and previous INRIX analyses.
The IGI represents the barometer of congestion intensity. For a road segment with no congestion, the IGI would be zero. Each additional point in the IGI represents a percentage point increase in the average travel time of a commute above free-flow conditions during peak hours. An IGI of 30, for example, indicates a 20-minute free-flow trip will take 26 minutes during the peak travel time periods, which is a 6-minute (30 percent) increase over the free-flow travel time. For each road segment, an IGI Score is calculated for each 15-minute period of the week, using the formula IGI= (RS/CS) – 1.
“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 06:00 to 10:00 a.m. and 3:00 to 7:00 p.m., 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 computed as follows, once the IGI is calculated for each road segment:
• STEP 1: For each of the 40 peak hours, all road segments analyzed in the CBSA are checked. Each road segment where the IGI is greater than 0 is contributing congestion and is analyzed further.
• STEP 2: For each road segment contributing congestion, the amount the IGI is greater than 1 is multiplied by the length of the road segment, resulting in a congestion factor.
• STEP 3: For each 15-minute period, the overall metropolitan area congestion factor is the sum of the congestion factors calculated in STEP 2.
• STEP 4: To establish the metropolitan IGI for a given 15-minute period, the metropolitan congestion factor from STEP 3 is divided by the number of road miles analyzed.
• STEP 5: A peak period IGI is determined by averaging the 15-minute indices from STEP 4.¬¬