12 Support competitiveness through the efficient movement of freight
Support the region’s economic competitiveness through the efficient movement of freight.
In comparison to other regions – use TTI, MnDOT or Streetlight data.
12.1 Travel time reliability
The Texas Transportation Institute’s (TTI) Urban Mobility Report compiles data on transportation system performance for metropolitan areas throughout the United States. These data can be used to measure changes in the performance of the Twin Cities’ highway system over time and provide a rough comparison with similar peer urban areas in the United States. These peer urban areas include Baltimore, MD; Cincinnati and Cleveland, OH;, Dallas, TX; Denver, CO; Milwaukee, WI; Pittsburgh, PA; Portland, OR; Seattle, WA; and St. Louis, MO. TTI published their most recent data (2020) in 2021.
One measure of highway performance is the time it takes to make trips in congested conditions versus the time it would take in uncongested or free-flow conditions. A travel time index is used to assess these impacts by measuring the proportion of additional time a trip takes due to congestion. A travel time index of 1.30 indicates that it takes 30 percent longer to make a trip in the peak period (6:00 a.m. to 10:00 a.m. and 3:00 p.m. to 7:00 p.m.) than in off-peak conditions, when the motorist could travel at free-flow speeds.
Figure 12.1 shows the travel time index for the Twin Cities urban area was relatively flat from 2010 to 2019 at about 1.25 indicating it took the average motorist 25% more time to travel along interstate highways during peak period times than during off-peak times. In 2020, the last year for which we have data, the travel time index declined across all peer regions; in the Twin Cities, the travel time index dropped to 1.1, only a 10% increase in the peak period compared to off-peak times.
12.2 Cost of truck congestion and delay
Highway congestion not only decreases the reliability of freight shipments, but also increases costs. The Texas Transportation Institute’s (TTI) Urban Mobility Report 1 calculates truck congestion costs to the freight sector as the value of increased travel time and other operating costs of large trucks and the extra diesel consumed. To compare these costs across regions and over time, we have expressed them as a percentage of gross domestic product (GDP). Like truck congestion costs, GDP (inflation-adjusted gross domestic production, US Bureau of Economic Analysis (BEA) (2023)) in this measurement is expressed in real dollars, adjusted for inflation.
Expressed as a percentage of regional GDP, the relative cost of delay to the freight sector was flat between 2010 and 2018, at about 0.08% (less than one-tenth of one percent of regional GDP). The Twin Cities consistently ranked below its peers for this measure until 2019, when the costs of congestion to the freight sector increased to $236 million, equivalent to 0.1% of GDP. These costs were markedly reduced across all peer regions in 2020, falling to $119 million in the Twin Cities region.
The cost of truck congestion can also be expressed in terms of greenhouse gas emissions: the amount of CO2 emitted in excess due to vehicles idling or moving slower in traffic. Figure 12.5 shows these excess emissions in 2019, the last year of pre-pandemic data. To aid peer-region comparisons and place them in their context, they are expressed as a share of total on-road emissions by region.
Across all peer regions, congestion-related emissions form a very small share of total on-road emissions: at their greatest – in the Portland metro – they comprise only 5.4% of total on-road emissions. In the Twin Cities, 3.9% of total on-road CO2 emissions in 2019 were emitted in excess due to congestion: 0.7% from trucks in congestion, and 3.2% from passenger vehicles in congestion.
TTI’s 2021 edition uses crowdsourced data from INRIX on urban streets and highways, along with highway inventory data from a Federal Highway Administration database. The report was sponsored by the Texas Department of Transportation and the National Institute for Congestion Reduction (Schrank et al. 2021).↩︎