Sustainability planning, assessment, and monitoring relies on data from a variety of sources to provide a complete picture of the environmental, economic, and social impacts as well as the basic functionality of transportation projects or agencies themselves. In the United States and internationally, data quality and availability have proven to be significant barriers to comprehensive transportation sustainability assessment (31,58,59). A research team in the UK developed an indicator-led approach to appraising sustainability in transportation. The appraisal method examines relationships among transportation, economy, environment, and society to cover all of the objectives in the UK sustainable development strategy and the European Commission's accepted definition of sustainable transportation. The goal was to operationalize already accepted definitions by selecting appropriate indicators. The research team intended to select indicators that were already in use or that relied on an existing data source in order to help with tracking progress from an established baseline. However, they found that several proposed indicators lacked data sources; in particular, the key social sustainability indicators like out-of-pocket transportation costs, quality and security of local environment for walking and cycling, equity of access to transport network from affordable housing (58).
For certain purposes, like developing performance measures or indicators for project delivery or system conditions, transportation agencies may face a data overload -- there may be too much data to choose from and the agency may struggle with what is the most important or most meaningful data to track. However, when it comes to sustainability assessment (particularly economic and social equity/quality of life impacts) agencies face a lack of sufficient data or may have questions about which measures are meaningful indicators of progress. In responding to survey questions about performance measures, DOTs demonstrated that they collect numerous environmental indicators (some of which are required by law) but fewer economic and social indicators. One DOT in particular expressed difficulty in finding a direct measure for social sustainability. Arizona, Delaware and California DOTs have been working to find appropriate performance measures. They reported difficulty in narrowing the list of measures to a manageable number and selecting the most meaningful ones. Further, data collection is costly and requires significant use of resources, and transportation agencies may find it difficult to prioritize additional data collection over other more urgent needs.
For sustainability, developing indicators may require creative use of available data. For example, several variables can be combined into a quality of life index. When considered alone, the data may indicate a certain state but when considered jointly the data may suggest trade-offs that occur. The UK's and other experiences (29, 59, 60) suggest that disaggregating traditional transportation statistics by transportation mode, geography or socioeconomic group could be a method for analyzing social equity. Such an analysis would require census data (demographics, geopolitical boundaries) and available transportation statistics.
Organizing indicators into a sustainability framework - like New Zealand, Texas or Missouri - can help narrow down a list of indicators or identify additional data that needs to be collected. Indicators ought to be linked to specific agency goals and the overall agency vision. By linking indicators to specific agency goals, objectives, and targets, it becomes necessary to sort through all of the available data and only choose indicators that provide a meaningful measure. Rather than collecting and tracking every possible piece of data, only the most important are selected (which can be financially beneficial given limited resources). With a streamlined list of indicators, the transportation agency can then track performance and more easily pinpoint why targets are not met. The agency can also assess trade-offs among different indicators that may result from policies. This must be done with the understanding that performance measures need not be static but must accurately reflect what the agency is trying to accomplish, as what gets measured gets managed. By considering the key indicators for assessing sustainability, gaps in data availability can be identified to guide future data collection or interagency data sharing.
Considerable benefits can be achieved by improving the coordination of existing transportation statistics gathering activities. Data cost effectiveness could be improved significantly. Data quality and usefulness could also be improved by focusing on: comprehensiveness, consistency, frequency, accuracy, transparency, and availability (61). In the meantime, transportation agencies can strive to identify meaningful measures based on available data sources. Following New Zealand and the UK's lead, agencies can also identify measures that they would like to have and potential sources for the data.
A significant challenge for sustainability analysis, evident in DOT interviews and the literature, is finding the right data at the appropriate geographic scale, level of aggregation, or timeframe. In the United States there are several publicly available and commonly used data sources for transportation and socioeconomic data. Table 4-1 summarizes some of these datasets. Environmental data sources, that are used more widely because of NEPA, are not included here.
|Data Source||Frequency||Description||Geographic Scale|
|APTA Transit Statistics||Annual||Annual statistics for US public transportation; Annual agency-specific statistics; historical time series statistics for US||United States; Local providers|
|BTS State Transportation Statistics||Annual*||Compilation of transportation statistics from multiple sources (timeframes vary)||State|
|DOE's Transportation Energy Data Book||Annual (from 1976)||Transportation statistics on fuel consumption, emissions, etc. (http://cta.ornl.gov/data/index.shtml)||National|
|Environmental Protection Agency's Envirofacts||Periodic, variable||Clearinghouse of EPA data sources||Zip code, city, county, state|
|FHWA National Bridge Inventory (NBI)||Annual||Condition of bridge infrastructure||State (individual bridges)|
|FHWA National Household Travel Survey||Variable (1969-2001)||Daily travel by all modes and traveler characteristics||National|
|FHWA's Highway Statistics||Annual||Information on US road conditions, highway travel, and expenditures||State and City|
|Highway Performance Monitoring System||Periodic, variable||National level highway information system||States|
|National Transit Database||Periodic, variable||National database of statistics for the Transit Industry||Nation's Transit Agencies|
|Texas Transportation Institute||Periodic, variable||Urban Mobility Report||U.S. Metropolitan Areas|
|US Census American Community Survey (ACS)||Annual (from 2005)||Similar data to US Census long form||Areas with population >65,000|
|US Census Bureau's Annual Economic Surveys||Annual||Local economic patterns by industry||County and Zip Code|
|US Census Bureau's Decennial Census||10 years||Demographic data||National, state|
|US Census Bureau's Economic Census||5 years||Profile of businesses and industry||By industry (or establishment)|
|US Census Bureau's Population Estimates||Annual||Population estimates for year's between decennial censuses by demographic group||Nation, state, counties|
|US Census Transportation Planning Package||10 years||Travel data (will be based on ACS)||Local, county, state|
For purposes of long-range planning, these data sources are produced at an appropriate time interval. However, for sustainability performance monitoring and lower-level planning activities, this data may not be sufficient. For example, there is no single integrated multi-modal database at the federal level. Many MPOs or state DOTs supplement public data sources with regional travel surveys, local land use information (primarily from comprehensive planning or zoning regulations), and data collected in-house (traffic counts, safety statistics, etc.). Additional data may also be available from other relevant state agencies, like economic development and public health departments or environmental agencies.
Data for evaluating system performance is collected regularly by transportation agencies and includes pavement condition, travel times, crash rates, etc. Measuring system performance is a necessary piece of sustainable transportation assessment, but not sufficient on its own. If a transportation system improves its operations at the expense of the environment, economy, and/or society, it may not be sustainable. When taken together, these four areas represent the sustainability of the transportation system and/or agency.
Various DOTs have been collecting data on the environment for several years as a result of the 1969 NEPA and several subsequent laws regulating air quality, water quality, noise, historic preservation, etc. Today agencies are able to evaluate the impacts of projects on the environment, both natural and built, by tracking noise pollution, construction run-off, wetland replacement, material recycling, and other data collected in-house. Agencies also gather data on operational impacts, both internal (like DOT fleet fuel consumption, energy use, paper recycling) and external (like air quality and highway plantings). Now, with emphasis on climate change and energy independence, transportation agencies are beginning to measure greenhouse gas emissions and fuel consumption. While a considerable amount of environmental data is collected by the transportation agency, additional data is obtained from federal and state resource agencies or local governments.
When it comes to sustainability indicators for the economy or social equity/quality of life, agencies tend to lack sufficient data sources. Aside from estimates of project costs and benefits, a majority of economic data is obtained from outside agencies like the US Census Bureau, state or local economic development agencies, and private data collection companies. Developing social indicators can require creative use of available data. For example, several variables can be combined into a quality of life index; when considered alone the data may indicate one state but when considered jointly the data may suggest trade-offs that occur. The UK experience (29) suggests that disaggregating traditional transportation statistics by geography or socioeconomic group could be an effective method for analyzing social equity. Experience in the United States in applying Health Impact Assessment (HIA) to transportation projects also supports disaggregation of available data as a means for assessing social impacts (60). Such an analysis requires census data (demographics, geopolitical boundaries) and available transportation and health-related statistics. GIS could also aid in such an analysis.
Data sources for GIS are becoming increasingly necessary for early environmental screenings and for assessment of economic, social, and land use impacts. GIS analysis is recommended as an effective way to examine local impacts during a strategic sustainability assessment and is also a valuable visualization and analytical tool for spatial analysis and scenario planning. In interviews with state DOTs, several mentioned using GIS to help identify environmental impacts (Delaware, Tennessee, Florida) and others expressed a desire to do so but cited the availability of data files as a primary barrier. In order to fully utilize GIS analysis, statewide data sources for transportation infrastructure, land use, and environmental features would be necessary. There are numerous GIS data clearinghouses available online, but not all data sources are free, and some may not provide the detail necessary to conduct corridor or project analyses. Additionally, there may be data gaps in states or counties that do not generate their own GIS files. For specific transportation projects, GIS data can be generated by attaching spatial data to existing data sources like transit station locations or employment centers (essentially mapping either manually or with GPS equipment). Creating GIS datasets is often costly and labor-intensive.
The GeoCommunity Data Catalog and Geospatial One Stop (geodata.gov) are two examples of clearinghouses that offer statewide and county-level data. There are also specific GIS data sources with particular relevance to transportation planning. Examples include:
Additional GIS resources are provided on the FHWA website.
The data sources presented above are the most commonly used sets for transportation planning because of the frequency of collection, appropriateness of scale, and convenient format. Additional data sources are available but may not be as useful for transportation planning purposes due to limited scale, less frequent collection, and cost of use (see Table 4-2).
|Data Source||Frequency||Description||Geographic Scale|
|Housing and Transportation Affordability Index||Varies||Maps housing and transportation costs as percent of income, annual household gasoline expenditures, carbon dioxide emissions from household auto use, and custom comparisons (user selected variables) http://htaindex.cnt.org/||Select US metropolitan regions|
|Metropolitan Travel Survey Archives||Varies||Database of travel surveys conducted by US states or metropolitan areas http://www.surveyarchive.org/archive.html||State and Metro|
|National Highway Traffic Safety Administration||Varies||Safety data and statistics for US states and the nation as a whole http://www.nhtsa-tsis.net/||State, National|
|North American Transport Statistics Online Database||Annual (1996-2005)||Transportation-related data in twelve thematic categories for the US, Canada, and Mexico http://nats.sct.gob.mx/sys/index.jsp?i=3||Nation|
|TranStats: Intermodal Transportation Database||Varies||Searchable index of US transportation datasets http://www.transtats.bts.gov/||Varies|
|US Bureau of Labor Statistics' Consumer Expenditure Survey||Annual (1984-2007)||Statistics on consumer patterns http://www.bls.gov/cex/||Nation|
|Cross-National Time-Series Data Archive||Annual (since 1815 for some countries)||Variety of demographic, economic, transportation, education, and other data collected for over 200 countries (must pay for a license) http://www.databanksinternational.com/||Nation|
|EarthTrends Searchable Database||Varies||Database of over 600 variables relating to transportation, environmental systems, and energy use http://earthtrends.wri.org/miscell/sitemap.php?theme=0||City, Region, Nation|
|iRAP International Transport Statistics Database||Varies||Various statistics for several countries, including the US http://www.iraptranstats.net/||International (Nation)|
|IRF's World Road Statistics||Annual (since 1964)||Collection of national transport statistics for over 185 countries http://www.irfnet.org/statistics.php||Nation|
|Millennium Cities and Mobility in Cities databases||2001||Transportation data on over 100 world cities (pay for service) http://www.uitp.org/publications2/store/index2.cfm?id=5&#mcd||International (City)|
|National Footprint Accounts||Varies||Contains data sources for each ecological footprint including Food and Agriculture Organization of the UN, the International Energy Agency, the UN Statistics Division, and the Intergovernmental Panel on Climate Change http://www.footprintnetwork.org/en/index.php/GFN/page/methodology/||International (Nation)|
|United Nations (UN) Global Urban Observatory Database||Varies||Transportation, land use and other data for world cities http://ww2.unhabitat.org/programmes/guo/||International (City, Nation)|
This section demonstrates applications of different data sources in various sustainable transportation planning practices and assessment methods identified in the survey and literature. Table 4-3 summarizes various practices, data requirements, sources, and limitations. A similar analysis is provided as part of each case study in Chapter 5. In addition to the publicly available data sources in Tables 4-1 and 4-2, some of the practices require data from private sources, which may not be free or easy to obtain. Also, GIS-based practices depend upon the availability of a comprehensive database, and so may not be easily applied by all state DOTs. Development of GIS databases for the purpose of sustainability assessment requires cooperation among multiple agencies and can be labor-intensive and costly.
|Tool||Description||Data Need||Data Type&Source||Strengths, Limitations, or Desired Data|
|Envision Utah (63)||Regional transportation-land use planning effort in northern Utah||Data gathered and converted into GIS format from:||Requires time and resources to prepare data files|
|Houston-Galveston Area Council's (H-GAC) Regional Decision Support System (RDSS) (78)|
Funded under FHWA's Eco-Logical grant program, RDSS is an interactive, GIS-based mapping tool used to integrate long-range transportation and environmental planning. First consensus-driven, regional-scale tool for identifying priorities for future conservation. Incorporated into H-GAC's 2040 Regional Transportation Plan.
RDSS can be used for mapping on Internet Explorer with Adobe Flex viewer or ArcGIS users can stream the data into their own GIS projects.
Eco-types: ecosystems specific to the H-GAC region, including bottomland and upland forests, tidal wetlands, coastal prairies, and water bodies.
Water quality data
2035 road network
Cumulative Metric Rankings
Other local and H-GAC data relevant to environment, transportation system, and growth
All data except ecotypes available in GIS format from H-GAC (landcover and road network), EPA (water quality, species), USGS (watershed)
Ecotypes were mapped using GIS - approximately 12,000 units mapped in 4 months
Cumulative Metric Ranking incorporates quantitative measures (like threatened and endangered species) and qualitative measures like ecotype quality (from observations using aerial photography and soil and geologic maps). Species indentified using EPA's Geographic Information System Screening Tool (GISST). Metrics and methodology for ecotype quality is described in the project report.
Scale of project was regional (8 counties), so limited mapping units to 100-acre minimum and thus could not map freshwater wetlands individually. Could not conduct on-the-ground verifications. Therefore, data not appropriate for site-specific evaluations.
Data is publicly available on Internet, so sensitive information such as threatened and endangered species could not be accessed.
|Florida DOT's Efficient Transportation Decision-making (ETDM)(64)||Process to anticipate environmental problems early on through public involvement and GIS assessment during planning, programming, and project development|
Transportation Project Information
Extensive data sets are compiled for each section in accordance with agency agreements and ETDM policies
Florida Geographic Data Library of University of Florida's GeoPlan Center (combines federal, state, local data from resource agencies, MPOs, FDOT, etc.)
Incorporate public feedback
Build database by transforming existing data into GIS format, using on-line data entry, or field data collection
|GIS data may be incomplete or inadequate, requiring manual review of a project alignment; Requires coordination with multiple agencies at least annually to update; adhere to QA/QC measures|
|Idaho Transportation Department's Greenhouse Gas Emissions Reduction||Calculating and tracking Idaho's transportation-related GHG emissions||GHG emissions from buildings, vehicles and equipment, and employee commuting||EPA emission factors for buildings (electricity and heating), vehicle fleet and equipment, and employee commutes (data based on survey of employees)||Establishing a baseline for future analysis|
|Minnesota DOT Performance Based Planning&Programming (65)||Uses clear policy priorities, performance trend data, and performance forecasting to guide investment decisions||Various performance measures related to transportation network performance and agency performance||Regularly collected DOT data (including crash statistics, freeway congestion, snow clearance, bridge condition); Transit bus hours (from cities, counties, or regional authorities)||Measures are more linked to transportation system performance and will need to be expanded to evaluate progress toward sustainability|
|Missouri DOT Tracker (66)||Quarterly report of measures for 18 outcome areas|
% of projects without environmental violation
% of projects protecting sensitive species
Ratio wetlands created/impacted
% clean air days
Gallons of fuel consumed by unit
Historic resources avoided/ -protected vs. mitigated
Tons recycled materials used in construction
DOT data, Clean Water Act permits
EPA ozone readings
Statewide financial system
Collected by DOT during planning phase
MoDOT construction management database
|Tracker utilizes existing and readily available data sources; additional sustainability focused measures would likely require new data sources|
|Montana DOT Highway Economic Assessment Tool (HEAT) (67)||Enhanced benefit-cost analysis tool for projects that accounts for system impacts|
Transportation system performance
Sociodemographics (block level data, including population, households, travel patterns)
Employment data at establishment-level and census tract and county level data
Economic data including project cost estimates, value of time for freight movements (by commodity), travel times, economic attractiveness
GIS data repository compiled from public and private sources - U.S. Census, the State of Montana, Highway Performance Monitoring System, Bureau of Transportation Statistics (BTS), private data collection companies such as Reebie, Woods&Poole, Info USA
U.S. Department of Agriculture National Agricultural Statistical Service, Commodity Flow Survey (CFS), IMPLAN, 1997 Economic Census Data on Wholesale Trade, FHWA's Freight Analysis Framework (FAF)
|The tool is data intensive and combines multiple data sources; labor intensive to geocode data into GIS format; private data sources may not be free for use|
|MultiCriteria Evaluation of Planning Alternatives for Sustainability (62)||Used in comparing metropolitan land use and transportation alternatives based on system performance, environmental, economic and social capital measures and tradeoffs.|
20-year land use/transportation scenarios
System Performance Measures: VMT per capita; avg. distance driven per day per person
Environmental Indicators: VOC, NOx emissions
Economic Indicators: vehicle hours traveled per capita, avg. duration of driving per day per person
Social: Equity of exposure to emissions, population exposure to emissions
Atlanta Regional Commission:
GIS files&Excel data for land use
Four-step transportation demand modeling inputs and outputs for the Baseline 2005 conditions and Mobility 2030 plan
|Evaluates relative rather than absolute sustainability value of different planning alternatives.|
|Public Transit Energy&Carbon Footprints (68)||Estimation of the Energy and Carbon Footprints for Public Transit Systems in the 100 Largest U.S. Metropolitan Areas|
Transit fuel consumption
BTUs and Carbon Emissions
FTA's National Transit Database (data reported by local agencies within metro areas)
US EIA published values (for liquid fuels) and estimated state carbon dioxide emissions from electricity generation (State Electricity Profiles)
|Only as accurate as data collected by local agencies; aggregation error; missing data from local agencies|
|Sustainability Footprint(42)||Used in analyzing the impacts of transportation infrastructure systems on regional sustainable development, in particular quality of life contributions|
System Sustainability (Quality of Life) - congested travel (% peak vehicle miles traveled or VMT)
Waste generation - annual delay per person
Resource usage - annual excess fuel consumption
|Texas Transportation Institute's Urban Mobility Report, comparing 1990 and 2000 data||Simplified footprint model - more robust measures would require additional data sources (like for safety, accessibility and other social benefits)|
|Transportation Energy&Carbon Footprints (69)||Method to measure and compare (and potentially track) emissions for metropolitan areas|
Fuel consumption for cars and trucks
Energy Use&Carbon Emissions
Urban Form Measures
HPMS&Highway Statistics (FHWA)
Oak Ridge Laboratory's Transportation Energy Databook, FHWA Highway Statistics, U.S. Census Bureau's 2002 Vehicle Inventory and Use Survey
US Energy Information Administration's (EIA) Published values
2000 Census, 2000 and 2005 County Business Patterns, 2000 and 2005 Zip Business Patterns, 2001 National Land Cover Database, FTA's 2005 National Transit Database
|TxDOT Sustainability Indicators (30)||Recently completed research project to develop sustainability indicators for the strategic plan|
Performance Indicators like:
Travel Time Index
Annual severe crashes per mile
Land use balance
Truck throughput efficiency
Capacity addition within Right-of-Way
Daily carbon dioxide emissions
Calculated with DOT data
Estimation procedure based on Interim Roadway Safety Design Workbook
GIS land use files
% trucks from TxDOT's Road-Highway Inventory and Network
GIS analysis or physical inspection
Estimated by Mobile 6 Emissions Model
|Indicators are more focused on transportation system performance rather than sustainability; based on mobility rather than accessibility and do not address social impacts; utilizes indirect measures that are derived from other variables|
|WSDOT Sustainability Plan&Progress Report (70)||Annual plan update and progress report on sustainability targets and emerging issues|
Agency performance in areas of:
Fleets and transportation
Purchase of goods and services
Facility construction, operation, and maintenance
Persistent Bioaccumulative Toxins (eg. from herbicides)
Data from multiple DOT departments and other state or federal agencies including:
WSDOT Transportation Equipment Fund (for fuel consumption)
Washington State Ferries Safety Systems Office
WSDOT Systems Analysis and Program Development
WSDOT Purchasing and Inventory Total
WSDOT Regional Offices
Energy Information Administration
WSDOT Environmental Services
|Primarily monitors internal agency sustainability|