Data used in the Bureau of Transportation Statistics' new interactive version of Transportation Economic Trends (TET). TET highlights transportation's role in the economy and explores changes (trends) over time through a series of interactive charts. TET also explains related concepts and data sources for a general audience.

Interactive visualizations available at: https://data.transportation.gov/stories/s/28tb-cpjy

Contribution of transportation services to the economy and the Transportation Satellite Accounts

Transportation capital stock

Investment in transportation

Transportation costs faced by households.

Employment in transportation and related industries

Final Demand Attributed to Transportation

Employment in transportation and related occupations

This dataset contains monthly performance statistics for the Great Lakes-St. Lawrence Seaway system.

About Transportation Services Index

The Transportation Services Index (TSI), created by the U.S. Department of Transportation (DOT), Bureau of Transportation Statistics (BTS), measures the movement of freight and passengers. The index, which is seasonally adjusted, combines available data on freight traffic, as well as passenger travel, that have been weighted to yield a monthly measure of transportation services output.

For charts and discussion on the relationship of the TSI to the economy, see our Transportation as an Economic Indicator: Transportation Services Index page (https://data.bts.gov/stories/s/TET-indicator-1/9czv-tjte)

For release schedule see: https://www.bts.gov/newsroom/transportation-services-index-release-schedule

About seasonally-adjusted data

Statisticians use the process of seasonal-adjustment to uncover trends in data. Monthly data, for instance, are influenced by the number of days and the number of weekends in a month as well as by the timing of holidays and seasonal activity. These influences make it difficult to see underlying changes in the data. Statisticians use seasonal adjustment to control for these influences.

Controlling of seasonal influences allows measurement of real monthly changes; short and long term patterns of growth or decline; and turning points. Data for one month can be compared to data for any other month in the series and the data series can be ranked to find high and low points. Any observed differences are “real” differences; that is, they are differences brought about by changes in the data and not brought about by a change in the number of days or weekends in the month, the occurrence or non-occurrence of a holiday, or seasonal activity.