The Infrastructure Investment and Jobs Act (IIJA) also known as the Bipartisan Infrastructure Law (BIL) contains thousands of line items of transportation infrastructure funds. This report helps detail those line items.

Release Note

BTS is withholding the scheduled release of the passenger and combined indexes for January. The passenger index is a statistical estimate of airline passenger travel and other components based on historical trends up to December 2019. As a result, the estimates have yet to fully account for the impact of the coronavirus. Air freight is also a statistical estimate. Since air freight makes up a smaller part of the freight index, the freight TSI is being released as scheduled.

Description

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.

This table shows the number and percent of people in the United States potentially exposed to different levels of noise from road sources.

The objective of this project was to develop system designs for programs to monitor travel time reliability and to prepare a guidebook that practitioners and others can use to design, build, operate, and maintain such systems. Generally, such travel time reliability monitoring systems will be built on top of existing traffic monitoring systems. The focus of this project was on travel time reliability. The data from the monitoring systems developed in this project – from both public and private sources –included, wherever cost-effective, information on the seven sources of non-recurring congestion. This data was used to construct performance measures or to perform various types of analyses useful for operations management as well as performance measurement, planning, and programming. The datasets in this zip file, which is 338.39 MB in size, are in support of SHRP 2 reliability project L38A, "Pilot testing of SHRP 2 reliability data and analytical products: Southern California." This report can be accessed via the following URL: https://rosap.ntl.bts.gov/view/dot/3611 This zip file contains 11 files, including 8 Microsoft Excel worksheets (XLSX), 2 Comma Separated Values (CSV), and 1 Zip Package (PKZIP) files. The Microsoft Excel worksheets can be opened using the 2010 and 2016 versions of Microsoft Word, the CSV files can be opened using most text editors, and the PKZIP files can be opened using most zip file extraction programs.

The U.S. Census Bureau provides monthly estimates of the total dollar value of construction work done in the United States as part of the Value of Construction Put in Place Survey (VIP). Includes construction related to passenger terminals, mass transit, railroad, maintenance facilities, and freight terminals.

Enplaned baggage and wheelchairs and scooters mishandled by commercial air carriers.

How many people are staying at home? How far are people traveling when they don’t stay home? Which states and counties have more people taking trips? The Bureau of Transportation Statistics (BTS) now provides answers to those questions through our new mobility statistics.

The Trips by Distance data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland. The travel statistics are produced from an anonymized national panel of mobile device data from multiple sources. All data sources used in the creation of the metrics contain no personal information. Data analysis is conducted at the aggregate national, state, and county levels. A weighting procedure expands the sample of millions of mobile devices, so the results are representative of the entire population in a nation, state, or county. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day.

Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized location away from home. Home locations are imputed on a weekly basis. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips. Trips capture travel by all modes of transportation. including driving, rail, transit, and air.

The daily travel estimates are from a mobile device data panel from merged multiple data sources that address the geographic and temporal sample variation issues often observed in a single data source. The merged data panel only includes mobile devices whose anonymized location data meet a set of data quality standards, which further ensures the overall data quality and consistency. The data quality standards consider both temporal frequency and spatial accuracy of anonymized location point observations, temporal coverage and representativeness at the device level, spatial representativeness at the sample and county level, etc. A multi-level weighting method that employs both device and trip-level weights expands the sample to the underlying population at the county and state levels, before travel statistics are computed.

These data are experimental and may not meet all of our quality standards. Experimental data products are created using new data sources or methodologies that benefit data users in the absence of other relevant products. We are seeking feedback from data users and stakeholders on the quality and usefulness of these new products. Experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production if resources permit.

The government plays an important role in the U.S. transportation system, as a provider of transportation infrastructure and as an administrator and regulator of the system. The government spends a large amount of funds on building, rehabilitating, maintaining, operating, and administering the infrastructure system. Government revenue generated from several sources including user fees, taxes from transportation and non-transportation-related activities, borrowing, and grants from federal, state, and local governments primarily supports these activities.

Government Transportation Financial Statistics (GTFS) provides a set of maps, charts, and tables with information on transportation-related revenue and expenditures for all levels of government, including federal, state, and local, and for all modes of transportation.

Related tables can be found in National Transportation Statistics, Section 3.D - Government Finance (https://www.bts.gov/topics/national-transportation-statistics).

For further information, data definitions, and methodology, see https://www.bts.gov/gtfs

Light trucks include trucks with up to 14,000 pounds gross vehicle weight, including minivans and sport utility vehicles. Prior to the 2003 Benchmark Revision, light trucks were up to 10,000 pounds. The U.S. Bureau of Economic Analysis releases auto and truck sales data, which are used in the preparation of estimates of personal consumption expenditures.

2023