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114 A Guidebook for Using American Community Survey Data for Transportation Planning ods can be used to model the trend and help in forecasting future values. Regression analysis (e.g., ordinary least squares [OLS] regression) is one such tool that also allows for the inclusion of other variables that could affect the indicator of interest. When using a regression to model a trend, it is preferable to use the actual annual rates rather than the moving averages because of the high correlations between moving averages from over- lapping years. The dependent variable can be the actual percentage of workers using public trans- portation, as in the following model: Percentage of workers using public transportation t = Intercept + Slope * Yeart where Yeart has the values 1 to N (number of years for which annual data are available). Alternatively, the dependent variable can be the natural logarithm of the percentage of work- ers using public transportation to work. A log transformation of the data does not change the overall direction of the trend, but it flattens the percentages and might give more realistic results. For example, applying a decreasing linear trend that uses the actual percentages, the percentage of workers using public transportation will equal zero in some future year. With a logarithmic transformation, however, it approaches zero but does not exactly equal zero. The logarithmic regression is of the following form: Ln(Percentage of workers using public transportation t) = Intercept + Slope * Yeart The regression equations can then be used to predict the percentage of workers using public transportation in any given future year. In this case study, the trends are not modeled because the number of datapoints available for estimation is very small. Other statistical techniques that can be used to model trends are Poisson regressions and time series analyses, which require more specialized software packages than those that perform OLS regression. Also, note that time series models allow for correlation in the error terms of the mod- eled observations, unlike OLS or Poisson regressions, which assume that the error terms are independent. 6.5 Specific Uses of Census Data for Trend Analyses 6.5.1 Demographic Trends Transportation planners and demographers need to monitor how a region's population has changed over time to better understand how the region's transportation system has evolved in order to inform forecasts of future regional growth. Some examples include Use of census data to understand demographic and economic growth in Lake, Porter, and LaPorte counties in Northwestern Indiana (Northwestern Indiana Regional Planning Commission).56 Development of growth and regional change projections by TAZ for the Johnson City (Ten- nessee) MPO Long-Range Transportation Plan.57 Use of census data by several counties in preparation of their comprehensive plans to under- stand trends and project these trends into the future. For example, Broward County, Florida, uses census data for economic and population modeling. 56 See www.nirpc.org/Census-DemoIntro.html. Last accessed November 8, 2004. 57 Johnson City MPO Long-Range Transportation Plan, "Section 2: Growth and Regional Change." See www.jcmpo.org/lrtp/chap2.pdf (August 15, 2001).

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Trend Analyses Using ACS Data 115 Use of CTPP 2000 data by the Maryland State Data Center to create a CD-ROM containing selected items from Part 1 and Part 2 for mapping via ArcView. Place-of-work data were used as part of an analysis of 10 military bases in Maryland in preparation for base realignment and closure, 2005.58 Use of census data to examine the aging of population and racial diversity, determine future transportation needs, evaluate travel behavior based on age and/or race groups, develop high- way projects to add capacity, develop and support policies (e.g., access control on identified cor- ridors in anticipated high-growth areas), and support public involvement/outreach purposes.59 Use of census data to develop population growth factors and demographic forecasts for trans- portation planning development work and modeling efforts.60 Use of 150 years of census data to study trends in the race and ethnicity history of Utah (espe- cially focusing on minority groups) and construct a historical county-level race/ethnicity data- base for Utah spanning 1850 through 2000. 61 6.5.2 Journey-to-Work Trends Trends of intercounty commuting are being classified at three levels: at the national level, for long-distance commuting, and for metropolitan commuting. Cervera and Hartgen,62 and Soot, DiJohn, and Christopher63 provide examples of recent efforts to review journey-to-work trends. Additional examples of published reports on journey-to-work trends include the following: FHWA's Journey-to-Work Trends Report64 that describes commuting trends at the national level, as well as for large metropolitan areas, using data from 1960 to 2000. North Jersey Transportation Planning Authority's (NJTPA) use of county-to-county com- muter flow data from 1980 to 2000 to depict commute patterns for residents in Northern New Jersey.65 The report analyzed changes in trip mode shares to work, travel times, and vehicle availability from 1990 to 2000 at the county level in Northern New Jersey. Metropolitan Council of Twin Cities' use of county-to-county commuter flows to analyze regional changes in commute behavior in the Minneapolis-St. Paul Region.66 Puget Sound Regional Council's analysis of trends in the Central Puget Sound Region using 1980, 1990, and 2000 Census data.67 Northwest Michigan Council of Government's use of census data to map trends in popula- tion, employment, and commute for the 10 counties constituting Northwest Michigan.68 58 Personal correspondence with Jane Traynham, Maryland State Data Center. For an example of the applica- tion, see www.mdp.state.md.us/msdc/military/military_site.htm. November 10, 2004. 59 Personal correspondence with Minnesota DOT, Indiana DOT, Pioneer Valley Regional Planning Commis- sion, Tulare County Association of Governments. 60 Personal correspondence with Nebraska Department of Roads, Denver Regional Council of Governments (DRCOG), Southeast AR Regional Planning Commission (Arkansas). 61 P.S. Perlich, Utah Minorities: The Story Told by 150 Years of Census Data. University of Utah, 2002. 62 E.D. Cervera and D.T. Hartgen, "Trends in North Carolina's Inter-County and Intra-County Commuting, 1990- 2000," Submitted to the Transportation Research Board, National Research Council, Washington, D.C., 2003. 63 Siim Soot, Joseph DiJohn, and Ed Christopher, 2003, "Chicago-Area Commuting Patterns and Emerging Trends," Urban Transportation Center, March 28, 2003. See www.berwyned.com/papers/co2cochgo.pdf. 64 N. McGuckin and N. Srinivasan, "Journey to Work Trends in the United States and its Major Metropolitan Areas, 1960-2000." Federal Highway Administration, 2003. 65 North Jersey Transportation Planning Authority, "Journey-to-Work Data: Census 2000 County-to-County Worker Flow Data for the NJTPA Region," November 2003. See www.njtpa.org/planning/census2000/ 2000JTWAnalysis2.pdf. 66 Robert Paddock, "County-County Commute Flow in the Minneapolis-St. Paul Region" CTPP 2000 Status Report, May 2003. See www.trbcensus.com/newsltr/sr0503.pdf. 67 Puget Sound Regional Council, "Puget Trends," No. T1, April 2003. 68 Northwest Michigan Council of Governments, "Transportation to Work Characteristics and Trends for Northwest Michigan," August 2002.

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116 A Guidebook for Using American Community Survey Data for Transportation Planning Commuting patterns in Utah and county trends for 1980, 1990, and 2000.69 Trends in 1970 through 1990 county-to-county commuter flows by means of transportation, "desire line" maps, reverse commuting, and interregional commuting in the Philadelphia region.70 Analysis of commuting to downtown in the San Francisco Bay Area.71 San Francisco Bay Area MTC's study of county-to-county commuters from 1960 to 1990 and the change in household vehicle availability since 1960.72 Other reports by the Chicago Area Transportation Study,73 the San Diego Association of Gov- ernments, the Puget Sound Regional Council in the Seattle region, the Maryland Department of Transportation.74 In addition, through interviews and personal correspondence with transportation planners at state departments of transportation and metropolitan planning organizations, it was evident that journey-to-work trends analysis (e.g., modal split, households without vehicles, locality-to-locality commute flows, average commute) is a common application of the decennial census data (e.g., at Minnesota DOT, Indiana DOT, Mass Highway, Hampton Roads Planning District Commission, Yakima Valley Conference of Governments, Denver Regional Transit District). For example, at Indi- ana DOT, available census employment data were compared to previous data in order to identify changes in employment type, primary industry, and occupation. Identified trends were used to fore- cast travel demand, evaluate access to jobs, study the movement of goods, and develop/evaluate transportation projects or policies designed to encourage future economic expansion. 69 P. Perlich, "Commuting Patterns in Utah: County Trends for 1980, 1990, and 2000." Utah Economic and Busi- ness Review, 2003. 70 Delaware Valley Regional Planning Commission, "Journey to Work Trends in the Delaware Valley Region, 1970-1990." Direction 2020 Report 5, Philadelphia, June 1993. 71 C. Purvis, 2004, "Commuting to Downtown." See www.mtc.ca.gov/datamart/census/ctpp2000/ (May 6, 2004). 72 C. Purvis, 1994, "The Decennial Census and Transportation Planning: Planning for Large Metropolitan Areas," Transportation Research Board, Decennial Census Data for Transportation Planning, Conference Proceedings 4, Irvine, California, March 13-16, 1994. 73 Ed Christopher, 1996, "Census Data Use in Illinois by a Large Metropolitan Planning Organization," Trans- portation Research Board, Decennial Census Data for Transportation Planning, Case Studies and Strategies for 2000, Conference Proceedings 13, April 28-May 1, 1996. 74 TCRP Report 28: Transit Markets of the Future: The Challenge of Change, Transportation Research Board, National Research Council, Washington, D.C., 1998. See http://gulliver.trb.org/publications/tcrp/tcrp_rpt_28- a.pdf.