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Trips by Distance - Daily Average by Month
data.bts.gov | Last Updated 2024-04-30T19:08:37.000ZHow 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.
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Trips by Distance
data.bts.gov | Last Updated 2024-04-30T19:08:37.000ZHow 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 mobility statistics program. 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. These data are made available under a public domain license. Data should be attributed to the "Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland and the United States Bureau of Transportation Statistics." Daily data for a given week will be uploaded to the BTS website within 9-10 days of the end of the week in question (e.g., data for Sunday September 17-Saturday September 23 would be updated on Tuesday, October 3). All BTS visualizations and tables that rely on these data will update at approximately 10am ET on days when new data are received, processed, and uploaded. The methodology used to develop these data can be found at: https://rosap.ntl.bts.gov/view/dot/67520.
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Trips by Distance - National
data.bts.gov | Last Updated 2024-04-30T19:08:37.000ZHow 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.
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Trips by Distance - Annual
data.bts.gov | Last Updated 2024-04-30T19:08:37.000ZHow 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.
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stayhome
data.bts.gov | Last Updated 2024-04-30T19:08:37.000ZHow 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.
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Impacts Assessment of Dynamic Speed Harmonization with Queue Warning: Task 3, Impacts Assessment Report [supporting datasets]
data.bts.gov | Last Updated 2019-05-24T12:43:45.000ZThe datasets in the .pdf and .zip attached to this record are in support of Intelligent Transportation Systems Joint Program Office (ITS JPO) report FHWA-JPO-15-222, "Impacts Assessment of Dynamic Speed Harmonization with Queue Warning: Task 3, Impacts Assessment Report". The files in these zip files are specifically related to the US-101 Testbed, near San Mateo, CA. The uncompressed and compressed files total 2.0265 GB in size. The files have been uploaded as-is; no further documentation was supplied by NTL. All located .docx files were converted to .pdf document files which are an open, archival format. These .pdfs were then added to the zip file alongside the original .docx files. The attached zip files can be unzipped using any zip compression/decompression software. These zip file contains files in the following formats: .pdf document files which can be read using any pdf reader; .xlsxm macro-enabled spreadsheet files which can be read in Microsoft Excel and some Tech Report spreadsheet programs; .accdb database files which may be opened with Microsoft Access Database software and Tech Report open database software applications ; as well as .db generic database files, often associated with thumbnail images in the Windows operating environment. [software requirements] These files were last accessed in 2017. File and .zip file names include: FHWA_JPO_15_222_INFLO_Performance_Measure_METADATA.pdf ; FHWA_JPO_15_222_INFLO_Performance_Measure_METADATA.docx ; FHWA_JPO_15_222_INFLO_VISSIM_Output_and_Analysis_Spreadsheets.zip ; FHWA_JPO_15_222_INFLO_Spreadsheet_PDFs.zip ; FHWA_JPO_15_222_DATA_CV50.zip ; and, FHWA_JPO_15_222_DATA_CV25.zip
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Docked Bikeshare Ridership by System, Year, and Month
data.bts.gov | Last Updated 2024-06-04T13:36:23.000ZHistoric data updated on 07/14/2023. Q4 of 2023 and data for all years on systems allowing parking outside of a docking station updated on 06/04/2024. Bikeshare ridership by system, year, and month for bikeshare systems with docking stations. Data available by month starting in January 2019. Months are rearranged to include the same number of days of the week across years (see below). Data designed to show the impacts of COVID-19 on bikeshare ridership as featured at https://maps.dot.gov/BTS/dockedbikeshare-COVID/ Ridership data not available for all docked bikeshare systems. Only docked bikeshare systems with ridership data shown. Some systems included in the data permit users to leave a bicycle outside of a docking station; these trips are indicated by the trip type. Trips defined as rides from point A to B. If user makes trip from B to A on same day, counted as a second trip. Trips labeled as round trips in Metro Bike Share and Indego trip files counted as 2 trips. Trips with no trip time are not counted. For trips starting and ending at a docking station or on systems where only docked trips are permitted, trips with no start station identifier and/or end station id are not counted in totals. Trips shorter than 1 minute or greater than 2 hours excluded. Days aligned to include the same days of weeks in 2019 and 2020. Days included in each month are as follows: Days included in each month can be found in the attachment (https://data.bts.gov/api/views/6cfa-ipzd/files/36fde1b8-57c3-4d31-b9dc-bbc896ba346e?download=true&filename=days_included_in_docked_bikeshare_monthly_summaries.xlsx) Trips beginning on 12/31/2019 but ending on 01/01/2020 not included in totals. Data visualizations available at: https://data.bts.gov/stories/s/Summary-of-Docked-Bikeshare-Trips-by-System-and-Ot/7fgy-2zkf/
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Analysis, Modeling, and Simulation (AMS) Testbed Development and Evaluation to Support Dynamic Mobility Applications (DMA) and Active Transportation and Demand Management (ATDM) Programs: San Mateo Testbed Analysis Plan [supporting datasets]
data.bts.gov | Last Updated 2019-05-24T12:45:02.000ZThis zip file contains files of data to support FHWA-JPO-16-370, Analysis, Modeling, and Simulation (AMS) Testbed Development and Evaluation to Support Dynamic Mobility Applications (DMA) and Active Transportation and Demand Management (ATDM) Programs - San Mateo Testbed Analysis Plan : Final Report. Zip size is 1.5 GB. The files have been uploaded as-is; no further documentation was supplied by NTL. All located .docx files were copied to .pdf document files which are an archival format. These .pdfs were then added to the zip file alongside the original .docx files. The attached zip files can be unzipped using any zip compression/decompression software. These zip file contains files in the following formats: .pdf document files which can be read using any pdf reader; .docx document files which may be opened with Microsoft Word or some other open source document editors; .xlsx spreadsheet files which may be opened with Microsoft Excel or some other open source spreadsheet editors; .syn files are a proprietary file format for signal timing plans which are provided in the Synchro Model given as “El Camino Real Synchro.syn” and can be opened using Trafficware Synchro, which may require users to purchase a license or software (for more information go to http://www.trafficware.com/); .csv data files, an open format, which may be opened with any text editor or in many spreadsheet applications; .db generic database files, often associated with thumbnail images in the Windows operating environment; .rbc files, which are scripts written in Rembo-C, which can be opened in a text editor, but require a server with Rembo installed to run the scripts; .vap audio files which will require special audio editing software to manipulate; .dll dynamically linked files for Windows program operations; .layx, a file type on which we could not locate reliable information; and .inpx files, a file type on which we could not locate reliable information [software requirements]. These files were last accessed in 2017. Files were accessed in 2017. Data will be preserved as is.
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State and Local Government Construction Spending - Air Transportation
data.bts.gov | Last Updated 2023-01-20T17:59:54.000ZThe 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, runways, pavement and lighting, hangars, air freight terminals, space facilities, air traffic towers, aircraft storage and maintenance buildings.
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Analysis, Modeling, and Simulation (AMS) Testbed Development and Evaluation to Support Dynamic Mobility Applications (DMA) and Active Transportation and Demand Management (ATDM) Programs — Summary Report for the Chicago Testbed [supporting datasets]
data.bts.gov | Last Updated 2019-05-24T12:50:26.000ZThe datasets in this zip file are in support of Intelligent Transportation Systems Joint Program Office (ITS JPO) report FHWA-JPO-16-385, \"Analysis, Modeling, and Simulation (AMS) Testbed Development and Evaluation to Support Dynamic Mobility Applications (DMA) and Active Transportation and Demand Management (ATDM) Programs — Evaluation Report for ATDM Program,\" https://rosap.ntl.bts.gov/view/dot/32520 and FHWA-JPO-16-388, \"Analysis, Modeling, and Simulation (AMS) Testbed Development and Evaluation to Support Dynamic Mobility Applications (DMA) and Active Transportation and Demand Management (ATDM) Programs — Evaluation Summary for the Chicago Testbed,\" https://rosap.ntl.bts.gov/view/dot/34269 \n\nThe files in this zip file are specifically related to the Chicago Testbed.\n\nThe compressed zip files total 1.6 GB in size. The files have been uploaded as-is; no further documentation was supplied by NTL. All located .docx files were converted to .pdf document files which are an open, archival format. These pdfs were then added to the zip file alongside the original .docx files.\n\nThese files can be unzipped using any zip compression/decompression software. \n\nThis zip file contains files in the following formats: \n.pdf document files which can be read using any pdf reader;\n.cvs text files which can be read using any text editor;\n.txt text files which can be read using any text editor;\n.docx document files which can be read in Microsoft Word and some other word processing programs;\n. xlsx spreadsheet files which can be read in Microsoft Excel and some other spreadsheet programs;\n.dat data files which may be text or multimedia;\nas well as GIS or mapping files in the fowlling formats: .mxd, .dbf, .prj, .sbn, .shp., .shp.xml; which may be opened in ArcGIS or other GIS software.\n[software requirements]\n\nThese files were last accessed in 2017.