- API data.bts.gov | Last Updated 2020-06-26T19:23:27.000Z
Total and percent of rural population with access to scheduled intercity bus, rail, and air transportation. Rural areas are Census block groups with their centroid (center) outside of all Census urban areas. Summarized to county level. Facilities used available at: https://data.transportation.gov/Research-and-Statistics/Intercity-Air-Bus-and-Rail-Transportation-Faciliti/xnub-2sc4. Interactive map showing access to intercity transportation in rural areas: https://datahub.transportation.gov/stories/s/Rural-Access-to-Intercity-Transportation/gr9y-9gjq/edit Methodology: https://datahub.transportation.gov/stories/s/dbb4-pr2c
- API data.bts.gov | Last Updated 2020-06-23T19:43:24.000Z
This dataset provides interactive data on the value and weight of shipments by the port of entry or exit of U.S exports and imports by commodity. FREQUENTLY ASKED QUESTIONS: https://www.bts.gov/statistical-products/transborder-freight-data/north-american-transborder-freight-data-faqs TRANSBORDER RAW DATA: https://www.bts.gov/topics/transborder-raw-data CODES FOR NORTH AMERICAN TRANSBORDER FREIGHT RAW DATA: https://www.bts.gov/browse-statistical-products-and-data/transborder-freight-data/all-codes-north-american-transborder
- API data.bts.gov | Last Updated 2020-06-16T20:36:05.000Z
Total value of merchandise freight for United States exports to and imports from Canada. The Bureau of Transportation Statistics releases freight data using a special tabulation of official international trade statistics that are collected by the U.S. Customs and Border Protection and processed and validated by the U.S. Census Bureau, Foreign Trade Division.
- API data.bts.gov | Last Updated 2020-06-16T20:36:52.000Z
Regular gasoline price is the average retail price for regular grade formulations. The U.S. Energy Information Administration releases weekly gasoline and diesel price estimates.
- API data.bts.gov | Last Updated 2019-05-24T12:30:48.000Z
The goal of the SHRP 2 Project L33 Validation of Urban Freeway Models was to assess and enhance the predictive travel time reliability models developed in the SHRP 2 Project L03, Analytic Procedures for Determining the Impacts of Reliability Mitigation Strategies. SHRP 2 Project L03, which concluded in 2010, developed two categories of reliability models to be used for the estimation or prediction of travel time reliability within planning, programming, and systems management contexts: data-rich and data-poor models. The objectives of Project L33 were the following: • The first was to validate the most important models – the “Data Poor” and “Data Rich” models with new datasets. • The second objective was to assess the validation outcomes to recommend potential enhancements. • The third was to explore enhancements and develop a final set of predictive equations. • The fourth was to validate the enhanced models. • The last was to develop a clear set of application guidelines for practitioner use of the project outputs. The datasets in these 5 zip files are in support of SHRP 2 Report S2-L33-RW-1, Validation of Urban Freeway Models, https://rosap.ntl.bts.gov/view/dot/3604 The 5 zip files contain a total of 60 comma separated value (.csv) files. The compressed zip files total 3.8 GB in size. The files have been uploaded as-is; no further documentation was supplied. These files can be unzipped using any zip compression/decompression software. The files can be read in any simple text editor. [software requirements] Note: Data files larger than 1GB each. Direct data download links: L03-01: https://doi.org/10.21949/1500858 L03-02: https://doi.org/10.21949/1500868 L03-03: https://doi.org/10.21949/1500869 L03-04: https://doi.org/10.21949/1500870 L03-05: https://doi.org/10.21949/1500871
- API data.bts.gov | Last Updated 2020-06-23T19:40:50.000Z
This dataset provides interactive data on the value and weight of shipments by the origin or destination state of U.S exports and imports by commodity. FREQUENTLY ASKED QUESTIONS: https://www.bts.gov/statistical-products/transborder-freight-data/north-american-transborder-freight-data-faqs TRANSBORDER RAW DATA: https://www.bts.gov/topics/transborder-raw-data CODES FOR NORTH AMERICAN TRANSBORDER FREIGHT RAW DATA: https://www.bts.gov/browse-statistical-products-and-data/transborder-freight-data/all-codes-north-american-transborder
- API data.bts.gov | Last Updated 2020-05-07T21:45:01.414Z
Freight Facts and Figures - Freight Transportation Energy Use and Environmental Impacts
- API data.bts.gov | Last Updated 2020-06-16T20:36:13.000Z
Customer on-time performance is the percentage of customers who arrive at their detraining stations on time. An Acela train is late when it arrives at a station more than 10 minutes after its scheduled time; a Northeast Regional or State-Supported train is late when it arrives more than 15 minutes after its scheduled time. For years before FY 2018, on-time performance is the percentage of total train arrivals on-time at each station, with every arrival weighted equally. Amtrak reports system performance in monthly Host Railroad Reports.
- API data.bts.gov | Last Updated 2020-06-16T20:36:03.000Z
Personal spending on gasoline and other energy goods includes spending on motor vehicle fuels, lubricants, and fluids; fuel oil; and other fuels. The Bureau of Economic Analysis estimates personal consumption expenditures, the primary measure of consumer spending on goods and services in the U.S. economy, for each quarter and releases new statistics every month. Quarterly PCE data are seasonally adjusted at annual rates to remove the effects of normal seasonal variation.
Analytical Procedures for Determining the Impacts of Reliability Mitigation Strategies [supporting datasets]data.bts.gov | Last Updated 2019-05-24T12:42:20.000Z
The objective of this project was to develop technical relationships between reliability improvement strategies and reliability performance metrics. This project defined reliability, explained the importance of travel time distributions for measuring reliability, and recommended specific reliability performance measures. The research reexamined the contribution of the various causes of nonrecurring congestion on urban freeway sections, however, some attention was also given to rural highways and urban arterials). Numerous actions that can potentially reduce nonrecurring congestion were identified with an indication of their relative importance. Models for predicting nonrecurring congestion were developed using three methods, all based on empirical procedures: The first involved before and after studies; the second was termed a 'data poor' approach and resulted in a parsimonious and easy-to-apply set of models; the third was entitled a 'data rich model' and used cross-section inputs including data on selected factors known to directly affect nonrecurring congestion. An important conclusion of the study is that actions to improve operations, reduce demand, and increase capacity all can improve travel time reliability. The 3 attached zip files contains comma separated value (.csv) files of data to support SHRP 2 report S2-L03-RR-1, Analytical procedures for determining the impacts of reliability mitigation strategies.Zip size is 1.83 MB. Files were accessed in Microsoft Excel 2016. Data will be preserved as is. To view publication see: https://rosap.ntl.bts.gov/view/dot/3605