This map contains the 2020 Vulnerable Population Index along with the component demographic layers. The following seven populations were determined to be vulnerable based on an understanding of both federal requirements and regional demographics: 1) Low-Income Population (below 200% of poverty level) 2) Non-Hispanic Minority Population 3) Hispanic or Latino Population (all races) 4) Population with Limited English Proficiency (LEP) 5) Population with Disabilities 6) Elderly Population (age 75 and up) 7) Households with No CarFor each of these populations, Census tracts with concentrations above the regional mean concentration are divided into two categories above the regional mean. These categories are calculated by dividing the range of values between the regional mean and the regional maximum into two equal-sized intervals. Tracts in the lower interval are given a score of 1 and tracts in the upper interval are given a score of 2 for that demographic variable. The scores are totaled from the seven individual demographic variables to yield the Vulnerable Population Index (VPI). The VPI can range from zero to fourteen (0 to 14). A lower VPI indicates a less vulnerable area, while a higher VPI indicates a more vulnerable area.FIELDSP_PovL100: Percent Below 100% of the Poverty Level, P_PovL200: Percent Below 200% of the Poverty Level, P_Minrty: Percent Minority (non-White, non-Hispanic), P_Hisp: Percent Hispanic, P_LEP: Percent Limited English Proficiency (speak English "not well" or "not at all"), P_Disabld: Percent with Disabilities, P_Elderly: Percent Elderly (age 75 and over), P_NoCarHH: Percent Households with No Vehicle, RG_PovL100: Regional Average (Mean) of Percent Below 100% of the Poverty Level, RG_PovL200: Regional Average (Mean) of Percent Below 200% of the Poverty Level, RG_Minrty: Regional Average (Mean) of Percent Minority (non-White, non-Hispanic), RG_Hisp: Regional Average (Mean) of Percent Hispanic, RG_LEP: Regional Average (Mean) of Percent Limited English Proficiency (speak English "not well" or "not at all"), RG_Disabld: Regional Average (Mean) of Percent with Disabilities, RG_Elderly: Regional Average (Mean) of Percent Elderly (age 75 and over), RG_NoCarHH: Regional Average (Mean) of Percent Households with No Vehicle, [NO SC_PovL100: Note: Percent Below 100% of the Poverty Level not used in VPI 2020 calculation],SC_PovL200: VPI Score for Below 200% of the Poverty Level (Values: 0, 1, or 2),SC_Minrty: VPI Score for Minority (non-White, non-Hispanic) (Values: 0, 1, or 2),SC_Hisp: VPI Score for Hispanic (Values: 0, 1, or 2),SC_LEP: VPI Score for Limited English Proficiency (speak English "not well" or "not at all") (Values: 0, 1, or 2),SC_Disabld: VPI Score for Disabilities (Values: 0, 1, or 2),SC_Elderly: VPI Score for Elderly (age 75 and over) (Values: 0, 1, or 2),SC_NoCarHH: VPI Score for Households with No Vehicle (Values: 0, 1, or 2),VPI_2020: Total VPI Score (0 minimum to 14 maximum).Additional information on equity planning at BMC can be found here.Sources: Baltimore Metropolitan Council, U.S. Census Bureau 2016–2020 American Community Survey 5-Year Estimates. Margins of error are not shown.Updated: April 2022

This map contains the 2020 Vulnerable Population Index along with the component demographic layers. The following seven populations were determined to be vulnerable based on an understanding of both federal requirements and regional demographics: 1) Low-Income Population (below 200% of poverty level) 2) Non-Hispanic Minority Population 3) Hispanic or Latino Population (all races) 4) Population with Limited English Proficiency (LEP) 5) Population with Disabilities 6) Elderly Population (age 75 and up) 7) Households with No CarFor each of these populations, Census tracts with concentrations above the regional mean concentration are divided into two categories above the regional mean. These categories are calculated by dividing the range of values between the regional mean and the regional maximum into two equal-sized intervals. Tracts in the lower interval are given a score of 1 and tracts in the upper interval are given a score of 2 for that demographic variable. The scores are totaled from the seven individual demographic variables to yield the Vulnerable Population Index (VPI). The VPI can range from zero to fourteen (0 to 14). A lower VPI indicates a less vulnerable area, while a higher VPI indicates a more vulnerable area.FIELDSP_PovL100: Percent Below 100% of the Poverty Level, P_PovL200: Percent Below 200% of the Poverty Level, P_Minrty: Percent Minority (non-White, non-Hispanic), P_Hisp: Percent Hispanic, P_LEP: Percent Limited English Proficiency (speak English "not well" or "not at all"), P_Disabld: Percent with Disabilities, P_Elderly: Percent Elderly (age 75 and over), P_NoCarHH: Percent Households with No Vehicle, RG_PovL100: Regional Average (Mean) of Percent Below 100% of the Poverty Level, RG_PovL200: Regional Average (Mean) of Percent Below 200% of the Poverty Level, RG_Minrty: Regional Average (Mean) of Percent Minority (non-White, non-Hispanic), RG_Hisp: Regional Average (Mean) of Percent Hispanic, RG_LEP: Regional Average (Mean) of Percent Limited English Proficiency (speak English "not well" or "not at all"), RG_Disabld: Regional Average (Mean) of Percent with Disabilities, RG_Elderly: Regional Average (Mean) of Percent Elderly (age 75 and over), RG_NoCarHH: Regional Average (Mean) of Percent Households with No Vehicle, [NO SC_PovL100: Note: Percent Below 100% of the Poverty Level not used in VPI 2020 calculation],SC_PovL200: VPI Score for Below 200% of the Poverty Level (Values: 0, 1, or 2),SC_Minrty: VPI Score for Minority (non-White, non-Hispanic) (Values: 0, 1, or 2),SC_Hisp: VPI Score for Hispanic (Values: 0, 1, or 2),SC_LEP: VPI Score for Limited English Proficiency (speak English "not well" or "not at all") (Values: 0, 1, or 2),SC_Disabld: VPI Score for Disabilities (Values: 0, 1, or 2),SC_Elderly: VPI Score for Elderly (age 75 and over) (Values: 0, 1, or 2),SC_NoCarHH: VPI Score for Households with No Vehicle (Values: 0, 1, or 2),VPI_2020: Total VPI Score (0 minimum to 14 maximum).Additional information on equity planning at BMC can be found here.Sources: Baltimore Metropolitan Council, U.S. Census Bureau 2016–2020 American Community Survey 5-Year Estimates. Margins of error are not shown.Updated: April 2022

StoryMap link:https://arcg.is/1OXPW1

This dataset contains the Hampton Roads Transportation Planning Organization (HRTPO) 9 Environmental Justice (EJ) Indicators (Carless Households, Cash Public Assistance Households, Disabled Population, Elderly Population, Female Head of Household, Food Stamps/SNAP Household, Limited English Proficiency Population, Minority Population, and Low-Income/Poverty Households) at the Census Block Group level. The U.S. Census data source uses the 2017-2021 ACS 5-Year Estimates. The dataset includes Youth Population, which is not an EJ Indicator but is used in the Transportation Challenges and Strategies Long-Range Transportation Plan (LRTP) report. This data will be used for the HRTPO 2050 LRTP, for planning purposes only.

Title VI - Environmental Justice Framework

Applied to 2050 Long-Range Transportation Plan

All Census information is as of April 1, 2012. Ages of residents are effective April 1, 2012. No data on any individual residence will be released. To protect the privacy of individuals, data is compiled and presented at the city, ward and neighbourhood level only. Neighbourhood results with a population under 49 are not posted to protect the information collected. The designation of “No Response” includes households from which no census data could be collected or only partial data was available. Bulk-count data from multiple-resident facilities (such as group homes, transient drop-in centres, residential hotels or criminal detention centres) may be limited when personal information cannot be obtained (i.e. information is not available or cannot be released by the facility administration).

This analysis scores Census Block Groups in Washington based on their degree of equity and environmental justice need for the purpose of identifying and prioritizing investment locations for the Connecting Communities Pilot Program. Each Block Group receives a score based on several factors related to vulnerable populations and environmentally burdened communities, and these scores are added together to create the final score. See the accompanying methodology word document for a full list of factors. Original data sources are the U.S. Census 2016-2020 American Community Survey (ACS) and the Washington Environmental Health Disparities (EHD) Map.Individual scores are calculated for each measure, which then sum up to aggregate scores for vulnerable populations and overburdened communities as well as a combined final score. Block Group scores based on demographic measures from the ACS data are calculated relative to other Block Groups in similarly sized population centers or in tribal areas. If a Block Group’s value for a given demographic measure is at or above the 80th percentile within its population center size category, it is given 2 points for that factor. If its value is at or above the 60th percentile within its population center size category, it is given 1 point. All other Block Groups receive 0 points for that factor. Block groups that overlap with or touch multiple population centers that have different sizes are assigned the highest possible point value based on all overlapping population centers. For the health and environmental measures sourced from the EHD map, scores are applied based on the measure’s rank value. Block Groups with a rank of 9 or 10 are given 2 points, and Block Groups with a rank of 7 or 8 are given 1 point. This is applied statewide without any scaling within population center sizes, as is performed for the demographic metrics, to ensure that Block Groups with similar environmental or health burdens across the state are scored evenly. Here is a list of measures (included in attribute table), used to calculate the final score: 1. Population less than 18 years of age; 2. Population age 65 or older; 3. Housing cost-burdened households (spending over 30% of income on housing); 4. Black, Indigenous, People of color; 5. Households with 1 or more persons with a disability; 6. Ability to speak English – less than very well; 7. Household income below 200% of the federal poverty level; 8. Zero to one car households; 9. Unemployment; 10. Transportation expense (%) for moderate income families; 11. Limited access to healthy food; 12. Low birthweight (<2500 grams); 13. High rate of hospitalization, based on the maximum rank value from the following variables; (a) Death from cardiovascular disease, (b) Cancer deaths, (c) Lower life expectancy at birth, (d) Premature death; 14. Environmental exposures; 15. Environmental effects; 16. Diesel pollution burdenFinally, 1 additional point is given to Block Groups that fall within or touch a tribal area to give a slight priority to areas serving tribal populations. This score, along with the demographic measures from the ACS as well as the transportation expense, limited access to healthy food, low birthweight, and high rate of hospitalization measures from the EHD Map are summed together to create the total vulnerable population score. The three environmental measures from the EHD Map are summed together to create the total overburdened communities score. These two totals are summed to create the Block Group’s final score.

The Vulnerable Population Index (VPI) is intended to guide location selection and stakeholder identification for public involvement and inform Title VI and Environmental Justice (EJ) performance measurement. The Baltimore Regional Transportation Board uses data from the US Census Bureau to determine the concentrations of seven sensitive populations for the region and for each census tract. A tract with a concentration of a sensitive population greater than the concentration of the Baltimore region as a whole is considered to be “vulnerable” for the sensitive population. The Vulnerable Population Index (VPI) indicated the number of vulnerable populations for each tract, and thus provides a general indication of the extent to which each tract is vulnerable. The VPI looks at the following variables:Population in Poverty (American Community Survey 2006-2010 5-Year Estimates)Age 75 and up (Census 2010) Non-Hispanic Minority (people who are non-White and non-Hispanic) (Census 2010) Hispanic or Latino Heritage (Census 2010)Limited English Proficiency (population who speaks English “not well” or “not at all.”) (American Community Survey 2006-2010 5-Year Estimates)Households with No Car (American Community Survey 2006-2010 5-Year Estimates)Disabled Population (Census 2000) This data was used in the interactive mapping application found at http://gis.baltometro.org/Application/VPI/index.html. For more information on Transportation Equity work and studies at BMC, go to http://www.baltometro.org/about-the-brtb/transportation-equity. Note that for ACS and Census 2000 data margins of error are not provided. This data has been modified by the Baltimore Metropolitan Council and should not replace data directly loaded from the Census.Source: Variables are American Community Survey 2006-2010 5-Year Estimates, the 2000 Census (SF3), and the 2010 Census. Census tracts are the 2010 Census. Main Index is calculated by BMC.Date: Index published in May 2015. Date of raw data is either 2000, 2010, or 2006-2010 depending on the variable. See the above list for more information.Update: The VPI is updated approximately every 5 years. Data will be added as a separate layer.Data fields:PCT_NotWhite_NotHisp - Percent of the population in each tract that is a non-Hispanic minority. PCT_Hispanic - Percent of the population in each tract that is Hispanic or Latino. Pct75up - Percent of the population in each tract that is age 75 or higher. PCT_LEP - Percent of the Limited English Proficiency population in each tract.PCT_People_in_Poverty - Percent of the population in each tract that is living below the Federal poverty level.PCT_NOCAR - Percent of households in each tract that do not have a car.PCT_Disabl - Percent of the population in each tract that is disabled. Reg_NotWhite_NotHisp - Regional average for the population that is a non-Hispanic minority. This is for the same time period as the tract data. Reg_Hispanic - Regional average for the population that is Hispanic or Latino. This is for the same time period as the tract data. Reg_75up - Regional average for the population that is age 75 or higher. This is for the same time period as the tract data. Reg_LEP - Regional average for the Limited English Proficiency population. This is for the same time period as the tract data. Reg_Poverty - Regional average for the population that is living below the Federal poverty level. This is for the same time period as the tract data. Reg_NOCAR - Regional average for percent of households that do not have a car. This is for the same time period as the tract data. Reg_Disabl - Regional average for the population that is disabled. This is for the same time period as the tract data. FLAG_NotWhite_NotHisp - This is used to determine the VPI. It is "1" if the tract number is greater than the regional average. Otherwise it is "0". FLAG_Hispanic - This is used to determine the VPI. It is "1" if the tract number is greater than the regional average. Otherwise it is "0". FLAG_75up - This is used to determine the VPI. It is "1" if the tract number is greater than the regional average. Otherwise it is "0". FLAG_LEP - This is used to determine the VPI. It is "1" if the tract number is greater than the regional average. Otherwise it is "0". FLAG_Poverty - This is used to determine the VPI. It is "1" if the tract number is greater than the regional average. Otherwise it is "0". FLAG_NOCAR - This is used to determine the VPI. It is "1" if the tract number is greater than the regional average. Otherwise it is "0". FLAG_Disabl - This is used to determine the VPI. It is "1" if the tract number is greater than the regional average. Otherwise it is "0". INDEX - The sum of all the FLAG fields.

Understanding how interurban movements can modify the spatial distribution of the population is important for transport planning but is also a fundamental ingredient for epidemic modeling. We illustrate this on vacation trips for all transportation modes in China during the Lunar New Year and compare the results for 2019 with the ones for 2020 where travel bans were applied for mitigating the spread of a novel coronavirus (COVID-19). We first show that inter-urban travel flows are broadly distributed and display both large temporal and spatial fluctuations, making their modeling very difficult. When flows are larger, they appear to be more dispersed over a larger number of origins and destinations, creating de facto hubs that can spread an epidemic at a large scale. These movements quickly induce (in about a week for this case) a very strong population concentration in a small set of cities. We characterize quantitatively the return to the initial distribution by defining a pendular ratio which allows us to show that this dynamics is in general very slow and even stopped for the 2020 Lunar New Year due to travel restrictions. Travel restrictions obviously limit the spread of the diseases between different cities, but have thus the counter-effect of keeping high concentration in a small set of cities, a priori favoring intra-city spread, unless individual contacts are strongly limited. These results shed some light on the statistics of interurban movements and how they modify the national distribution of populations, a crucial ingredient for devising effective control strategies at a national level.

The North America transportation infrastructure construction market size was valued at USD XX million in 2025 and is projected to reach USD XX million by 2033, exhibiting a CAGR of 5.00% during the forecast period. The growth of the market is primarily driven by increasing government investments in infrastructure development, rising demand for efficient transportation networks, and the need for sustainable transportation solutions. Additionally, the growing population and urbanization in the region are fueling the demand for improved transportation infrastructure. Governments are prioritizing the development of smart cities and investing heavily in infrastructure projects to enhance mobility and connectivity. The focus on reducing carbon emissions and promoting green transportation is also expected to drive the adoption of sustainable construction practices in the market. Key trends in the market include the growing adoption of advanced technologies such as Building Information Modeling (BIM) and 3D printing, which enhance project efficiency and reduce construction time. The increasing use of alternative materials and sustainable practices is also shaping the market, as governments and construction companies prioritize environmental consciousness. Partnerships between public and private sectors are becoming more common, with governments seeking private investment and expertise to support infrastructure development. The evolving regulatory landscape, with stricter safety and environmental regulations, is also influencing market dynamics. Major companies operating in the market include L&T Construction, Kraemer North America, Bechtel Corporation, CK Hutchison Holdings Limited, and ACS Actividades de Construccin y Servicios SA. These companies are involved in various projects across the region, providing a wide range of construction services. Recent developments include: August 2021: The Ministry of Transportation and Infrastructure announced a USD 837 million Trans-Canada highway widening project between Alberta and B.C. This project involves the construction of bridges and the widening of two lanes highways to four lanes, creating more than 1,200 direct jobs and 700 indirect jobs., February 2021: The United States and Canada planned to invest in transport infrastructure development to offer pipeline projects in the pre-construction or construction stages in the next five years.. Key drivers for this market are: 4., Rapid Urbanization and Rising Disposable Income4.; Government Initiatives and Expanding Economy. Potential restraints include: 4., Limited Land Availability4.; Economic Uncertainties. Notable trends are: Increasing Infrastructure Activities in the United States.

Disabled Transportation Service Market Outlook

The global disabled transportation service market size was valued at USD 8.5 billion in 2023, and it is projected to reach USD 16.9 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.8% from 2024 to 2032. The growth of this market is primarily driven by the increasing demand for accessible and reliable transportation options for individuals with disabilities.

One of the key growth factors for the disabled transportation service market is the aging population, which is anticipated to increase the demand for mobility services catered to elderly individuals with limited mobility. As the global population ages, the number of individuals with disabilities is projected to rise, creating a substantial market for transportation services tailored to their needs. Advances in medical technology and healthcare have also contributed to increased life expectancy, leading to a larger demographic requiring specialized transportation options. This trend is expected to drive the marketÂ’s growth significantly.

Additionally, legislative measures and government initiatives aimed at improving accessibility and inclusivity are playing a vital role in the market's expansion. Many countries have implemented regulations mandating the availability of accessible transportation services for disabled individuals. For example, the Americans with Disabilities Act (ADA) in the United States requires public transportation systems to offer paratransit services for individuals who cannot use the fixed-route system due to their disabilities. Such mandates are encouraging transportation service providers to invest in accessible vehicles and services, thereby propelling market growth.

Technological advancements are also contributing to the growth of the disabled transportation service market. Innovations in vehicle design, such as the development of wheelchair-accessible vehicles (WAVs) and advanced transportation management systems, are improving the convenience and safety of transportation services for disabled individuals. Ride-sharing companies and traditional taxi services are increasingly integrating technology to offer more efficient and user-friendly booking options, including mobile apps that cater specifically to disabled passengers. These technological enhancements are expected to attract more users and drive market growth.

The emergence of Alternate Transportation Technology is also reshaping the landscape of disabled transportation services. Innovations such as electric vehicles, autonomous driving capabilities, and smart transportation systems are being integrated into the market to enhance accessibility and efficiency. These technologies offer the potential to reduce operational costs, increase service reliability, and provide a more personalized transportation experience for disabled individuals. As these technologies continue to evolve, they are expected to play a crucial role in meeting the growing demand for accessible transportation solutions, ultimately contributing to the market's expansion.

Regionally, North America is expected to dominate the disabled transportation service market due to the high prevalence of advanced healthcare infrastructure, supportive government regulations, and a strong focus on accessibility. Europe is also anticipated to witness significant growth, driven by similar factors and increasing awareness about the rights and needs of disabled individuals. Asia Pacific is emerging as a lucrative market, with countries like Japan and Australia investing heavily in accessible transportation solutions. Latin America and the Middle East & Africa regions are also expected to show growth potential, albeit at a slower pace compared to their counterparts.

Service Type Analysis

In analyzing the service type segment, paratransit services stand out as a vital component of the disabled transportation service market. Paratransit services offer a flexible and responsive transportation option for individuals who are unable to use traditional public transit due to their disabilities. These services typically involve special vehicles equipped to accommodate wheelchairs and other mobility aids, operated on a door-to-door or curb-to-curb basis. The growing emphasis on inclusivity and equal access to public services is driving the demand for paratransit services. Additionally, the implementation of government regulations mandating such services is further boosting t

Title VI of the Civil Rights Act and the Executive Order on Environmental Justice (#12898) do not provide specific guidance to evaluate EJ issues within a region's transportation planning process. Therefore, MPOs must devise their own methods for ensuring that EJ issues are investigated and evaluated in transportation decision-making. In 2001, DVRPC developed an EJ technical assessment to identify direct and disparate impacts of its plans, programs, and planning process on defined population groups in the Delaware Valley region. This assessment, called the Indicators of Potential Disadvantage Methodology, is utilized in a variety of DVRPC plans and programs. DVRPC currently assesses the following population groups, defined by the U.S. Census Bureau:

Youth

Older Adults

Female

Racial Minority

Ethnic Minority

Foreign-Born

Disabled

Limited English Proficiency

Low-Income Census tables used to gather data from the 2017-2021 American Community Survey 5-Year Estimates Using U.S. Census American Community Survey data, the population groups listed above are identified and located at the census tract level. Data is gathered at the regional level, combining populations from each of the nine counties, for either individuals or households, depending on the indicator. From there, the total number of persons in each demographic group is divided by the appropriate universe (either population or households) for the nine-county region, providing a regional average for that population group. Any census tract that meets or exceeds the regional average level, or threshold, is considered an EJ-sensitive tract for that group. Census tables used to gather data from the 2017-2021 American Community Survey 5-Year Estimates. For more information and for methodology, visit DVRPC's website: https://www.dvrpc.org/GetInvolved/TitleVI/ For technical documentation visit DVRPC's GitHub IPD repo: https://github.com/dvrpc/ipd Source of tract boundaries: US Census Bureau. The TIGER/Line Files Note: Tracts with null values should be symbolized as "Insufficient or No Data". Data Dictionary for Attributes: (Source = DVRPC indicates a calculated field) Field Alias Description Source geoid20 GEOID20 Census tract identifier (text) Census statefp20 State FIPS FIPS Code for State Census countyfp20 County FIPS FIPS Code for County Census name20 Tract Number Tract Number Census d_class Disabled Classification Classification of tract's disabled percentage as: well below average, below average, average, above average, or well above average DVRPC d_cntest Disabled Count Estimate Estimated count of disabled population Census d_cntmoe Disabled Count MOE Margin of error for estimated count of disabled population Census d_pctest Disabled Percentage Estimate Estimated percentage of disabled population DVRPC d_pctile Disabled Percentile Tract's regional percentile for percentage disabled DVRPC d_pctmoe Disabled Percentage MOE Margin of error for percentage of disabled population DVRPC d_score Disabled Score Corresponding numeric score for tract's disabled classification: 0, 1, 2, 3, 4 DVRPC em_class Ethnic Minority Classification Classification of tract's Hispanic/Latino percentage as: well below average, below average, average, above average, or well above average DVRPC em_cntest Ethnic Minority Count Estimate Estimated count of Hispanic/Latino population Census em_cntmoe Ethnic Minority Count MOE Margin of error for estimated count of Hispanic/Latino population Census em_pctest Ethnic Minority Percentage Estimate Estimated percentage of Hispanic/Latino population DVRPC em_pctile Ethnic Minority Percentile Tract's regional percentile for percentage Hispanic/Latino DVRPC em_pctmoe Ethnic Minority Percentage MOE Margin of error for percentage of Hispanic/Latino population DVRPC em_score Ethnic Minority Score Corresponding numeric score for tract's Hispanic/Latino classification: 0, 1, 2, 3, 4 DVRPC f_class Female Classification Classification of tract's female percentage as: well below average, below average, average, above average, or well above average DVRPC f_cntest Female Count Estimate Estimated count of female population Census f_cntmoe Female Count MOE Margin of error for estimated count of female population Census f_pctest Female Percentage Estimate Estimated percentage of female population DVRPC f_pctile Female Percentile Tract's regional percentile for percentage female DVRPC f_pctmoe Female Percentage MOE Margin of error for percentage of female population DVRPC f_score Female Score Corresponding numeric score for tract's female classification: 0, 1, 2, 3, 4 DVRPC fb_class Foreign Born Classification Classification of tract's foreign born percentage as: well below average, below average, average, above average, or well above average DVRPC fb_cntest Foreign Born Count Estimate Estimated count of foreign born population Census fb_cntmoe Foreign Born Count MOE Margin of error for estimated count of foreign born population Census fb_pctest Foreign Born Percentage Estimate Estimated percentage of foreign born population DVRPC fb_pctile Foreign Born Percentile Tract's regional percentile for percentage foreign born DVRPC fb_pctmoe Foreign Born Percentage MOE Margin of error for percentage of foreign born population DVRPC fb_score Foreign Born Score Corresponding numeric score for tract's foreign born classification: 0, 1, 2, 3, 4 DVRPC lep_class Limited English Proficiency Count Estimate Estimated count of limited english proficiency population Census lep_cntest Limited English Proficiency Count MOE Margin of error for estimated count of limited english proficiency population Census lep_cntmoe Limited English Proficiency Percentage Estimate Estimated percentage of limited english proficiency population DVRPC lep_pctest Limited English Proficiency Percentage MOE Margin of error for percentage of limited english proficiency population DVRPC lep_pctile Limited English Proficiency Percentile Tract's regional percentile for percentage limited english proficiency DVRPC lep_pctmoe Limited English Proficiency Classification Classification of tract's limited english proficiency percentage as: well below average, below average, average, above average, or well above average DVRPC lep_score Limited English Proficiency Score Corresponding numeric score for tract's limited english proficiency classification: 0, 1, 2, 3, 4 DVRPC li_class Low Income Classification Classification of tract's low income percentage as: well below average, below average, average, above average, or well above average DVRPC li_cntest Low Income Count Estimate Estimated count of low income (below 200% of poverty level) population Census li_cntmoe Low Income Count MOE Margin of error for estimated count of low income population Census li_pctest Low Income Percentage Estimate Estimated percentage of low income (below 200% of poverty level) population DVRPC li_pctile Low Income Percentile Tract's regional percentile for percentage low income DVRPC li_pctmoe Low Income Percentage MOE Margin of error for percentage of low income population DVRPC li_score Low Income Score Corresponding numeric score for tract's low income classification: 0, 1, 2, 3, 4 DVRPC oa_class Older Adult Classification Classification of tract's older adult percentage as: well below average, below average, average, above average, or well above average DVRPC oa_cntest Older Adult Count Estimate Estimated count of older adult population (65 years or older) Census oa_cntmoe Older Adult Count MOE Margin of error for estimated count of older adult population Census oa_pctest Older Adult Percentage Estimate Estimated percentage of older adult population (65 years or older) DVRPC oa_pctile Older Adult Percentile Tract's regional percentile for percentage older adult DVRPC oa_pctmoe Older Adult Percentage MOE Margin of error for percentage of older adult population DVRPC oa_score Older Adult Score Corresponding numeric score for tract's older adult classification: 0, 1, 2, 3, 4 DVRPC rm_class Racial Minority Classification Classification of tract's non-white percentage as: well below average, below average, average, above average, or well above average DVRPC rm_cntest Racial Minority Count Estimate Estimated count of non-white population DVRPC rm_cntmoe Racial Minority Count MOE Margin of error for estimated count of non-white population DVRPC rm_pctest Racial Minority Percentage Estimate Estimated percentage of non-white population DVRPC rm_pctile Racial Minority Percentile Tract's regional percentile for percentage non-white DVRPC rm_pctmoe Racial Minority Percentage MOE Margin of error for percentage of non-white population DVRPC rm_score Racial Minority Score Corresponding numeric score for tract's non-white classification: 0, 1, 2, 3, 4 DVRPC y_class Youth Classification Classification of tract's youth percentage as: well below average, below average, average, above average, or well above average DVRPC y_cntest Youth Count Estimate Estimated count of youth population (under 18 years) Census y_cntmoe Youth Count MOE Margin of error for estimated count of youth population Census y_pctest Youth Percentage Estimate Estimated percentage of youth population (under 18 years) DVRPC y_pctile Youth Percentile Tract's regional percentile for percentage youth DVRPC y_pctmoe Youth Percentage MOE Margin of error for percentage of youth population DVRPC y_score Youth Score Corresponding numeric score for tract's youth classification: 0, 1, 2, 3, 4 DVRPC ipd_score Composite Score Overall score adding the classification scores across all nine variables DVRPC u_tpopest Total Population Estimate Estimated total population of tract (universe [or denominator] for youth, older adult, female, racial minoriry, ethnic minority, & foreign born) Census u_tpopmoe Total Population MOE Margin of error for estimated total population of tract Census u_pop6est Population 6+ Estimated population over five years of age (universe [or

The Mobility on Demand (MoD) market, valued at $3577 million in 2025, is projected to experience robust growth, driven by increasing urbanization, rising disposable incomes, and a growing preference for convenient and efficient transportation solutions. The 6.6% CAGR indicates a significant expansion over the forecast period (2025-2033). Key market segments include commercial and personal applications, further categorized by service type: car rental, e-hailing, station-based mobility, and car-sharing. The dominance of e-hailing services, spearheaded by companies like Uber and Lyft, is undeniable. However, the market is witnessing diversification with the rise of car-sharing and station-based mobility options, catering to specific needs and environmental concerns. Technological advancements, such as improved navigation systems, autonomous driving capabilities, and integrated payment systems, are fueling market growth. Furthermore, the integration of MoD services with public transportation networks is enhancing accessibility and user experience. Geographic expansion, particularly in developing economies with burgeoning populations and limited public transportation infrastructure, presents significant opportunities. Regulatory frameworks and concerns around driver safety and fair pricing remain key challenges impacting market expansion. The competitive landscape is characterized by both established players like Uber, Lyft, and Didi Chuxing, and emerging companies focusing on innovative solutions within specific niches. Strategic partnerships between technology providers and transportation companies are becoming increasingly common, further accelerating innovation and market consolidation. Future growth will likely be shaped by the adoption of sustainable practices, including the incorporation of electric vehicles and the development of efficient route optimization algorithms. Addressing concerns around data privacy and security will also be crucial for sustaining consumer trust and market expansion. The overall trajectory suggests a continued upward trend for the MoD market, with substantial growth potential across various geographical regions and service categories.

Patient Transportation Market Outlook

The global patient transportation market size was valued at USD 18.2 billion in 2023 and is projected to reach USD 29.4 billion by 2032, exhibiting a CAGR of 5.5% during the forecast period. This market is primarily driven by the increasing prevalence of chronic diseases, which necessitate frequent and timely patient transport between healthcare facilities. Additionally, the aging population and advancements in medical technology are contributing significantly to the growth of this market.

One of the foremost growth factors for the patient transportation market is the escalating incidence of chronic illnesses such as cardiovascular diseases, diabetes, and cancer. These conditions often require patients to undergo regular medical check-ups, specialist consultations, and treatments, necessitating reliable and efficient patient transportation services. The rise in chronic diseases is a direct result of lifestyle changes, urbanization, and the increasing geriatric population, all propelling the demand for patient transportation solutions.

The advancements in medical technology have also contributed to the expansion of the patient transportation market. Innovations such as portable medical devices, telemedicine, and enhanced communication systems in ambulances have made it possible to provide a higher level of care during transportation. These technological advancements ensure that patients receive continuous care and monitoring, thus improving patient outcomes and increasing the reliance on professional patient transportation services.

The growing elderly population is another significant driver. As people age, their mobility decreases, and they are more likely to suffer from multiple health issues that require frequent visits to healthcare facilities. This demographic shift has resulted in a heightened demand for specialized transportation services that cater to the specific needs of older adults, ensuring their safety and comfort during transit. Moreover, government initiatives and healthcare programs aimed at providing better care for the elderly are further fueling the market growth.

In addition to the growing elderly population, the need for Disabled Transportation Service is becoming increasingly important. This service is essential for individuals with disabilities who require specialized transportation to access healthcare facilities and other essential services. Disabled Transportation Service ensures that these individuals receive the necessary support and accommodations during transit, enhancing their independence and quality of life. As awareness and advocacy for disability rights continue to grow, there is a rising demand for transportation solutions that cater to the unique needs of disabled individuals. This trend is expected to contribute significantly to the expansion of the patient transportation market.

Regionally, North America holds the largest share of the patient transportation market, owing to its well-established healthcare infrastructure, high expenditure on healthcare, and the availability of advanced medical transportation services. Europe follows closely, with a significant market share driven by similar factors. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period due to its rapidly expanding healthcare sector, increasing investment in healthcare facilities, and rising awareness about patient transportation solutions.

Product Type Analysis

Within the patient transportation market, the product type segment encompasses a variety of essential equipment, including wheelchairs, stretchers, medical beds, ambulance services, and other specialized transport devices. Wheelchairs represent a fundamental component, providing mobility to patients with limited mobility due to injury, illness, or disability. The demand for wheelchairs is expected to grow steadily, driven by the increasing aging population and the rising incidence of conditions that impair mobility. Additionally, technological advancements such as electric and motorized wheelchairs are enhancing user convenience and boosting the market.

Stretchers are another crucial product type within this segment. They are indispensable in emergency situations and patient transfers within hospitals and other healthcare facilities. The market for stretchers is expanding due to the growing number of emergency cases and surgeries that r

A selection of census tracts for two different Equity Focused analyses. The first is a combination of higher than the regional rate of People of Color and Limited English Proficiency (POC_LEP = 1). The second adds Low Income (POC_LEP_LI = 1). Non-Equity focus areas (NON_EQUITY = 1) are areas not included in the POC_LEP or POC_LEP_LI selection.The development of the equity focus areas occurred in conjunction with the 2018 Regional Transportation Plan and were informed through discussions of the transportation equity work group, regional advisory committees (TPAC, MTAC, JPACT, and MPAC), four Regional Leadership Forums, and direction from Metro Council.

Equity focus areas are Census tracts that represent communities where the rate of people of color, people with low income (i.e., incomes equal to or less than 200% of the Federal Poverty Level), or people with limited English proficiency is greater than the regional average and double the density of one or more of these populations.

The data sources for equity focus areas include 2010 Census (people of color) and 2011-2015 American Community Survey 5-year estimates (low income, limited English proficiency).

The web-based taxi-sharing market is experiencing robust growth, driven by increasing urbanization, rising fuel costs, and a growing preference for cost-effective and convenient transportation solutions. The market's expansion is fueled by technological advancements, such as sophisticated ride-sharing apps offering real-time tracking, fare splitting, and integrated payment systems. The integration of these platforms with existing navigation apps like Waze further enhances user experience and adoption. While standalone platforms initially dominated the market, integrated solutions are gaining traction due to their seamless integration with existing transportation ecosystems. The segment catering to businesses is experiencing particularly strong growth, driven by corporate travel needs and employee commuting solutions. However, regulatory hurdles in certain regions and concerns regarding driver safety and insurance remain key challenges. Competition is fierce, with established players like Uber and Lyft, alongside innovative startups, constantly vying for market share. Geographic expansion into emerging markets presents significant opportunities, particularly in regions with rapidly expanding urban populations and limited public transportation infrastructure. Future growth will likely depend on the ability of companies to adapt to evolving consumer preferences, address regulatory concerns, and leverage innovative technologies to improve efficiency and safety. The market is segmented by platform type (standalone vs. integrated), user type (business, individual, schools), and geographic region. A CAGR in the mid-teens is a reasonable estimate given the industry's growth trajectory. Assuming a 2025 market size of $15 billion, based on the numerous players and industry trends, this translates to a projected market value exceeding $25 billion by 2033. North America and Europe currently hold the largest market share, however, significant potential exists in rapidly developing Asian and African markets, where increased smartphone penetration and rising middle-class incomes are fueling demand. The competitive landscape is characterized by a mix of large multinational corporations and smaller, agile startups, leading to continuous innovation and improvement of services. Strategic partnerships and acquisitions are expected to play a crucial role in shaping the market’s future trajectory.

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Articulated Bus Market Size 2024-2028

The articulated bus market size is forecast to increase by USD 1.88 billion at a CAGR of 5.68% between 2023 and 2028.

The market is witnessing significant growth due to several key factors. One of the primary reasons is the benefits that articulated buses offer over double-decker buses, including better maneuverability in tight city streets and increased passenger capacity. Another trend driving market growth is the increasing adoption of electric articulated buses, which contribute to reducing carbon emissions and improving air quality in urban areas. Design features include fuel-efficient propulsion systems, passenger-centric amenities, and emission regulations aimed at reducing greenhouse gas emissions and improving air quality. However, the inadequate bus infrastructure in many cities poses a challenge to the market's growth. Subsidies and incentives from governments and transit authorities help mitigate the financial risk for bus manufacturers, driving the market growth. Despite this, the market is expected to continue expanding as governments and transportation authorities invest in modernizing their public transportation systems. Overall, the market is an essential segment In the global public transportation industry, offering innovative solutions to address the growing demand for efficient and eco-friendly mass transit systems.

What will be the Size of the Articulated Bus Market During the Forecast Period?

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The market caters to the unique transportation needs of urban populations in dense areas, offering increased passenger capacity compared to conventional buses. These buses, also known as bendy buses or accordion buses, feature an articulated joint that allows them to maneuver effectively in crowded urban areas with limited road space. With a focus on low-floor designs and spacious interiors, articulated buses provide a more comfortable and accessible experience for passengers. Transit agencies in burgeoning cities increasingly turn to these buses to expand public transport options, addressing the challenges of rapid population growth and increasing road traffic. As the demand for eco-friendly and fuel-efficient bus transport solutions continues to rise, articulated buses, available in both single and double-decker configurations, offer a flexible and sustainable alternative to conventional buses.

How is this Articulated Bus Industry segmented and which is the largest segment?

The articulated bus industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Fuel Type

  Diesel
  Electric and hybrid
  Others


Type

  Single-decker bus
  Double-decker bus


Geography

  Europe

    Germany
    France


  APAC

    China


  North America



  South America



  Middle East and Africa

By Fuel Type Insights

The diesel segment is estimated to witness significant growth during the forecast period.

Articulated buses, available in both single-decker and double-decker configurations, cater to the transportation needs of dense urban areas and intracity routes. These buses, featuring low-floor designs and spacious interiors, accommodate larger passenger capacities compared to conventional buses. Transit agencies prioritize sustainable mobility initiatives, leading to the growing popularity of zero-emission electric buses. However, diesel buses continue to dominate the market due to their high range and versatility, suitable for long-haul intercity routes and areas with limited charging infrastructure.

Moreover, urban populations rely on buses for efficient and flexible transportation, especially in crowded city streets with tight turns. The vehicle market consists of various types, including single-decker buses, double-decker buses, airport shuttle services, tour & charter services, and ICE (Internal Combustion Engine) vehicles. Flexible construction and automation technologies, such as lane assist and collision avoidance, enhance safety and passenger comfort. Urban transportation and public transport options continue to evolve, with government laws and environmental concerns shaping the market landscape.

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The diesel segment was valued at USD 2.95 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

Europe is estimated to contribute 44% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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Articulated bu

The Atlanta Regional Commission (ARC) has created an interactive equity analysis tool to help address these questions as featured on 33n, the ARC Research & Analytics Blog. In the past, the ARC used a static map of their Equitable Target Areas (ETA) for project evaluation. The ETA index was a tool that helped ARC better identify areas with minority or low-income populations to understand how proposed projects might impact these groups.Now, Atlanta Regional Commission has created a transportation data platform called DASH where a more nuanced equity analysis can be visualized. This equity analysis helps ARC understand where people reside across all nine of the federally protected classes. Coming soon, there will be transportation data added to DASH. Combined with the data already included, the equity analysis provided by DASH will help guide regional transportation and land use planning and will be used as input for project prioritization and evaluation, monitoring resource allocation, and assisting in decision-making.ARC's Equity Analysis is widely used throughout the agency to demonstrate compliance with Federal Guidance, including Title VI of the Civil Rights Act of 1964, Limited English Proficiency Executive Order, Americans with Disabilities Act of 1990, Environmental Justice Executive Order, and FHWA and FTA's Title VI and Environmental Justice documents.MethodologyThe Equity Analysis methodology generates a composite score based on the concentrations of the criteria selected, which is used to meet the nondiscrimination requirements and recommendations of Title VI and EJ for ARC's plans, programs, and decision-making processes.The score calculation is determined by standard deviations relative to a criteria's regional average. This score classifies the concentration of the populations of interest under Title VI and EJ present in every census tract in the region. These population groups are represented by the nine equity analysis criteria: youth, older adults, females, racial minorities, ethnic minorities, foreign-born, limited English proficiency, people with disabilities, and low-income.The data for each of the criteria in the equity analysis are split into five "bins" based on the relative concentration across the region: well below average (score of 0); below average (score of 1); average (score of 2); above average (score of 3); and well above average (score of 4). See Figure 1 below. A summary score of all nine indicators for each census tract (ranging from 0-36) is used to show regional concentrations of populations of interest under Title VI and EJ. A summary score of racial minority, ethnic minority, and low-income for each census tract is used in ARC's Project Evaluation Framework to prioritize projects in the Transportation Improvement Program (TIP). This view is the map default.Bin 2 for each indicator contains census tracts at or near (within a half standard deviation from) the regional average (mean) for that indicator. Bins 4, 3, 1, and 0 are then built out from the regio nal average; Bins 1 and 3 go another full standard deviation out from bin 2, and bins 0 and 4 contain any remaining tracts further out from 1 or 3, respectively.This Equity Analysis supplants previous equity analysis iterations, including ARC's Equitable Targ et Areas (ETAs).The design of this methodology is supported by both FHWA's and FTA's Title VI recommendations to simply identify the protected classes using demographic data from the US Census Bureau as the first step in conducting equity analyses. Additionally, FTA's EJ guidance cautions recipients of federal funds to not be too reliant on population thresholds to determ ine the impact of a program, plan, or policy to a population group, but rather design a meaning ful measure to identify the presence of all protected and considered population groups and then calculate the possibility of discrimination or disproportionately high and adverse effect on these populations.ARC plans to continue the conversation with its staff, partners, and Transportation Equity Advisory Group (TEAG) about measuring and evaluating transportation benefits and burdens, as well as layering the Equity Analysis with supplemental analyses such as access to essential services, affordability, and displacement.Data DisclaimerThis webpage is a public resource using ACS data. The Atlanta Regional Commission (ARC) makes no warranty, representation, or guarantee as to the content, sequence, accuracy, timeliness, or completeness of any of the spatial data or database information provided herein. ARC and partner state, regional, local, and other agencies shall assume no liability for errors, omissions, or inaccuracies in the information provided regardless of how caused, or any decision made or action taken or not taken by any person relying on any information or data furnished within.ARC is committed to enforcing the provisions of Title VI of the Civil Rights Act of 1964 and taking positive and realistic affirmative steps to ensure the protection of rights and opportunities for all persons affected by its programs, services, and activities.CSV DownloadGIS Data Available SoonDate: 2018Equity Analysis Contact Info:Aileen DaneySenior PlannerTransportation Access & Mobility Group470.378.1579adaney@atlantaregional.orgTitle VI Policy and Complaint Contact Info:Brittany ZwaldTitle VI Officer/Grants and Contracts AnalystFinance Group470.378.1494bzwald@atlantaregional.orgFor more information on ARC's Title VI program or to obtain a Title VI Policy and Complaint Form please visit:https://atlantaregional.org/leadership-and-engagement/guidelines-compliance/title-vi-plan-and-program/

The dataset contains the number of people in North Rhine-Westphalia who can be reached within an hour by public transport. It includes a complete day for each inhabited 100 m grid cell of the Rheinische Revier.

The underlying methodology is a ‘cumulative opportunity’ accessibility calculated with the best possible connection that can be started in the given hour. This means that for each populated 100 m grid cell, all connections to the other grid cells are calculated for each minute in a maximum of 60 minutes. For each hour, all persons are then summed up in the reachable cells.

Nomenclature of field naming: OV_01: Accessible population by public transport with start of travel between 01:00 and 01:59. The addition "_01" represents the time disk.

The calculation was carried out with R5R. The following settings were selected • access to public transport is on foot • the reasonable distance to the stop has been limited to 1000 m • a maximum of 3 transfer processes are possible • Start and finish points are the midpoints of the 100m grid cells. • The accessibility was calculated for 18.01.2023 (restrictions due to temporary failures or blockages cannot be excluded).

The following data sources were used to create the dataset: • Association for the Promotion of End-to-End Electronic Passenger Information (DELFI) (2023): Germany-wide target timetable data (GTFS) • Federal Office of Cartography and Geodesy (BKG): Geographical grids for Germany in Lambert projection (GeoGitter Inspire) © GeoBasis-DE / BKG (2023) • OpenStreetMap © OpenStreetMap contributors (2022) • Federal and State Statistical Offices (2015): Population per hectare

R5R: Pereira, R. H. M., M. Saraiva, D. Herszenhut, C. K. V. Braga, and M. W. Conway. R5r: Rapid Realistic Routing on Multimodal Transport Networks with R 5 in R (https://github.com/ipeaGIT/r5r)

The Asia-Pacific rickshaw ride-hailing service market is experiencing robust growth, driven by increasing urbanization, rising disposable incomes, and the burgeoning adoption of smartphone technology. The market's convenience, affordability compared to traditional taxis, and eco-friendliness in the case of electric rickshaws are key factors fueling its expansion. A Compound Annual Growth Rate (CAGR) of 21.50% from 2019 to 2024 indicates a significant upward trajectory. While precise market size figures for 2025 are unavailable, projecting from the historical data and considering the sustained growth rate, a reasonable estimate places the market value at approximately $2.5 billion for 2025. This figure is supported by the growing number of players—including both established ride-hailing giants like Grab and Gojek, and regional startups like Mauto Electric Mobility—actively competing for market share. The market is segmented based on application (freight and passenger commuting), booking type (online and offline), payment methods (cashless and e-wallets), and propulsion type (electric and internal combustion engine). The preference for cashless transactions and the increasing availability of electric rickshaws contribute to the market's dynamism. Significant growth potential exists in less penetrated markets within the region, particularly in countries with high population density and limited public transportation options. However, challenges remain, including regulatory hurdles related to licensing and safety standards, and the need for improved infrastructure to support the expansion of electric vehicle charging networks. The dominance of major players like Grab and Gojek highlights the competitive landscape. However, the market also offers significant opportunities for smaller, localized firms specializing in specific niches, such as freight transportation or electric rickshaw services. Further growth will be determined by the success of these companies in adapting to evolving consumer demands, leveraging technological advancements, and navigating regulatory landscapes. The increasing adoption of innovative technologies such as GPS tracking, real-time fare calculation, and integrated payment systems is expected to enhance the user experience and drive market growth. The future success of the market hinges on a synergistic relationship between technological innovation, supportive government policies, and the continued expansion of e-commerce and logistics activities within the region. This market segment displays a strong growth forecast extending into 2033. This comprehensive report provides an in-depth analysis of the rapidly evolving Asia-Pacific rickshaw ride hailing service market. Covering the historical period (2019-2024), base year (2025), and forecast period (2025-2033), this study offers invaluable insights for stakeholders seeking to understand this dynamic sector. The market is segmented by application (freight and logistics, passenger commuting), booking type (online, offline), payment method (cashless, e-money/e-wallet), and propulsion type (electric, internal combustion engine). Key players like Uber, Ola Cabs, Gojek, and numerous regional players are analyzed, revealing market concentration, competitive dynamics, and future growth trajectories. This report uses data valued in the millions. Recent developments include: In 2021, Uber India announced increasing its electric vehicle fleet to 3,000 e-vehicles due to trending e-mobility and green technology trends in the country. The company also has plans to establish charging infrastructures and partnered with OEM to smoothen its operations.. Key drivers for this market are: Increasing Inclusion of E-bikes in the Sharing Fleet. Potential restraints include: Limited Infrastructure May Hinder Market Growth. Notable trends are: Rising Tourism, Leisure Traveling and Logistics Sector.

The spike in gasoline and diesel fuel prices during the spring of 2022 provided a unique opportunity to evaluate how changes in transportation costs affect travel behavior, including changes in small and rural communities. We created an internet-based survey that asked people living in Vermont how they responded to the recent increase in gasoline and diesel fuel prices and what they plan to do if prices remain high. We included questions about changes in the vehicles they use and plans to purchase more fuel-efficient or electric vehicles. We also asked about changes made to travel for essential trips, including trips for work, education, medical appointments, and food, and less essential trips including visiting friends and family, recreational activities, and going to social events. For each trip type, essential and less-essential, respondents could make one or more selections from a list of possible actions that could have been taken in response to higher fuel costs. Actions included ..., We began by recruiting participants using a geolocated database of about 40,000 Vermont e-mail addresses (geolocated to the town level) obtained from a marketing company. The sample collected from this recruitment method skewed much older than the Vermont population but was otherwise broadly representative. We therefore recruited additional participants through Facebook and Instagram advertisements. The sample collected from the social media advertisements was, on average, 29 years younger. All participants were given a chance to enter a drawing for one of ten cash cards each worth $50, as an incentive. The survey was distributed in March 2022 and received 911 responses. After filtering out surveys that were less than 50% complete (these were mostly surveys that were started but never completed), the final size of the sample used in our analysis was 749. Missing values in the final sample were imputed in R using the MICE package. Numerical variables we imputed using Predictive Mean Matc..., , # Data for: Evaluating heterogeneity in household travel response to carbon pricing: a study focusing on small and rural communities

Dataset DOI: https://doi.org/10.5061/dryad.3r2280gnp

Response to Fuel Price Survey Metadata

University of Vermont Transportation Research Center

Metadata Producer(s): Narges Ahmadnia and Gregory Rowangould, University of Vermont Date: February 2025

Table of Contents

1. Overview
2. Producers and Sponsers
3. Data Collection
4. Variables List
5. Variables Description

Overview

We take advantage of the dramatic spike in gasoline and diesel fuel prices during the first half of 2022 to gain insights into the responses of residents living in diverse small cities, towns, and rural communities in Vermont. Limited existing data on how rural populations react to changes in transportation fuel prices made this...