Weekly carloads from the Surface Transportation Board’s (STB) Rail Service Metrics. Each week, the seven Class I railroads submit originated and received carloads by 23 major commodity groups, such as grain, grain mill, primary forest products, chemicals, coal, containers, trailers, etc.

While the STB began collecting some service metrics from railroads (e.g., trains speeds, origin dwell, and terminal dwell) in October 2014, it did not start collecting carload data until March 2017 through a new rulemaking.

Bar chart showing a query from the rail carloadings dataset to show the most recent week of data by railroad. It includes average weekly carloads based on the month to provide a sense of the seasonal expected level. It also includes a calculation of one standard deviation above and below that average to provide a sense of variation around the expected level.

Weekly carloads from the Surface Transportation Board’s (STB) Rail Service Metrics. Each week the seven Class I railroads submit originated and received carloads by 23 major commodity groups, such as grain, grain mill, primary forest products, chemicals, coal, containers, trailers, etc.

While the STB began collecting some service metrics from railroads (e.g., trains speeds, origin dwell, and terminal dwell) in October 2014, it did not start collecting carload data until March 2017 through a new rulemaking.

Line chart showing weekly originated grain carloadings with a separate line for each railroad. Use the filters to choose different commodities, shipment types, railroads, or date ranges.

Line chart showing total carloads per railroad (originated grain, by default) by week, with separate lines for recent years, providing a seasonal comparison. Filters can be adjusted to specific railroads, different commodities, or to include received carloads.

Query from rail carloadings data set to show the most recent week of data. It also includes average weekly carloads based on the month to provide a sense of the seasonal expected level. Additionally, it includes a calculation of one standard deviation above and below that average to provide a sense of variation around the expected level.