This table contains data on the percent of residents aged 16 years and older mode of transportation to work for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Census Bureau, Decennial Census and American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Commute trips to work represent 19% of travel miles in the United States. The predominant mode – the automobile - offers extraordinary personal mobility and independence, but it is also associated with health hazards, such as air pollution, motor vehicle crashes, pedestrian injuries and fatalities, and sedentary lifestyles. Automobile commuting has been linked to stress-related health problems. Active modes of transport – bicycling and walking alone and in combination with public transit – offer opportunities for physical activity, which is associated with lowering rates of heart disease and stroke, diabetes, colon and breast cancer, dementia and depression. Risk of injury and death in collisions are higher in urban areas with more concentrated vehicle and pedestrian activity. Bus and rail passengers have a lower risk of injury in collisions than motorcyclists, pedestrians, and bicyclists. Minority communities bear a disproportionate share of pedestrian-car fatalities; Native American male pedestrians experience four times the death rate Whites or Asian pedestrians, and African-Americans and Latinos experience twice the rate as Whites or Asians. More information about the data table and a data dictionary can be found in the About/Attachments section.

About Transportation Services Index

The Transportation Services Index (TSI), created by the U.S. Department of Transportation (DOT), Bureau of Transportation Statistics (BTS), measures the movement of freight and passengers. The index, which is seasonally adjusted, combines available data on freight traffic, as well as passenger travel, that have been weighted to yield a monthly measure of transportation services output.

For charts and discussion on the relationship of the TSI to the economy, see our Transportation as an Economic Indicator: Transportation Services Index page (https://data.bts.gov/stories/s/TET-indicator-1/9czv-tjte)

For release schedule see: https://www.bts.gov/newsroom/transportation-services-index-release-schedule

About seasonally-adjusted data

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.

The Means of Transportation to Work dataset was compiled using information from December 08, 2022 and updated September 01, 2023 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 Means of Transportation to Work table from the 2021 American Community Survey (ACS) (5-year estimates) was joined by 2020 tract-level shapefiles for all 50 States, District of Columbia and Puerto Rico provided by the Census Bureau. A new file was created that combines the demographic variables from the former with the cartographic boundaries of the latter. The demographic variables contain count estimates for means of transportation to work, by Census tract, and the geospatial file allows these values to be visualized on a map. Percentage values for the count estimates were calculated and added, with workers 16 years and over as the denominator.

We present the dataset of bus trajectories and commuters' activity records (collected inside the bus) for the trips conducted in the Ahmedabad-Gandhinagar district of Gujarat, India. It also contains the trip demand for the Ahmedabad-Gandhinagar district of Gujarat, India. The data can be used for arrival time prediction, automatic bus-stop detection, and bus crowdedness detection for the scenarios of developing countries where Intelligent Transportation System (ITS) infrastructure deployment is limited. The automatic bus-stop detector resolves the problem of dynamic bus-stops on the routes. Moreover, the arrival time predictor can disseminate the estimated bus arrival time to the waiting commuters, and the bus crowdedness information can be utilized for scheduling and provisioning buses in crowded areas.

The data is collected using the android application, which periodically fetches the GPS records at the frequency of 1 Hz and the accelerometer records at the frequency of 40 Hz. It processes the bus trajectories to calculate arrival time prediction and all en-route stoppages of the bus trips for automatic bus-stop detection. However, it is challenging to infer whether a bus stoppage is due to bus-stops, traffic jams, or junctions. We observed that the bus-stop is a stoppage where commuter boards the bus. Therefore, the bus trajectory data and the commuters' records are opportunistically processed using the baseline classifier comprising of stepping detector, transport mode classifier, and commuter activity classifier to determine the bus boarding events and commuter's state (as standing/sitting) after boarding the bus. The software filters the en-route bus-stops using the bus boarding locations. The commuter's state (standing/sitting) is used to determine the crowdedness inside the bus. The sitting commuter's state indicates the availability of seat and a lesser level of crowdedness. In contrast, the standing commuter's state shows that the commuter did not got a seat on boarding the bus and reflects a higher crowdedness level. The software implementation uses open-source libraries and is reproducible. The link for the reproducible capsule of the implementation can be found in the related link section. The dataset information and meta-data for different records is mentioned in the respective folders.

This map presents transportation data, including highways, roads, railroads, and airports for the world.

The map was developed by Esri using Esri highway data; Garmin basemap layers; HERE street data for North America, Europe, Australia, New Zealand, South America and Central America, India, most of the Middle East and Asia, and select countries in Africa. Data for Pacific Island nations and the remaining countries of Africa was sourced from OpenStreetMap contributors. Specific country list and documentation of Esri's process for including OSM data is available to view.

Graph and download economic data for Consumer Price Index for All Urban Consumers: Public Transportation in U.S. City Average (CUUR0000SETG) from Mar 1935 to Mar 2024 about public, transportation, urban, consumer, CPI, inflation, price index, indexes, price, and USA.

Transportation data links compiled for the National Day Hackathon of Civic Hacking 2014.

The USGS Transportation downloadable data from The National Map (TNM) is based on TIGER/Line data provided through U.S. Census Bureau and supplemented with HERE road data to create tile cache base maps. Some of the TIGER/Line data includes limited corrections done by USGS. Transportation data consists of roads, railroads, trails, airports, and other features associated with the transport of people or commerce. The data include the name or route designator, classification, and location. Transportation data support general mapping and geographic information system technology analysis for applications such as traffic safety, congestion mitigation, disaster planning, and emergency response. The National Map transportation data is commonly combined with other data themes, such as boundaries, elevation, hydrography, and structures, to produce general reference base maps. The National Map viewer allows free downloads of public domain transportation data in either Esri File Geodatabase or Shapefile formats. For additional information on the transportation data model, go to https://nationalmap.gov/transport.html.

Contains tables of data for the United States, Canada, and Mexico. Data tables are divided up into 12 categories, including a country overview, transportation flows, safety, environment, trade, and infrastructure

This data set provides a 38-year, 1-km resolution inventory of annual on-road CO2 emissions for the conterminous United States based on roadway-level vehicle traffic data and state-specific emissions factors for multiple vehicle types on urban and rural roads as compiled in the Database of Road Transportation Emissions (DARTE). CO2 emissions from the on-road transportation sector are provided annually for 1980-2017 as a continuous surface at a spatial resolution of 1 km.

The Transportation Secure Data Center (TSDC) provides free access to detailed transportation data from a variety of travel surveys and studies conducted across the nation. Data include global positioning system (GPS) readings for millions of miles of travel, along with vehicle characteristics and survey participant demographics. NREL screens the initial data for quality control, translates each data set into a consistent format, and interprets the data for spatial analysis. NREL's processing routines add information on vehicle fuel economy and road grades and join data points to the road network.

As surveyed in 2023 in Indonesia, 71.65 percent of respondents used private motorcycles as their primary mode of transportation. Indonesia is known as one of the countries in Southeast Asia with the highest motorcycle use, while public transport was the main mode of transportation for only 12.25 percent of respondents.

This is a list of all Major Safety and Security Events from January of 2014 to the most recently published data within the Federal Transit Administration's major event time series.

Data in this table were compiled from APPENDIX L 2018 Regional Transportation Plan Federal performance-based planning and congestion management process produced as part of Metro's 2018 Regional Transportation Plan. Data reported here were sourced from the National Performance Management Research Dataset (NPMRDS) for the period Jan. to Dec. 2017U.S. Census Bureau American Community Survey – Journey to Work, 1‐year estimates (2017)Data were aggregated and clipped to the Portland metropolitan planning area.

All drivers reported by Transportation Network Providers (sometimes called rideshare companies) to the City of Chicago as part of the licensing process and routine reporting required by ordinance. Inclusion of a driver in a monthly report indicates that the driver was eligible for trips in Chicago in that month for at least one day, regardless of whether he or she actually provided any rides. If a driver is eligible in multiple months, which is common, he or she will have records in each of these reporting months. As of the creation of this dataset, there have been four TNPs licensed to operate in Chicago, although never more than three at any given time and currently three. Drivers reported by more than one company in the same month are combined and the MULTIPLE_TNPS column is marked as TRUE. However, the matching process is imperfect so not all such drivers are necessarily identified. Similarly, matching between the licensing and routine reporting databases is imperfect, creating potential for occasional errors. The reporting is done on a monthly basis, as indicated in the MONTH_REPORTED column. However, starting in 2018, the reports are batched and files for all three months in a quarter are delivered to the City of Chicago at the end of each quarter. Due to an issue in this transition, some vehicle records for Q2 2018 (April-June) were reported for the quarter as a whole, rather than for individual months. For purposes of this dataset, those records have been assigned to 2018-06 (June). Therefore, some caution in interpreting this month and the quarter as a whole is advised.

Introduction:

The "Department of Transportation Inventory of Artificial Intelligence Use Cases" dataset serves as a comprehensive compilation of various Artificial Intelligence (AI) use cases across different agencies within the Department of Transportation (DOT) in the United States. This dataset is in compliance with the directives outlined in Executive Order 13960, titled "Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government." According to Section 5 of this executive order, federal agencies are mandated to create and share inventories of their AI use cases, fostering transparency and collaboration across the government.

Dataset Overview:

The dataset encompasses the following key columns and variables:

1. AI Use Case Name
2. Agency With AI Use Case
3. Optional Note Field
4. Office With AI Use Case
5. Summary
6. Current Stage Of Production
7. Lifecycle Stage Additional Comments
8. AI Techniques Used
9. Agency Training Used
10. Training Data Origins
11. Included In Enterprise Inventory
12. Public Data Link
13. Use Case Code Accessible
14. Code Included In Agency Inventory
15. Source Code Public Link
16. Agency Able To Test Code
17. Agency Able To Monitor/Audit
18. FISMA System Name
19. Withhold Use Case
20. Explanation To Withhold Use Case

License:

The dataset, as part of the federal initiative to promote trustworthy AI, is made available with a license that encourages transparency, collaboration, and responsible use. Users are encouraged to adhere to ethical standards and legal requirements when utilizing the dataset. It is essential to acknowledge the source and the Department of Transportation when using or referencing this dataset in any analysis, research, or publications.

Significance and Use:

This dataset plays a crucial role in advancing the understanding and implementation of AI technologies within the federal government, specifically within the Department of Transportation. By providing a detailed inventory of AI use cases, it enables transparency, accountability, and knowledge sharing among different agencies. Researchers, policymakers, and the public can leverage this dataset to gain insights into the diverse applications of AI, the associated techniques, and the current stages of production. Furthermore, it supports collaborative efforts in enhancing the quality of AI applications, fostering innovation, and contributing to the broader goals of economic growth and improved quality of life for citizens.

Note:

It's advisable to check the specific license terms associated with the dataset, which might be provided separately or in the documentation accompanying the dataset.

"In the event of original dataset owners' dissatisfaction, this dataset will be withdrawn."

Transportation Analysis Zones (TAZs) are used in the Metropolitan Transportation Commission travel and Urban Sim models for the San Francisco Bay Region. These models are used in processes to forecast population growth, economic growth, and transportation/transit capacity and responsiveness and then distribute those results throughout the region.Data tables containing modeling results are joined to the TAZ features using a zone id (taz1454). Tables currently available for download are as follows:Plan Bay Area 2040 ForecastEmployment (total employment, TAZ resident employment, retail employment, financial and professional services employment, health, educational, and recreational employment, manufacturing, wholesale, and transportation employment, agricultural and natural resources employment, and other employment)Households (number of households and household income quartile)Land Use and Transportation (area type, commercial or industrial acres, residential acres, number of single-family and multi-family dwelling units, time to get from automobile storage location to origin/destination, and hourly parking rates)Population and Demographics (total population, household and group quarter populations, population by age group, share of population that is 62+, high school enrollment, and college enrollment)

Italians do not seem to love urban public transportation. This is what emerged from a survey conducted in May 2020, highlighting how the percentage of frequent users (at least three times a week) was as low as 21 percent. Only 12 percent of the Italian population chooses this as their daily means of transportation, while 27 percent of the respondents declared to use public transport less often than once a month. Another 27 percent does not use it at all.

In 2022, almost 67 percent of workers in the U.S. commuted by personal vehicle on their own. During the same year, only 3.1 percent of Americans workers traveled to work using public transportation. Meanwhile, around 15 percent of U.S. Americans worked from home.