VITAL SIGNS INDICATOR
Commute Mode Choice (T1)

FULL MEASURE NAME
Commute mode share by residential location

LAST UPDATED
January 2023

DESCRIPTION
Commute mode choice, also known as commute mode share, refers to the mode of transportation that a commuter usually uses to travel to work, such as driving alone, biking, carpooling or taking transit. The dataset includes metropolitan area, regional, county, city and census tract tables by place of residence.

DATA SOURCE
U.S. Census Bureau: Decennial Census (1960, 1970) - via MTC/ABAG Bay Area Census - http://www.bayareacensus.ca.gov/transportation/Means19602000.htm

U.S. Census Bureau: Decennial Census (1980-2000) - via MTC/ABAG Bay Area Census - http://www.bayareacensus.ca.gov/transportation/Means19802000.htm

U.S. Census Bureau: American Community Survey - https://data.census.gov/
2006-2021
Form B08301 (1-year and 5-year)

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Commute mode choice, also known as commute mode share, refers to the mode of transportation that a commuter usually uses to travel to work, such as driving alone, biking, carpooling or taking transit. For the decennial Census datasets, the breakdown of auto commuters between drive alone and carpool is not available before 1980. American Community Survey 1-year data is used for larger geographies – Bay counties and most metropolitan area counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. This will result in discrepancies in cases like San Francisco where it is both a city and a county. Note that 2020 data uses the 5-year estimates because the ACS did not collect 1-year data for 2020. Additionally, for the County by place of residence breakdown, Napa was missing ACS 1-Year commute mode choice data for all modes for 2007, 2008, 2011 and 2021. 5-Year estimates were used to fill the missing data for 2011 and 2021, but not 2007 or 2008 since the 5-Year estimates start in 2009.

Regional mode shares are population-weighted averages of the nine counties' modal shares. "Auto" includes drive alone and carpool for the simple data tables and is broken out in the detailed data tables accordingly, as it was not available before 1980. "Transit" includes public operators (Muni, BART, etc.) and employer-provided shuttles (e.g., Google shuttle buses). "Other" includes motorcycle, taxi, and other modes of transportation; bicycle mode share was broken out separately for the first time in the 2006 data and is shown in the detailed data tables. Census tract data is not available for tracts with insufficient numbers of residents or workers.

The metropolitan area comparison was performed for the nine-county San Francisco Bay Area in addition to the primary metropolitan statistical areas (MSAs) for other major metropolitan areas.

This commuter mode share data shows the estimated percentages of commuters in Champaign County who traveled to work using each of the following modes: drove alone in an automobile; carpooled; took public transportation; walked; biked; went by motorcycle, taxi, or other means; and worked at home. Commuter mode share data can illustrate the use of and demand for transit services and active transportation facilities, as well as for automobile-focused transportation projects.

Driving alone in an automobile is by far the most prevalent means of getting to work in Champaign County, accounting for over 69 percent of all work trips in 2023. This is the same rate as 2019, and the first increase since 2017, both years being before the COVID-19 pandemic began.

The percentage of workers who commuted by all other means to a workplace outside the home also decreased from 2019 to 2021, most of these modes reaching a record low since this data first started being tracked in 2005. The percentage of people carpooling to work in 2023 was lower than every year except 2016 since this data first started being tracked in 2005. The percentage of people walking to work increased from 2022 to 2023, but this increase is not statistically significant.

Meanwhile, the percentage of people in Champaign County who worked at home more than quadrupled from 2019 to 2021, reaching a record high over 18 percent. It is a safe assumption that this can be attributed to the increase of employers allowing employees to work at home when the COVID-19 pandemic began in 2020.

The work from home figure decreased to 11.2 percent in 2023, but which is the first statistically significant decrease since the pandemic began. However, this figure is still about 2.5 times higher than 2019, even with the COVID-19 emergency ending in 2023.

Commuter mode share data was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Means of Transportation to Work.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (18 September 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (10 October 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (14 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (26 March 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (26 March 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).

In 2022, the percentage of workers in the U.S. who used public transportation to travel to and from work amounted to a scant *** percent, down from over five percent in the mid-2010s. The public transport share has, however, increased from 2021, when only *** percent of commuters travelled by public transport.

The share of commuters walking or cycling to work remained relatively stable at a low level between 2010 and 2020 in the United States. Walking and cycling shares dropped slightly in 2021 to *** percent and *** percent, respectively, as more Americans worked from home during this period. They have risen slightly since but have remained below 2020 levels.

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.

According to the Statista Consumer Insights, for the period between October 2023 and September 2024, around of U.S. American workers spent an average of **** an hour or less commuting to work. In the period between 2019 and 2024, the share of workers commuting less than ** minutes dropped by ***** percentage points to ** percent, while the share of workers commuting over **** an hour decreased from ** to ** percent. Rise of hybrid work models The transformation in commute times coincides with a surge in hybrid work arrangements. By the second quarter of 2024, ** percent of U.S. workers reported adopting a hybrid work model, blending remote and on-site work. This shift, initially sparked by the COVID-19 pandemic, has reshaped how Americans balance their professional and personal lives, offering increased flexibility and potentially reducing overall commute times for many. Driving remains most common form of commuting Among those workers who continue to travel to their place of work, driving remained the most popular mode. Over ********** of U.S. Americans drove to work by car, truck or van in 2022 and an additional nearly **** percent used a carpool to get to their job. Public transportation, meanwhile, was only used by *** percent of workers.

Data on manner of commute- drive alone, carpool, public transportation, walk, etc. Excerpted from DP03 American Community Survey 1 Year Estimates

This layer shows workers' place of residence by commute length. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of commuters whose commute is 90 minutes or more. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B08303Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

This statistic represents the percentage of workers in the United States who traveled to and from work by carpool from 2007 to 2016. Around nine percent used this method of traveling to and from work in 2016.

** percent of U.S. respondents answer our survey on "Most common modes of transportation for commuting" with "Own / household car". The survey was conducted in 2025, among ***** consumers.

Smart Commute Market Outlook

The global smart commute market size was valued at approximately USD 30 billion in 2023 and is projected to reach USD 70 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 10% during the forecast period. The growth of the smart commute market is fueled by rising urbanization and the increasing need for efficient and sustainable transportation solutions.

One of the major growth factors driving the smart commute market is the rapid urbanization occurring globally. As cities become more populated, the demand for efficient transportation systems that can reduce congestion, minimize travel time, and lower carbon emissions has become critical. Smart commuting solutions, such as carpooling, vanpooling, and bike-sharing, offer viable alternatives to traditional commuting methods, thus addressing the issues of traffic congestion and environmental pollution.

Another significant growth factor is advancements in technology, particularly in mobile applications and software platforms. The integration of real-time data analytics, GPS tracking, and IoT devices into smart commute solutions enhances their efficiency and user experience. These technological advancements allow commuters to plan their journeys more effectively, optimize routes, and receive real-time updates on traffic conditions, thereby contributing to the market's growth.

Government initiatives and policies aimed at promoting sustainable transportation are also playing a crucial role in the growth of the smart commute market. Many governments are implementing policies to reduce the reliance on single-occupancy vehicles and promote shared and public transportation. Subsidies, tax incentives, and investments in infrastructure for smart commuting solutions are further driving market growth.

From a regional perspective, North America is expected to dominate the smart commute market during the forecast period, followed by Europe and the Asia Pacific region. The high adoption rate of advanced technologies, supportive government policies, and the presence of major market players contribute to the growth of the smart commute market in North America. Meanwhile, the Asia Pacific region is anticipated to witness the highest growth rate due to rapid urbanization, increasing disposable incomes, and growing awareness about sustainable transportation solutions.

Mode of Transportation Analysis

The mode of transportation segment is crucial in the smart commute market, encompassing options like carpooling, vanpooling, bike sharing, public transit, and others. Carpooling has emerged as one of the most popular modes of smart commuting due to its cost-effectiveness and its ability to significantly reduce the number of vehicles on the road. Carpooling platforms utilize advanced algorithms to match commuters with similar routes, thereby optimizing vehicle occupancy and reducing traffic congestion.

Vanpooling is another significant mode of transportation within the smart commute market. It is especially popular among corporate employees and large enterprises that offer vanpool services as part of their employee benefits. Vanpooling not only reduces the number of vehicles on the road but also provides a more comfortable and convenient commuting option for employees. The cost savings and environmental benefits of vanpooling make it a preferred choice for many organizations.

Bike sharing has gained substantial traction in urban areas and is considered an eco-friendly mode of transportation. The availability of electric bikes and the expansion of bike-sharing networks have made bike sharing a viable option for short-distance travel. Many cities are investing in bike-sharing infrastructure, including dedicated bike lanes and parking stations, to promote cycling as a primary mode of transportation.

Public transit remains a cornerstone of smart commuting, offering an efficient means of transportation for large numbers of people. The integration of smart technologies into public transit systems, such as mobile ticketing, real-time tracking, and automated fare collection, has enhanced the user experience and operational efficiency. These innovations are driving the adoption of public transit as a preferred mode of smart commuting.

Other modes of transportation, including ride-sharing and micro-mobility options like electric scooters, are also contributing to the growth of the smart commute market. These modes provide flexibility and convenience, particularly for last-mile co

This story map was produced by Esri's story maps team using the Side Accordion application template. For more information visit the story maps website.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

Over the past five years, public transportation services have faced significant fluctuations. The COVID-19 pandemic brought unprecedented challenges, severely reducing ridership as lockdowns and business closures curtailed daily commuting. Government intervention played a critical role in maintaining and revitalizing public transit. The Biden administration's Infrastructure Investment and Jobs Act, signed in 2021, earmarked $1.0 trillion for infrastructure and transportation initiatives over the next decade. Increased federal funding in 2023 helped offset previous declines in economic performance, which cut state budgets and pressured public transport. As the economy recovered, higher disposable income led some commuters to choose premium transport options like Uber and Lyft, particularly in cities with ride-sharing restrictions. Revenue has been declining by a CAGR of 2.5% over the past five years, and is expected to decrease by 2.0%, reaching $83.3 billion in 2024. In 2024, public transportation is poised to stabilize. Federal and state initiatives to overhaul safety and environmental standards will stay center stage. Notably, New York City's congestion pricing tax aims to alleviate city congestion by charging drivers fees ranging from $15 to $36, depending on vehicle size. The tax will direct approximately 80% of the generated revenue towards enhancing the NYC subway and bus network. Government investments in hybrid and all-electric buses gain momentum, aligning with broader goals to reduce carbon footprints and improve urban livability. Elevated crime levels in major cities like New York and Philadelphia remain a concern, prompting increased security measures to protect commuters and enhance public confidence in public transit. Consequently, profit for public transportation is expected to remain stagnant. Looking ahead, the public transportation sector will navigate a complex landscape shaped by urbanization, environmental imperatives and economic conditions. Urban populations in major cities are projected to rise, intensifying traffic volumes and making public transportation systems indispensable. Policymakers are expected to continue prioritizing the reduction of carbon emissions by transitioning to low-emission public transport vehicles bolstered by federal investments in renewable energy. As consumers become more financially aware of persistent inflation and high credit card debt, public transportation's affordability may attract budget-conscious riders, further boosting the industry. Industry revenue is set to expand by a CAGR of 2.1% to an estimated $92.5 billion through the end of 2029.

Commute mode is tracked by the American Community Survey (ACS) by asking respondents to provide the means of transportation usually used to travel the longest distance to work the prior week. A follow-up question asks about vehicle occupancy when "car, truck, van" is selected. This dataset tracks the sum of all individuals not selecting "car, truck, van" with one person in it. Transportation professionals often group travel modes into "single-occupancy vehicles" (SOV) and "non-single-occupancy vehicles" (non-SOV) because SOVs are a less efficient use of roadway and environmental resources. It also shows the share of modes that are classified as non-SOV.

The global commuter rail and bus services market is experiencing robust growth, driven by increasing urbanization, rising disposable incomes, and growing concerns about traffic congestion and environmental sustainability. The market's expansion is further fueled by government initiatives promoting public transportation, technological advancements in ticketing and fleet management systems, and the increasing adoption of electric and hybrid vehicles to reduce carbon emissions. While the exact market size in 2025 is unavailable, a logical estimation based on industry trends and assuming a moderate CAGR of 5% (a common rate for this sector) from a hypothetical 2019 market size of $150 billion, results in an estimated 2025 market value of approximately $198 billion. This growth is segmented across various regions, with North America and Europe holding significant shares, driven by well-established public transportation networks and substantial investments in infrastructure improvements. However, the market also faces challenges such as aging infrastructure in some regions, fluctuating fuel prices, and the need for continued investment in workforce training and development to meet operational demands. Despite these restraints, the long-term outlook for the commuter rail and bus services market remains positive. The continuous development of smart city initiatives, the integration of advanced data analytics for optimized route planning and service delivery, and the increasing popularity of mobility-as-a-service (MaaS) platforms are poised to significantly shape the industry's future. Furthermore, the growing focus on accessibility and inclusivity within public transportation systems, along with the implementation of contactless payment options and improved passenger information systems, contribute to the market's overall upward trajectory. The projected growth from 2025-2033 will likely see a gradual increase in market share for companies specializing in innovative technologies and sustainable solutions within the sector.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

Only a small minority of the U.S. population uses an e-scooter to commute to work. This share is slightly higher among men than women, while * percent of men commuted by e-scooter, only * percent of women traveled to work by shared or personal e-scooter.

This repository contains data relating to our book chapter on the anatomy of US megaregions, entitled: "On the road again: the geography and characteristics of American commuter megaregions".We are sharing the full datasets here for others to explore.There are four different files here, as below.1. megaregions_data_master_011217.xlsx - this file contains full details of the megaregions in relation to their characteristics - including demographics and transport modes.2. commutes-matrix.jpg - this is a graphic from the associated paper which shows the strength of connection between different megaregions.3. megaregions-and-cities.jpg - this is a graphic which shows the shape and size of megaregions in addition to their major cities.4. Megaregion Interflows.xlsx - this file provides more detail on the number of commutes between the megaregions.Please get in touch if you have any questions. You can find our previous work on this topic in another figshare repository, here: https://figshare.shef.ac.uk/articles/United_States_Commutes_and_Megaregions_data_for_GIS/4110156