Recognizing the importance of transportation and the importance of objective statistics for transportation decision-making, Congress requires the Director of the Bureau of Transportation Statistics (BTS) of the U.S. Department of Transportation (USDOT) to provide the Transportation Statistics Annual Report (TSAR) each year to Congress and the President.1 BTS published the first TSAR in 1994. This 30th TSAR edition documents the conduct of the duties of BTS as called out in the statute.Source: https://rosap.ntl.bts.gov/view/dot/79039The Transportation Statistics Annual Report (TSAR) describes the Nation’s transportation system, the system’s performance, its contributions to the economy, and its effects on people and the environment. This report is based on information collected or compiled by the Bureau of Transportation Statistics (BTS), a principle Federal statistical agency at the U.S. Department of Transportation.Source: https://www.bts.gov/product/transportation-statistics-annual-reportThis upload contains xlsx files supporting the 2023 (https://rosap.ntl.bts.gov/view/dot/72943) and 2024 (https://rosap.ntl.bts.gov/view/dot/79039) TSARs.The two readme files were created for this upload and were not produced by the BTS.

Graph and download economic data for Combined Transportation Services Index (TSITTLC) from Feb 2000 to Apr 2025 about transportation, services, rate, indexes, and USA.

BTS Members

## Overview

BTS Members is a dataset for object detection tasks - it contains Jin RM Suga Jhope Jimin V JK annotations for 224 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Graph and download economic data for Leading Indicators OECD: Component series: BTS - Order books: Normalised for the Russian Federation (RUSLOCOBONOSTSAM) from Sep 1992 to Jan 2022 about book, leading indicator, and Russia.

The National Network dataset is as of December 22, 2020 and is from the Bureau of Transportation Statistics (BTS) along with the Federal Highway Administration (FHWA), and part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The National Network was authorized by the Surface Transportation Assistance Act of 1982 (P.L. 97-424) and specified in the U.S. Code of Federal Regulations (23 CFR 658) to require states to allow conventional combinations on "the Interstate System and those portions of the Federal-aid Primary System serving to link principal cities and densely developed portions of the states on high volume routes utilized extensively by large vehicles for interstate commerce which do not have any unusual characteristics causing current or anticipated safety problems. “The National Network (NN) includes almost all of the Interstate Highway System and other, specified non-Interstate highways. On March 31, 2025, four (4) records were updated to correct their "SIGNT1" and "SIGNN1" values. “The National Network (NN) includes almost all of the Interstate Highway System and other, specified non-Interstate highways. The network comprises more than 200,000 miles of highways. The National Network supports interstate commerce by regulating the size of trucks. This file is a geospatial representation of the National Network as described in 23 CFR 658 Appendix A and should not be interpreted as the official National Network and should not be used for truck size and weight enforcement purposes or for navigation. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529045

The T-100 Domestic Market and Segment Data dataset was downloaded on April 08, 2025 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The database includes data obtained from a 100 percent census of BTS Form 41 schedule submissions by large certificated air carriers. It shows 2024 statistics for all domestic airports operated by US carriers, and all information are totals for the year. This dataset is a combination of both T-100 Market and T-100 Segments datasets. The T-100 Market includes enplanement data, and T-100 Segment data includes arrivals, departures, freight, and mail. Data is by origin airport. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529081

The National Transit Map - Stops dataset was compiled on June 02, 2025 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The National Transit Map (NTM) is a nationwide catalog of fixed-guideway and fixed-route transit service in America. It is compiled using General Transit Feed Specification (GTFS) Schedule data. The NTM Stops dataset shows stops where vehicles pick up or drop off riders. This dataset uses the GTFS stops.txt file. The GTFS schedule format and structure documentation is available at, https://gtfs.org/schedule/. To improve the spatial accuracy of the NTM Stops, the Bureau of Transportation Statistics (BTS) adjusts transit stops using context from the submitted GTFS source data and/or from other publicly available information about the transit service. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529049

Graph and download economic data for Leading Indicators OECD: Component series: BTS - Production: Original series for Hungary (HUNLOCOBPORSTSAM) from Jan 1996 to Nov 2022 about Hungary, leading indicator, origination, and production.

From https://www.bts.gov/faf:The Freight Analysis Framework (FAF) database provides estimates of US freight flows. The FAF provides data for states and metropolitan areas. Flows include all modes of transportation and 42 commodity types. The Bureau of Transportation Statistics (BTS) produces the FAF with support from the Federal Highway Administration (FHWA). BTS builds FAF with data from many sources. Inputs include the Commodity Flow Survey (CFS), foreign trade data, and data from agriculture, extraction, utility, construction, service, and other sectors.FAF5 includes three types of freight flows: weight, value and activity. The FAF provides weight in thousands of tons, value in millions of 2017 constant dollars, and activity in millions of ton-miles. Users can download .csv and Microsoft Access files below.The latest version of FAF (FAF5.6.1) provides estimates of weight, value, and activity by origin and destination regions, commodity type, and mode for:* Base year (2017)* Annual estimates (2018–2022)* Preliminary annual estimates (2023)* Forecast year estimates (2025–2050)* State-level historical trend estimates (1997–2012)* Experimental county-to-county estimates (2022) – the recently released experimental product (county-level flows) and documentationThe experimental county-to-county estimates are uploaded separately at https://www.datalumos.org/datalumos/project/231661/version/V1/view.From https://ops.fhwa.dot.gov/freight/freight_analysis/faf/:The Freight Analysis Framework (FAF), produced through a partnership between Bureau of Transportation Statistics (BTS) and Federal Highway Administration (FHWA), integrates data from a variety of sources to create a comprehensive picture of freight movement among states and major metropolitan areas by all modes of transportation. Starting with data from the 2017 Commodity Flow Survey (CFS) and international trade data from the Census Bureau, FAF version 5 (FAF5) incorporates data from agriculture, extraction, utility, construction, service, and other sectors.The FAF5 provides estimates for tonnage and value by regions of origin and destination, commodity type, and mode for base year 2017 and a 30- year forecasts. FAF5 forecasts provide a range of future freight demands at five-year increments representing three different economic growth scenarios, through 2050, by various modes of transportation.

Flight data from the US Department of Transportation, Bureau of Transportation Statistics. Downloaded on 2019-07-04. https://www.transtats.bts.gov Data are here for use in software tutorials.

The Transportation Satellite Accounts (TSAs) provide a means for measuring the contribution of transportation services to the national economy. Prior to the TSAs, the magnitude of transportation services had long been underestimated, as most national measures counted only the value of for-hire services. Measurement of services provided only by for-hire firms misses the sizable contribution of transportation services that take place within nontransportation industries, termed as in-house transportation.To more accurately measure transportation services, the Bureau of Transportation Statistics (BTS) of the U.S. Department of Transportation and the Bureau of Economic Analysis (BEA) of the U.S. Department of Commerce, jointly developed the Transportation Satellite Accounts (TSAs). The TSAs, as a supplement to the U.S. Input-Output (I-O) Accounts, measure the contribution of both for-hire and in-house transportation. The TSAs include all seven of the for-hire transportation industries reported in the U.S. I-O accounts and four in-house transportation modes.

The Intermodal Freight Facilities - Air-to-Truck dataset was compiled on January 15, 2019 and was updated on February 24, 2020 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This dataset includes air to truck intermodal freight facilities for the top 60 airports by total freight moved in 2017. This dataset is one of several layers in the Bureau of Transportation Statistics (BTS) Intermodal Freight Facility Database. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529031

The Intermodal Freight Facilities - Marine Roll-on/Roll-off dataset was compiled on July 01, 2019 and was updated on July 22, 2022 from the Bureau of Transportation Statistics (BTS), and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). When available, primary sources for this dataset were the websites of the ports, as well as port operators. Every facility is associated with a port and assumed to be served by both marine and truck, and those facilities which support rail operations, the reporting code for the operating rail company is also identified. The dataset also includes at least one Navigation Unit ID (NAV_UNIT_ID) from the U.S. Army Corps of Engineers (USACE) Port Facilities dataset which is associated with the Ro/Ro terminal. This dataset is one of several layers in the Bureau of Transportation Statistics (BTS) Intermodal Freight Facility Database. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529032

The Docks dataset is periodically updated by the United States Army Corp of Engineers (USACE) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The Dock file provides the complete dock list of all facility types. Additional attributes include a location description, street address, city, state, zip code, county, congressional district, owners, operators, highway-and-railway connections, commodities, type of construction, cargo-handling equipment, water depth alongside the facility, berthing space, and deck height. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529017

BTS Documentation can be found in the wiki corresponding to the Bitbucket repository:
https://bitbucket.org/wanglab-upenn/bts-docker/wiki/Home

To run the test dataset, follow the instructions described in "Getting Started":
https://bitbucket.org/wanglab-upenn/bts-docker/wiki/Getting%20Started
Sample data used during testing is available in the "BTS_Test_Dataset.zip" file.

Description of inputs and input parameters:
https://bitbucket.org/wanglab-upenn/bts-docker/wiki/Usage%20Parameters
Annotation tracks used for evaluation are available in the "BTS_Annotation_Tracks.zip" file.

Description of outputs:
https://bitbucket.org/wanglab-upenn/bts-docker/wiki/BTS%20Outputs
Outputs of our analysis of 4 evaluation GWAS datasets are available in the "BTS_Results.zip" file.

• Coronary Artery Disease (CAD)
• Inflammatory Bowel Disease (IBD)
• Systemic Lupus Erythematosus (SLE)
• Rheumatoid Arthritis (RA)

BTS

## Overview

BTS is a dataset for object detection tasks - it contains D annotations for 484 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Using Python’s Selenium package (https://www.selenium.dev/), we crawled the Weverse BTS channel (https://weverse.io/bts/feed) on March 3, 2024, starting at 11:00 p.m. and continuing for two hours (from 11:13 p.m. on March 3 to 1:26 a.m. on March 4). This procedure yielded 16,020 posts and 14,223 unique user IDs.

Because our aim was to investigate the behavior of established, active users rather than recent joiners, we paused data collection for a few months. On May 24, 2024, we revisited the Weverse BTS channel and accessed each previously identified user ID via its profile page at https://weverse.com/bts/\{profile_id\}. We then crawled all posts and comments these users had written during the two-month period from March 3 to May 3. Profiles set to private or belonging to deleted accounts were inaccessible and therefore excluded, resulting in a final dataset of 3,410 users (Weverse_BTS_User_Dataset.xlsx).

In total, we collected 167,456 posts and 484,437 comments from these users. For each post or comment, the dataset includes a timestamp, text, user nickname, and user profile URL. We then filtered only English-language text and analyzed the remaining text using LIWC-22, resulting in the Weverse_BTS_LIWC_Dataset.xlsx.

The National Census of Ferry Operators (NCFO) Routes dataset was collected through December 31, 2020 and compiled on October 16, 2024 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The Ferry Routes dataset represents all ferry routes from operators that provided responses to the 2020 National Census of Ferry Operators. Areas covered by the dataset include the 50 states as well as the territories of Puerto Rico, the United States Virgin Island, and American Samoa. Each segment in the dataset connects to two terminals from the Ferry Terminals dataset, describing the route ferries travel between them. Route geometries were determined using GPS points from Automatic Identification System data, as well existing government datasets from the Census Bureau, the US Geological Survey, the National Oceanic and Atmospheric Association, and the US Army Corps of Engineers. Other routes were determined using least-cost analysis. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529042

Graph and download economic data for Leading Indicators OECD: Component series: BTS - Production: Original series for Austria (AUTLOCOBPORSTSAM) from Mar 1963 to Nov 2022 about Austria, leading indicator, origination, and production.

The Freight Analysis Framework (FAF5) - Network Links dataset was created from 2017 base year data and was published on April 11, 2022 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The FAF (Version 5) Network contains 487,384 link features. All link features are topologically connected to permit network pathbuilding and vehicle assignment using a variety of assignment algorithms. The FAF Link and the FAF Node datasets can be used together to create a network. The link features include all roads represented in prior FAF networks, and all roads in the National Highway System (NHS) and the National Highway Freight Network (NHFN) that are currently open to traffic. Other included links provide connections between intersecting routes, and to select intermodal facilities and all U.S. counties. The network consists of over 588,000 miles of equivalent road mileage. The dataset covers the 48 contiguous States plus the District of Columbia, Alaska, and Hawaii. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529027