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This section describes travel demand modeling as a common application of census data. Section 9.1 defines travel demand modeling and describes how census data can be used to support it. This section also provides some examples of uses of census data for this purpose. A more detailed list of specific uses is provided at the end of this section. Section 9.2 describes some benefits and limitations of shifting from census to ACS data related to travel demand modeling. Section 9.3 provides two case study examples. The first case study shows how to estimate an auto ownership model using ACS PUMS data. The second case study describes how ACS data may be used in the validation of a trip distribution model. Finally, Section 9.4 details the specific uses of census data for travel demand modeling. 9.1 Travel Demand Modeling Travel demand modeling consists of a variety of mathematical models developed to support long-range transportation plans and policy planning analyses. Transportation planners have used decennial census data for different components of travel demand modeling, including trip generation, trip distribution, mode choice, traffic assignment, demographic and auto ownership models, and microsimulation. The specific ways in which census data can assist in travel demand modeling are described next. 9.1.1 Trip Generation Traditional trip generation models relate the number of trips produced and attracted in TAZs to the characteristics of those zones. Census data are generally the best source of zonal estimates. 9.1.2 Model Input for the Base Year Transportation planners rely heavily on census data as a primary source of socioeconomic and demographic data needed as base-year input to travel demand models. Almost all trans- portation planners contacted during this research use census data in this context and have updated (or are in the process of updating) their travel demand models to include the 2000 socioeconomic data. Where CTPP data were still unavailable at the time that the plan- ners provided their opinions, these planners were often using Summary Files 1 and 3 to sup- port modeling applications. Many MPOs participated in the TAZ-Update Program to define/transfer their local TAZ structure into TIGER/Line 2000. Some MPOs aggregate block or block group level data to define TAZs. CTPP 2000 provides data at the TAZ, census tract, andâin some casesâblock group geography. MPOs are able to use these data easily in their models. 141 C H A P T E R 9 Travel Demand Modeling Analyses Using ACS Data
9.1.3 Trip Generation Rates Since data on trip frequency per household or worker are not available from the census, trip generation models cannot be estimated using census data. However, observed work trip travel patterns could be used to calibrate work trip generation models. Trip attraction models might be more difficult to calibrate and validate due to various issues associated with the way the cen- sus estimates employment. 9.1.4 Trip Distribution Aggregate calibration of friction factors used in gravity work trip distribution models is being done using observed flows from the census, by monitoring average commute and commute time frequency distribution.99 9.1.5 Work Trip Mode Choice Modeling Mode choice models cannot be estimated using census data, but the data can be used to cali- brate and validate existing work-based mode choice models. 9.1.6 Traffic Assignment Modeling Census travel time data are used to calibrate and adjust speeds and travel times in traffic assignment models. 9.1.7 Demographic and Auto Ownership Models Estimation and validation of demographic (e.g., household income distribution models, distribution models for households by number of workers/persons/vehicles available in house- hold) and auto ownership models are being performed using census data. For example, disaggregate models are being estimated using PUMS data.100 Aggregate validation of those models could be done using CTPP Part 1 (CTPP also can be used for aggregate estimation of models). 9.1.8 Microsimulation In addition to the traditional modeling steps, census data are being used for more advanced model components as well, such as using PUMS data for population synthesis for microsimula- tion models. 9.1.9 Examples of Use This section provides some examples of presenting travel demand modeling analyses. Figure 9.1 shows a home-based work trip length frequency distribution,101 and Figure 9.2 shows out-of-county 142 A Guidebook for Using American Community Survey Data for Transportation Planning 99 Examples of DOTs/MPOs where trip distribution calibration efforts were done using Census data are: Indiana DOT, Vermont Agency of Transportation, Mass Highway, Chicago Area Transportation Study (personal corre- spondence). 100 Travel Model Improvement Program, U.S. DOT, âModel Validation and Reasonableness Checking Manual.â See http://tmip.fhwa.dot.gov/clearinghouse/docs/mvrcm/ch1.stm. November 4, 2004. 101 A. Noelting, 2005. âU.S. Census, CTPP, and NHTS Data Used in the Des Moines Area MPOâs Travel Demand Model.â See www.fhwa.dot.gov/ctpp/sr0105.htm.
Travel Demand Modeling Analyses Using ACS Data 143 Figure 9.2. Out-of-county commute map. Figure 9.1. Home-based work trip length frequency distribution.
commutes.102 See Section 9.4 for specific examples that illustrate how census data have been used to do travel demand modeling. 9.2 Benefits and Limitations of ACS for Travel Demand Modeling In discussions with transportation planners, the following potential benefits of ACS were identified: â¢ The availability of data on a continuous basis provides opportunities for more frequent updates of travel demand models (in particular, the base-year socioeconomic and demo- graphic data). Again, the sparse sample size and resulting standard errors in the data during any particular year could limit the potential opportunities. â¢ If PUMS data are made available from ACS on a continuous basis, this has positive implica- tions for tracking regional changes taking place in regions experiencing accelerated growth. â¢ In selecting demographic variables for use in developing models, usually these variables are restricted to those found in the census databases, so that the models can be applied to the full pop- ulation using the joint distributions from the census data. Because ACS will enhance trend analy- sis and ACS data are available continuously, more variables could be forecast into the future, and it is likely that more demographic variables could then be included in travel demand models. â¢ ACS should not cause problems with model estimation since decennial census data (except for PUMS) are generally not used for parameter estimation of work trip travel models due to the aggregate nature of the data. The following ACS issues were identified as limitations for travel demand modeling: â¢ Theoretically, five-year cumulative averages are inconsistent with models that are used to predict at a point in time (such as trip distribution models or mode choice models), and model calibration/validation could be problematic. However, practical ways could be found to over- come this limitation. For example, the validation of work mode choice models occurs at a coarse level of geography, and changes in household characteristics and mode choices over five years would probably not be very significant. â¢ As discussed above, the cumulative averaging of ACS model inputs is inconsistent with most travel demand models in use. In addition, the larger standard errors associated with ACS parameters, compared to decennial census Long Form parameters, increase the variability and error of models that rely on these data. In most instances, travel demand modelers treat cen- sus Long Form data as simple point estimates and do not acknowledge in their model systems that these data are subject to sampling and non-sampling error. In migrating to using ACS data, it will be more difficult to make these simplifying assumptions. â¢ Origin-destination matrices developed from journey-to-work data can be problematic. Prob- lems include: sparse data from a single year for small geographies, seasonality if data are aver- aged over a year, and changes in development of new housing and business locations over three to five years if rolling averages are used. 9.3 Travel Demand Modeling Case Studies The following case studies illustrate how a data user might use ACS data to support travel demand modeling efforts. The case studies provide a step-by-step description of how one might obtain the data, do the computations, and present the results. 144 A Guidebook for Using American Community Survey Data for Transportation Planning 102 North Jersey Transportation Planning Authority, 2003. âJourney-to-Work Data: Census 2000 County-to- County Worker Flow Data for the NJTPA Region.â
For this purpose, assume that you are a transportation analyst working in an MPO. In the first analysis, your manager has asked you to estimate an auto ownership model. In the second analy- sis, you are asked to validate a trip distribution model. Section 3 of this guidebook has detailed instructions on downloading ACS data. 9.3.1 Analysis 1âEstimation of Auto Ownership Model This case study illustrates how an auto availability model can be estimated using ACS data. Auto availability models can be estimated using disaggregate household data. The ACS PUMS is a great source of disaggregate detailed household data with information on household income, size, workers, type, etc. One limitation of the data is that the household residence is not available at a geographic level fine enough to allow the use of accessibility measures and land-use density in the model. The geographic level that is normally reported is the PUMA, but the ACS PUMS that currently are available on the American FactFinder website have state-level data. The model estimated in this case study builds on the work done previously103 to estimate automobile own- ership models for the Bay Area and the San Diego region. This exercise requires estimating an auto availability model for the state of California using recent ACS PUMS data from years 2000 to 2003. To present the results of the auto availability model exercise, it is important to show the alternatives modeled, explanatory variables used, parameter estimates, statistical significance of the variables, and model fit. This information is displayed in Table 9.1. NOTE: T-statistics appear in parentheses; parameters are set to zero in the model specification. Travel Demand Modeling Analyses Using ACS Data 145 103 C. Purvis, âUsing 1990 Census Public Use Microdata Sample to Estimate Demographic and Automobile Own- ership Models,â Transportation Research Record 1443, TRB, National Research Council, Washington, D.C., 1994. Alternative Variable 0-Vehicle 1-Vehicle 2+ Vehicles Constant -0.75 (t = -37.13) - â -2.74 (t = -132.18) Persons in household -- -- 0.34 (t = 54.42) Workers in household -- 0.17 (t = 10.23) 0.93 (t = 52.73) Income: $ 35,000 â¤ Income < $ 70,000 -- 1.03 (t = 33.0) 1.61 (t = 49.66) Income â¥ $ 70,000 -- 0.93 (t = 22.48) 2.26 (t = 54.82) One-family house -- 0.66 (t = 27.06) 1.89 (t = 73.20) Model Statistics Likelihood with Zero Coefficients -151,351.42 Likelihood with Constants only -118,021.73 Final value of Likelihood -87,412.02 âRho-Squaredâ with respect to Zero 0.4225 âRho-Squaredâ with respect to Constants 0.2594 Note: - â Parameters set to zero in the model specification. Rho-squared is a goodness-of-fit measure for discrete choice models that is analogous to R-squared in regression analysis. Table 9.1. Magnitudes of variable coefficients.
In addition, the following conclusions can be associated with this analysis: â¢ The alternative-specific constants of the zero and two-plus alternatives are negative, which indicates that, if all else is the same, households are more likely to have one vehicle available. Moreover, a household is more likely to have zero than two vehicles. â¢ As household size increases, a household is more likely to have two or more vehicles available. â¢ The presence of workers in the household increases the utility of having one or more vehicles available, and the effect is stronger for the two-plus vehicle category. â¢ Relative to low-income households, medium- and high-income households are more likely to have one or more vehicles available, and the effect is stronger for the two-plus vehicle category. â¢ Finally, the effect of dwelling type on vehicle availability is that one-family houses are more likely to have one or more vehicles available than zero vehicles, and are more likely to have two-plus vehicles than one vehicle. Available Data The auto availability model that is estimated in this case study is for the state of California. Four years of ACS data, 2000 through 2003, are pooled to increase the sample size. Note that the pooling of the household records from these four years does not cause any correla- tion problems in the estimation since the four samples will not have overlapping housing units.104 The ACS PUMS data are composed of a household file and a person file. Even though the vehicle availability model is estimated at the household level, the person file also provides some characteristics that can be used in the model, such as number of workers in the household. To download the data, the user should follow the steps below: â¢ Go to the American FactFinder website at http://factfinder.census.gov/home/saff/main. html?_lang=en, â¢ Click on the Datasets tab, â¢ Select the â2000-2003 American Community Surveyâ tab, â¢ Click on the link leading to âPublic Use Microdata Sample (PUMS)â, and â¢ Click on a certain year (e.g., 2003) and then download the person records (âPâ records) and household records (âHâ records) for a selected state. Analysis Steps The 2000-2003 household records are pooled into one sample for estima- tion, removing those records that correspond to vacant units or to group quarters. The follow- ing variables from the household file are retained for use in the estimation: number of vehicles in the household (variable is VEH), number of persons in household (variable is NP), household income (variable is HINCP), and type of residence (variable is BLD). In addition, the person files are used to obtain the number of workers in the household (variable is COW). Two types of adjustment factors are applied to the income variable, as follows:105 â¢ For a given ACS PUMS year, the first adjustment factor is a value that is applied to all obser- vations obtained from that year. This factor is included in the PUMS datasets and is called ADJUST. The reason this adjustment is needed is because interviews in the ACS were con- ducted throughout the year. Application of the adjustment factor will convert dollar amounts to July (of the given year) dollars.106 â¢ The second adjustment factor is needed because ACS PUMS data from years 2000-2003 are used in this case study. When working with dollar amounts from different years, it is necessary to convert the amounts into dollars from a common year (after applying the adjustment factor 146 A Guidebook for Using American Community Survey Data for Transportation Planning 104 No housing unit will be sampled more than once in a five-year period. 105 Correspondence with Nicholas Spanos of the Census Bureau, May 27, 2005. 106 Note that the value of ADJUST is the same for all sample cases. This is for disclosure avoidance reasons, that is, so that the month of interview cannot be identified by the adjustment factor. The original dollar amounts were adjusted so that one value of ADJUST could be used for all sample cases.
described in the previous paragraph). The CPI-U-RS adjustment factors from the Bureau of Labor Statistics are used.107 The number of household observations used in the estimation is equal to 137,766. The alter- natives are zero, one, and two-plus vehicles. The model estimation exercise consists of iteratively selecting explanatory variables; running the model through model estimation software; exam- ining the magnitudes, signs, and t-statistics of the coefficients and overall model fit; and adjust- ing the selected variables accordingly. 9.3.2 Analysis 2âValidation of a Trip Distribution Model This exercise requires validating the trip distribution gravity model of a county-level travel demand model system by comparing model results to observed data. To present the results of this model validation exercise, it is useful to show two types of com- parison. The first is a comparison of number/percentage of trips from a given origin to all des- tinations (e.g., at the district level). The second is a comparison of county-level mean travel time and travel time distribution. Each of these comparisons can assist in adjusting the coefficients of the gravity model if large discrepancies exist between modeled and observed travel times. For example, Table 9.2 shows the number and percentage of trips from District 1 to all other districts using the 2000 ACS and the gravity model, as well as the difference between the two sources. The table shows that â¢ Overall, the number of trips originating from District 1 is under simulated. â¢ In terms of distribution of trips, the largest discrepancies occur with the intradistrict flow (to District 1 at â4.5 percent) and the flow to District 2 (at 5.4 percent). Figure 9.3 compares the travel time distribution obtained from the gravity model to the ACS reported travel time distribution. The figure shows that the model under predicts short trips and over predicts long trips. Available Data Two data sources are available to do this analysis. The first data source is the trip distribution model outputs in terms of number of trips and travel time skims by origin-des- tination pair. The second data source is ACS, which provides data on worker flows between every origin-destination pair (assuming a CTPP-like product from ACS is available) and reported travel time data for these origin-destination pairs. Travel Demand Modeling Analyses Using ACS Data 147 107 These factors can be found at the following URL: http://www.bls.gov/cpi/cpiurstx.htm. [For example, to express year 2000 dollars in terms of 2003 dollars, multiply the 2000 dollars by 267.9/250.8 = 1.06818182]. Origin: District 1 ACS Gravity Model Gravity Model â ACS To District Flow Percentage Flow Percentage Flow Percentage 1 36,545 63.0 30,000 58.5 -6,545 -4.5 2 14,945 25.8 16,000 31.2 1,055 5.4 3 2,705 4.7 2,000 3.9 -705 -0.8 4 1,750 3.0 1,500 2.9 -250 -0.1 5 2,070 3.6 1,800 3.5 -270 -0.1 -0.1Total 58,015 100 51,300 100 -6,715 Table 9.2. Comparison of worker trips from a given district to all other districts using ACS and the gravity model.
Note that in addition to these data sources, one also can use the household travel survey, where respondentsâ trip origins and destinations can be geocoded and the corresponding travel time skims used to derive an observed travel time distribution. An origin-destination survey, if avail- able, also can be a valuable data source for validating trip interchanges. For this case study, the available ACS data (obtained from San Francisco County records) were at the tract-to-tract flow level. They were aggregated to the district level producing the flows in Table 9.3. Note that the gravity model numbers presented in this case study are fictitious. Analysis Steps The following four steps are involved in conducting this analysis: 1. Select the level of geography at which the validation of flows should be conducted. The selec- tion depends on the model type (e.g., statewide model, countywide model, etc.) and the desired level of accuracy. For this analysis, the validation of flows is conducted at the district- to-district level. 2. Aggregate the flows from the available geographic level detail to the desired geographic level. For example, the ACS flow data are available for this case study at the tract-to-tract level. A correspondence table between tracts and districts is used to derive the district-to-district flows presented in Table 9.3. 3. Since ACS flows correspond to the home-to-work direction only, the model home-based work flows, which combine both the home-to-work and work-to-home directions, should be divided in half to be comparable to the ACS data. 4. Use the tract-to-tract reported travel times to derive a travel time distribution from ACS and compare it to the model distribution. The following caveats regarding using ACS data for the validation of trip distribution should be noted: â¢ The ACS travel times are reported travel times; hence they inherently suffer from respondent rounding and inaccuracy. â¢ Because of confidentiality issues, ACS flow data might be suppressed for origin-destination pairs that do not meet the threshold for data tabulation. This might cause inaccuracies when comparing ACS flows to model flows. â¢ ACS measures worker flows rather than trips; ACS does not account for absenteeism or for mul- tiple job locations. These factors can cause additional differences between ACS and model results. 148 A Guidebook for Using American Community Survey Data for Transportation Planning 0 5 10 15 20 25 0-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40 40-45 45-60 60-75 75-90 More Travel Time (in Minutes) Trips (in Percent) ACS Gravity Model Figure 9.3. Travel time distributions from ACS and the gravity model.
9.4 Specific Uses of Census Data for Travel Demand Modeling This section provides a list of specific examples of uses of census data by transportation plan- ners to do travel demand modeling. 9.4.1 Trip Generation Model input for base year is being performed at Indiana DOT. Census Bureau variables used in the statewide travel demand model include number of households, number of household units, population size, population age, number of children, average income, household income, total employment, occupation, number of autos per household, journey-to-work data, and land use information (residential density, accessibility, and urbanized area boundaries). Similar vari- ables are used by other DOTs and MPOs.108 Efforts involving trip generation rates include the following: â¢ The 1990 CTPP data were used by the Gainesville Urbanized Area to calibrate home-based work trip rates since its travel demand model was under assigning trips.109 The last available trip rates were from 1971. â¢ The Chicago Area Transportation Study, Vermont Agency of Transportation, and MTC (using PUMS data to create county-level calibration files) also have used census data to corroborate trip generation rates.110 9.4.2 Work Trip Mode Choice Modeling Examples of work trip mode choice modeling efforts include the following: â¢ Work done by the Denver Regional Council of Governments (where geographic market segments were identified and mode choice models were calibrated separately for each), Travel Demand Modeling Analyses Using ACS Data 149 108 The DOTs and MPOs that have indicated the use of Census data in this context include: Des Moines MPO, Minnesota DOT, Vermont Agency of Transportation, Mass Highway, Broward County MPO, Hampton Roads Planning District Commission, Chittenden County MPO, Mid-Ohio Regional Planning Commission, Pioneer Valley Planning Commission, Tulare County Association of Governments, Pima Association of Governments, Yakima Valley Conference of Governments, and Caliper Corporation. 109 W. Blanton, 1996, âSmall-Area Applications Using 1990 Census Transportation Planning Package: Gainesville, Florida.â Proceedings of a Conference on Decennial Census Data for Transportation Planning: Case Studies and Strategies for 2000. 110 C. Purvis, âUses of PUMS 2000 Data at the Metropolitan Transportation Commission,â Transportation Research Board, National Research Council, Washington, D.C., 2004. Destination District Origin District 41 2 3 5 Total 1 36,545 14,945 2,705 1,750 2,070 58,015 2 15,433 18,385 3,680 1,584 2,405 41,487 3 23,805 25,423 21,954 5,110 5,549 81,841 4 16,020 15,455 5,700 13,650 4,275 55,100 5 10,160 8,105 1,790 1,955 5,245 27,255 Total 101,963 82,313 35,829 24,049 19,544 263,698 Table 9.3. Available ACS flow (workers) data.
Central Transportation Planning Staff, Chicago Area Transportation Study, and Caliper Corporation;111 â¢ The use of PUMS data at MTC to create county-level calibration files for an auto ownership model; and â¢ The use of PUMS data and CTPP flow data at King County Transit to assess whether vanpool usage as inferred from the census matches with actual observations, in which case census data are used for further analysis. 9.4.3 Traffic Assignment Modeling An example of using census data to calibrate and adjust speeds and travel times in a traffic assignment model includes the analysis of New Jersey counties in the DVRPC region. 9.4.4 Demographic and Auto Ownership Models Examples of agencies/researchers using census data for demographic and auto ownership models include the following: â¢ Mid-Ohio Regional Planning Commission estimating models, using variables such as income and area type, for predicting the distribution of households by household size; â¢ Municipality of Anchorage, Central Transportation Planning Staff auto ownership model; â¢ Caliper Corporation auto ownership and employment models; â¢ Cambridge Systematics112 vehicle availability model for New Hampshire; and â¢ Auto ownership model for Honolulu, Hawaii.113 9.4.5 Microsimulation Examples of agencies/researchers using census data for microsimulation include the following: â¢ Census data are used at Caliper Corporation to validate aggregate employment shares. â¢ PUMS data are used for population synthesis for microsimulation models at Caliper Corpora- tion. Synthetic population estimates can be input to very disaggregate travel behavior models. â¢ Citilabs currently is building a tool for developing synthetic populations (e.g., demographic data for use in models) through sample enumeration using PUMS data. The developed population estimates will be controlled to full counts from the census. â¢ TRANSIMS114 creates synthetic household information using PUMS data. The âPopulation Synthesizerâ routine in TRANSIMS takes in various types of census data at the census block and census block group level to generate synthetic households, individuals, and vehicles through a series of six steps. 150 A Guidebook for Using American Community Survey Data for Transportation Planning 111 At Caliper Corporation, CTPP data also are used for calibrating a time-of-day model and a destination choice gravity model related to work trips. 112 Cambridge Systematics, Inc., 1997, âVehicle Availability Modeling.â Prepared for FHWA. 113 J.M. Ryan and G. Han, âVehicle-Ownership Model Using Family Structure and Accessibility Application to Hon- olulu, Hawaii.â Transportation Research Record 1676, TRB, National Research Council, Washington, D.C., 1999. 114 Travel Model Improvement Program, U.S. DOT, 1998 âTRANSIMS: The Dallas Case Study.â See http://tmip.fhwa.dot.gov/clearinghouse/browse, November 4, 2004.