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In cooperation with the TRB Census Subcommittee

CTPP Status Report

January 2012

Census Transportation Planning Products (CTPP) AASHTO Update

Penelope Weinberger, AASHTO,

The CTPP Oversight Board met in October following the Census Data Conference held in Irvine, California. One big decision the board made was to extend the CTPP program for another year, through 2013. The funds are available and the need is certainly there, as the CTPP tabulation based on five-year ACS is not expected to be delivered until 2013. We are reviewing proposals for Commuting in America IV and expect a product which includes a web-based portal for information on commuting trends.

The CTPP training team continues to "spread the word" by visiting States and MPOs to provide day-and-a-half-long training, and attending conferences and meetings with workshops and presentations, covering what the CTPP is, how to best use it and caveats for use. Additional ways to learn about CTPP products and issues are through the e-Learning modules Topics include ACS, CTPP based on ACS, Geography, Margins of Error, and changes to the CTPP related to the discontinuation of the Census Long Form. More e-Learning modules are expected soon.

Using Census Data for Transportation Applications Conference

Jonette Kreideweis, TRB Conference Chair,

A TRB sponsored "Using Census Data for Transportation" conference held in October 2011 affirmed the critical importance of demographic, mode and travel flow data from the U.S. Census Bureau for transportation planning applications. Presentations from the conference are available at Katie Turnbull from TTI is preparing the conference summary.

Nearly 120 Federal, state, MPO, consultant, and academic participants shared their experiences in using census data and identified opportunities for overcoming issues and improving census data for decision-making. Presentations demonstrated the value of census data for planning, policy, travel demand modeling, environmental justice, and transit studies.

Participants heard how Census Bureau's (CB) American Community Survey (ACS) standard tables are being integrated and supplemented with data from the Census Transportation Planning Products (CTPP) program, the Longitudinal Household Dynamics (LED) program and an increasing number of private and publicly available data sources.

Data providers, researchers and technical experts shared how they are dealing with data issues and challenges and discussed new products and processes to improve the utility and availability of census data products. Breakout sessions identified themes to guide future census data efforts:

Research Areas (selected):

The Use of ACS and Decennial Census Data Products in the Demographic Forecasting Process at NCTCOG

Kathleen Yu,, Behruz Paschai,, Arash Mirzaei,, North Central Texas Council of Governments

Demographic Forecasting at NCTCOG

Figure 1. Map of 12-county MPA.

North Central Texas Council of Government (NCTCOG) metropolitan planning area (MPA) 12 counties boundary

The travel demand model at the North Central Texas Council of Government (NCTCOG) covers the metropolitan planning area (MPA) that includes 12 counties with a total area of about 10,000 square miles, as shown in Figure 1. The total population of the region has grown from 5,199,317 in year 2000 to 6,417,724 in year 2010, a total increase of 23.43 percent, as shown in Table 1. NCTCOG has historically used the DRAM/EMPAL model for the distribution of the population and employment control totals in the region. In the recent update to the demographic forecasts the G-LUM open-source application written in MATLAB script ( at The University of Texas at Austin ( was used. The travel model at NCTCOG has 5,386 traffic survey zones (TSZ) and 242 demographic forecasting districts. Note that the 2010 Decennial Census and ACS (2005-2009) population data was not available until after the completion of the forecasting process. The population and employment control totals used in the forecasting process were purchased from the Perryman Group in year 2007 and verified against the other available data sources (State Data Center, Texas Water Development Board, NCTCOG historical datasets, and ACS data). The 2000 household dataset was created based on the Decennial Census data and modified based on local input. However, the 2000 employment and 2005 household and employment datasets were constructed based on an in-house development monitoring program.

Table 1. Total Population Growth by County

County Total Population (Decennial Census)
County Census 2000 Census 2010 Difference Percent Total Growth Percent Simple Annual Growth Percent Share of Total Growth
Collin 491,675 782,341 290,666 59.12 5.91 23.86
Dallas 2,218,899 2,368,139 149,240 6.73 0.67 12.25
Denton 432,976 662,614 229,638 53.04 5.30 18.85
Ellis 111,360 149,610 38,250 34.35 3.43 3.14
Hood 41,100 51,182 10,082 24.53 2.45 0.83
Hunt 76,596 86,129 9,533 12.45 1.24 0.78
Johnson 128,811 150,934 22,123 17.17 1.72 1.82
Kaufman 71,313 103,350 32,037 44.92 4.49 2.63
Parker 88,495 116,927 28,432 32.13 3.21 2.33
Rockwall 43,080 78,337 35,257 81.84 8.18 2.89
Tarrant 1,446,219 1,809,034 362,815 25.09 2.51 29.78
Wise 48,793 59,127 10,334 21.18 2.12 0.85
Total 5,199,317 6,417,724 1,218,407 23.43 2.34 100.00

Household Size

Figure 2. Historical change in average HH size.

Line chart. Click image for source data.

The household population in year 2005 and all forecast years were calculated based on the estimated number of households (HH) and the average HH size in each corresponding year. Therefore, it was necessary to first obtain the average HH size in year 2005 and to establish a method for calculating the future average HH sizes. The census data products indicate that the average HH size in the NCTCOG region increased from 2.70 in year 2000 (Decennial Census) to 2.81 in year 2005 (ACS 2005). This increase was considered significant and therefore triggered the need for reviewing the historical trend of the average HH size in the region based on the data available in the Census Fact Book and the Decennial Census, as shown in Figure 2. We also compared the average HH sizes based on the 1990 and 2000 Decennial Census against the values reported by ACS products, as shown in Figure 3.

These comparisons did not show a logical pattern that could be used for forecasting the future HH sizes in the region. However, it did show differences in the average HH size between urban and rural counties and that the average HH size becomes more stable as an area becomes more urbanized. The purchased Woods and Poole data indicates that the average HH size for the NCTCOG 16-county area will not exceed 2.70 in the next 20 years. The final HH sizes utilized in the forecasting process were calculated starting from the ACS 2005 values and based on the assumptions listed below:

Figure 3. Comparison of average HH sizes in the 1-county MPA.

Line chart. Click image for source data.

The 2010 Census and ACS (2005-2009) became available after the forecasts were produced. Hence, they were only used to further verify the changes in the average HH size of the states against the Census 2000 data and to evaluate the difference between the ACS (2005-2009) and 2010 Decennial Census. This comparison indicated that based on the ACS (2005-2009), 19 states have shown an increase in their average HH size, as displayed in Table 2. However, the 2010 Decennial Census indicated that the average HH size only increased in five states: Nevada, California, Florida, Delaware, and Texas. The ACS (2005-2009), overestimated the average HH size with rather small margins of error (MOE) when compared to the 2010 Decennial Census in all the states where it represented an increase in average HH size compared to the 2000 Decennial Census. In four of the states (Nevada, California, Maryland, and Tennessee), the 2010 Decennial Census average HH size was at the lower boundary of the ACS (2005-2009) estimated range.

Population to Employment Ratio

The population to employment ratio (P/E) in the NCTCOG region has been historically around 1.60. It was more conservative for travel model forecasting to assume that it will remain at 1.60 in all forecast years. The control totals as provided by the Perryman Group also followed the same trend in all forecast years. The original NCTCOG calibration dataset for year 2005 was modified such that the total number of households and average HH size matched the ACS 2005 data in the 12-county MPA, and the P/E ratio was 1.60. These adjustments resulted in a reduction of about 200,000 in the household population and an increase of about 300,000 in the number of employees in the region for year 2005 compared to the original 2005 dataset.

Average HH Size
State Census 2000 Census 2010 Percent Difference vs. Census 2000 ACS (2005-2009) M
Percent Difference vs. Census 2000 Percent Difference vs. Census 2010
Nevada 2.62 2.65 1.15 2.66 0.01 1.53 0.38
California 2.87 2.90 1.05 2.91 0.01 1.39 0.34
Florida 2.46 2.48 0.81 2.52 0.03 2.44 1.61
Delaware 2.54 2.55 0.39 2.58 0.01 1.57 1.18
Texas 2.74 2.75 0.36 2.81 0.01 2.55 2.18
Maryland 2.61 2.61 0 2.63 0.02 0.77 0.77
Tennessee 2.48 2.48 0 2.49 0.01 0.40 0.40
Arizona 2.64 2.63 -0.38 2.76 0.01 4.55 4.94
Connecticut 2.53 2.52 -0.40 2.55 0.02 0.79 1.19
Georgia 2.65 2.63 -0.75 2.70 0.01 1.89 2.66
Utah 3.13 3.10 -0.96 3.14 0.02 0.32 1.29
Massachusetts 2.51 2.48 -1.20 2.54 0.01 1.20 2.42
Rhode Island 2.47 2.44 -1.21 2.52 0.01 2.02 3.28
New York 2.61 2.57 -1.53 2.64 0.01 1.15 2.72
District of Columbia 2.16 2.11 -2.31 2.21 0.02 2.31 4.74
New Hampshire 2.53 2.46 -2.77 2.54 0.01 0.40 3.25
Alaska 2.74 2.65 -3.28 2.82 0.01 2.92 6.42
Montana 2.45 2.35 -4.08 2.49 0.01 1.63 5.96
Puerto Rico 2.98 2.68 -10.07 3.21 0.01 7.72 19.78

Model Validation

Table 2. Comparison of Average HH Size

The ACS (2005-2009) HH data was used during the calibration and validation of the forecasting model. For this purpose, the estimate of the 2010 HHs was calculated based on the ACS (2005-2009) values grown for six months based on the growth rates observed between ACS 2008 and ACS 2009, and then compared to the 2010 forecasts. The results of this comparison indicated that the total 2010 HHs resulting from the forecasting process was only 0.49 percent higher than the estimates we had calculated based on the ACS (2005-2009) data, with acceptable errors at the county levels, as shown in Table 3. The 2010 Decennial Census data became available at a later stage in the process (April 2011) and hence only could be used as another comparison point for validating the 2010 forecasts that were prepared based on the ACS (2005-2009) data, as shown in Table 4. This comparison indicated that the forecasted household population for year 2010 was only -0.28 percent different in total compared to the 2010 Decennial Census, with acceptable errors at county levels. We also validated the 2010 HH population forecasts in randomly selected TSZs in a 10-mile radius. All the comparisons indicated that the 2010 forecasts match the available reference data for year 2010 with an acceptable margin of error.

Table 3. Validation of 2010 HH Forecasts

2010 Decennial Census

2010 HH by County
County 2010 Forecast ACS
(2005-2009) Estimate
Difference Percent Difference
Collin 273,533 283,400 -9,863 -3.48%
Dallas 858,538 864,039 -5,499 -0.64%
Denton 219,732 223,921 -4,189 -1.87%
Ellis 51,752 50,143 1,610 3.21%
Hood 23,818 18,346 5,472 29.82%
Hunt 34,213 29,941 4,272 14.27%
Johnson 54,405 50,898 3,508 6.89%
Kaufman 31,318 30,991 328 1.06%
Parker 41,042 37,671 3,372 8.95%
Rockwall 26,920 26,458 462 1.75%
Tarrant 653,268 644,410 8,859 1.37%
Wise 21,816 18,895 2,920 15.45%
Summary 273,533 2,279,114 11,251 0.49%

Since the 2010 Decennial Census data was not available during the forecasting process, it was not until after the fact that we were able to compare the estimated 2010 HHs based on the ACS (2005-2009) with the actual 2010 Census numbers at the 242 demographics forecasting district level. This comparison showed that the two data sources provide rather comparable data at the region and county levels, as can be seen by reviewing the data in Tables 3 and 4. However, there are some major inconsistencies in the ACS (2005-2009) data in the areas that were of most concern for the region due to their rapid growth or decline in population in the recent years, as shown in Figure 4. The areas shaded in red indicate that the ACS (2005-2009) data underestimated the 2010 household population by more than 20 percent. The dark green areas indicate an overestimation of more than 20 percent. The areas of concern, which are circled in black, include northern/southern Tarrant County and most of Collin and Denton Counties which experienced a rapid growth in the recent years, and northern Dallas County that experienced some reduction in population and employment in the previous years, as shown in Figure 5. The comparison of Figures 4 and 5 also shows that the ACS (2005-2009) has the tendency to overestimate the HH population in areas with small growth and underestimate it in areas with rapid growth in the DFW area.

Table 4. Validation of 2010 HH Population Forecasts

Nontechnical Issues

2010 HH Population by County
County 2010 Forecast 2010 Census Difference Percent Difference
Collin 761,378 782,341 -20,963 -2.68%
Dallas 2,397,572 2,368,139 29,433 1.24%
Denton 625,580 662,614 -37,034 -5.59%
Ellis 152,861 149,610 3,251 2.17%
Hood 64,427 51,182 13,245 25.88%
Hunt 90,918 86,129 4,789 5.56%
Johnson 163,748 150,934 12,814 8.49%
Kaufman 95,537 103,350 -7,813 -7.56%
Parker 116,093 116,927 -834 -0.71%
Rockwall 79,234 78,337 897 1.15%
Tarrant 1,785,206 1,809,034 -23,828 -1.32%
Wise 66,908 59,127 7,781 13.16%
Summary 6,399,461 6,417,724 -18,263 -0.28%

Aside from the numerous data-related technical issues that we had to resolve during this process, there were a couple of nontechnical issues, related to the perceived accuracy of the ACS and Decennial Census, which had to be addressed as well: 1) the overall disagreement and resistance of the demographers regarding the use of ACS products due to the sampling nature of it and the associated margins of error; and 2) the disagreement of some local governments with the data published by the Decennial Census products claiming that it underreports the minority population.

Figure 4. Comparison of HH Population based on ACS (2005-2009) estimates and Census 2010.

Comparison of household Population based on ACS (2005-2009) estimates and Census 2010.

Figure 5. Comparison of HH Population based on 2000 and 2010 Decennial Census.

Comparison of Household Population based on 2000 and 2010 Decennial Census.


The Census data products make valuable and up-to-date data available to the transportation community in fairly small geographies. The uniqueness of this data source makes it unrealistic to assume that it will not be used due to the associated accuracy disclaimers. This data is often times used for short-term decisions that have real consequences. Therefore, the need for consistency is an actual issue with which the users are faced. It seems that improvement in the expansion of the data in the subcounty level can improve the usability and credibility of the ACS datasets to a large degree.

A Preview of Small Area Transportation Data from the American Community Survey

Ken Hodges, Nielsen,
Ed Spar, COPAFS,

This report describes a preliminary look at ACS data on Means of Transportation to Work and Travel Time to Work at the block group level. The findings are a useful preview of what users can expect from ACS-based CTPP for very small areas.

Even after combining five years of data collection, the ACS sample used to provide small area data is considerably smaller than that of the Census Long Form - with 2005-2009 ACS data based on responses from only 7.6 percent of housing units, compared with 15.8 percent from the 2000 Census Long Form. As shown in Table 5, the ACS data for over 90 percent of block groups are based on fewer than 100 interviews.

Given the relatively small ACS sample, large margins of error (MOE) are expected, and Table 6 shows how often each cell in the Means of Transportation table has an MOE larger than the estimated value. The table also reveals that in many of these cases, the cell value was zero (as expected since many modes are rare in many areas). And as Table 7 illustrates, for a block group in Orange County, California, the ACS reports large MOEs for cells with a zero estimate. Travel Time to Work reflects a more even distribution, with fewer zero values, and as shown in Table 8, MOEs often exceed cell values even where cell values are greater than zero. In other words, the large MOEs cannot be blamed entirely on cells with zero or very small values.

Table 9 shows an unusually high number walking to work in another Orange County, California block group, leading one to wonder if it might be an ACS error. But given the location at the University of California, Irvine, and the fact that the 2000 census showed a similar distribution, the preponderance of walkers is plausible.

With a sample smaller than that of the Census Long Form, one would expect a higher frequency of zero values in the ACS block group data, and Table 10 confirms this tendency for Travel Time to Work. For example, for travel time "Less than 5 Minutes," the ACS reports 80,253 block groups with a value of zero compared with only 53,328 for the 2000 Census Long Form.

An examination of uncommon transportation modes confirmed that the ACS puts large numbers in expected areas. For example, block groups with the highest percent of workers commuting by "subway or elevated" were found in counties, including New York, Bronx, Queens, and Kings in New York. Workers commuting by "ferryboat" were most common in block groups in Hudson, New Jersey, Kitsap, Washington, and Richmond, New York. Just as important, the ACS seems not to show large numbers using uncommon modes in areas where they would not be expected. But there are occasional exceptions, as illustrated by Table 11 - which shows a conspicuously large number of workers (618) commuting by bicycle in a block group in Larimer County, Colorado.

Unlike the UC Irvine block group, where the large number of walkers could be expected (and was backed by the 2000 Census), there is no apparent explanation for the dominance of bicycles in the Larimer County block group (and 2000 Census provides no backup). The more likely explanation is that the ACS captured one or a few bicycle commuters, and weighted up to 618. But why would ACS weight to such an unrealistically large number? The probable explanation is that overestimation in this block group (and presumably others) compensates for a large number of block groups that actually have one or a few bicycle riders, but show zero because none were captured in the ACS sample. In other words, overestimation of this type is the flip side to the large number of zero cells reported by the ACS, and enhances the accuracy of ACS data for aggregate areas. And recall that the Census Bureau recommends that ACS block group data be used only for aggregate areas.

A broader assessment of the ACS block group data was provided by computing the index of dissimilarity (IOD) between ACS distributions and those from the 2000 Census. A measure of the difference between two percent distributions, the IOD ranges from 0 for distributions that are identical, to 100 for distributions that have nothing in common (where 100 percent of records in one distribution would have be in a different category to replicate the percent distribution of the other). Differences are expected between ACS and 2000 Census distributions, but there should be considerable similarity, and it can be useful to see where consistency is greatest.

As summarized in Table 12, the mean IOD is consistently lower for Means of Transportation than for Travel Time - probably because the dominance of "drive alone" is reflected in both ACS and the 2000 Census. For all block groups, the IOD was 15.1 for Means of Transportation and 30.7 for Travel Time. Limiting to block groups with stable population (change less than five percent from 2000 to 2010) reduces the IODs only slightly. It is when limiting to block groups with 100 or more ACS interviews that the IODs drop sharply to 8.2 and 18.3. Block groups in counties with populations of 500,000 or more actually have the highest IODs at 17.2 and 32.2. With large metropolitan counties typically of interest to transportation planners, one might wonder why consistency with 2000 Census is relatively low in these areas.

The relatively large IODs in large metropolitan counties might be a byproduct of a small town bias in the ACS sample. The ACS samples heavily in small towns to the detriment of the sample allocated to small statistical geographies in large metropolitan areas. Consequently, while 9.3 percent of households responded to ACS nationwide, the percent was only 7.7 for block groups in counties with populations of 500,000 or more.

Frequency of updates is a highly touted ACS benefit, and a review of ACS data confirms its ability to add value in areas with rapid population growth. For example, Table 13 shows the ACS Means of Transportation distribution for a block group that was part of Denver's former Stapleton Airport, and was developed following the 2000 Census. The census counted zero households in 2000, but 4,084 by 2010.

The preliminary analysis suggests a mix of strengths and limitations for small area ACS data. Users can expect large margins of error, but many are of little consequence, as they relate to reasonable estimates of zero or very small numbers. For small areas, the number of ACS interviews (unweighted sample housing units) might provide a better sense for data quality than the MOEs. The ACS certainly estimates "zero" in many cells that should have small numbers, and occasionally inflates cell values to unrealistically high levels. But some of the most questionable estimates for individual block groups contribute to enhanced accuracy for aggregate areas. Transportation planners might prefer that more ACS samples be allocated to small statistical geographies in metropolitan areas (as opposed to small towns), but the benefits of frequent updates are apparent in areas with rapidly changing populations. In short, both challenges and opportunities are apparent in the ACS block group journey to work data, and users are likely to encounter similar challenges and opportunities in the forthcoming ACS-based CTPP data.

Table 5. Block Groups by Number of ACS Interviews: 2005-2009

ACS Interviews N Pct
Missing (no ACS) 1,533 0.7
N suppressed (1 or 2) 801 0.4
3-9 2,982 1.4
10-19 24,527 11.7
20-49 115,865 55.5
50-99 48,002 23.0
100-199 13.303 6.4
200-499 1,711 0.8
500 or more 73 0.0
Total 208,797 100.0

Table 6. Percent of Block Groups with Margins of Error Greater Than Cell Values for Means of Transportation to Work

Means of Transportation Pct MOE
GT Cell Value
Cell Value = 0
Total Workers 1.6 0.7
Drive alone 3.3 1.1
Carpool 49.2 11.1
Bus or trolley bus 90.1 63.7
Streetcar 99.9 98.3
Subway 95.6 89.4
Railroad 97.5 89.8
Ferryboat 99.9 99.3
Taxicab 99.8 96.5
Motorcycle 99.7 90.3
Bicycle 98.8 86.1
Walk 88.6 51.2
Other means 98.1 74.7
Worked at home 76.5 34.6

Table 7. Means of Transportation to Work for Block Group 0630.04 3 In Orange County, California

Means of Transportation Workers MOE
Total Workers 271 +/- 114
Drive alone 207 +/- 91
Carpool 19 +/- 30
Bus or trolley bus 0 +/- 132
Streetcar 0 +/- 132
Subway 0 +/- 132
Railroad 0 +/- 132
Ferryboat 0 +/- 132
Taxicab 0 +/- 132
Motorcycle 0 +/- 132
Bicycle 0 +/- 132
Walk 0 +/- 132
Other means 15 +/- 25
Worked at home 30 +/- 34

Table 8. Percent of Block Groups with Margins of Error Greater Than Cell Values for Travel Time to Work

Travel Time to Work Pct MOE
GT Cell Value
Cell Value = 0
Total 1.7 0.8
LT 5 minutes 80.7 39.2
5 to 9 minutes 44.7 12.2
10 to 14 minutes 32.0 6.9
15 to 19 minutes 29.8 6.0
20 to 24 minutes 33.4 7.5
25 to 29 minutes 65.7 24.4
30 to 34 minutes 39.4 9.7
35 to 39 minutes 85.3 46.5
40 to 44 minutes 80.5 40.4
45 to 59 minutes 61.2 22.7
60 to 89 minutes 72.3 31.8
90 or more minutes 88.4 49.3

Table 9. Means of Transportation to Work for Block Group 0626.14 2 in Orange County, California. ACS 2005-2009 and 2000 Census

Means of Transportation Workers MOE 2000 Census
Total Workers 4,289 +/- 1,286 4,121
Drive alone 1,138 +/- 307 1,456
Carpool 198 +/- 153 180
Bus or trolley bus 22 +/- 26 16
Streetcar 0 +/- 132 0
Subway 0 +/- 132 0
Railroad 28 +/- 32 0
Ferryboat 0 +/- 132 0
Taxicab 0 +/- 132 0
Motorcycle 15 +/- 24 18
Bicycle 534 +/- 321 506
Walk 2,169 +/- 891 1,815
Other means 0 +/- 132 0
Worked at home 185 +/- 213 130

Table 10. Block Groups with Workers but Zero in the Travel Time Cell

Travel Time to Work 2005-2009
Census 2000 SF3 ACS Increase
LT 5 minutes 80,253 53,328 26,925
5 to 9 minutes 23,839 10,387 13,452
10 to 14 minutes 12,766 4,458 8,308
15 to 19 minutes 11,033 5,352 5,681
20 to 24 minutes 14,151 4,995 9,156
25 to 29 minutes 49,332 26,079 23,253
30 to 34 minutes 18,696 6,610 12,086
35 to 39 minutes 95,559 66,290 29,269
40 to 44 minutes 82,696 56,001 26,695
45 to 59 minutes 45,850 23,259 22,591
60 to 89 minutes 64,843 35,692 29,151
90 or more minutes 101,444 57,749 43,695

Table 11. Means of Transportation to Work for Block Group 0016.04 1 in Larimer County, Colorado

Means of Transportation ACS MOE 2000 Census
Total Workers 1,235 1,463 139
Drive alone 387 207 119
Carpool 218 324 6
Bus or trolley bus 0 123 0
Streetcar 0 123 0
Subway 0 123 0
Railroad 0 123 0
Ferryboat 0 123 0
Taxicab 0 123 0
Motorcycle 0 123 0
Bicycle 618 967 0
Walk 0 123 0
Other means 0 123 0
Worked at home 12 19 14

Table 12. Mean Index of Dissimilarity for Block Groups - 2005-2009 ACS vs. 2000 Census. Means of Transportation and Travel Time

Block Group Type Means of Transportation Travel Time
All Block Groups 15.1 30.7
Pop change less than 5 pct 14.2 29.7
100+ ACS Interviews 8.2 18.3
In county with Pop 500,000+ 17.2 32.2

Table 13. Means of Transportation to Work for Block Group 0041.05 2 in Denver Colorado: 2005-2009 ACS and 2000 Census

Means of Transportation 2000 Census ACS MOE
Total Workers 0 4,177 +/- 237
Drive alone 0 3,221 +/- 221
Carpool 0 207 +/- 84
Bus or trolley bus 0 94 +/- 68
Streetcar 0 0 +/- 123
Subway 0 18 +/- 24
Railroad 0 0 +/- 123
Ferryboat 0 0 +/- 123
Taxicab 0 0 +/- 123
Motorcycle 0 0 +/- 123
Bicycle 0 50 +/- 34
Walk 0 68 +/- 49
Other means 0 43 +/- 34
Worked at home 0 476 +/- 124

CTPP Hotline - 202/366-5000
CTPP Listserv:
CTPP Website:
FHWA Website for Census issues:
2005-2007 ACS Profiles:
AASHTO Website for CTPP:
1990 and 2000 CTPP downloadable via Transtats:
TRB Subcommittee on census data:

Penelope Weinberger
PH: 202/624-3556

Jennifer Toth, AZDOT
Chair, CTPP Oversight Board
PH: 602-712-8143

Susan Gorski, MI DOT
Vice Chair, CTPP Oversight Board
PH: 651/366-3854

Census Bureau: Housing and Household Economic Statistics Division
Alison Fields
PH: 301/763-2456

Brian McKenzie
PH: 301/763-6532

Ken Cervenka
PH: 202/493-0512

Li Leung
PH: 202/366-0634

Elaine Murakami
PH: 206/220-4460

Ed Christopher
PH: 708/283-3534

TRB Committees
Catherine Lawson
Urban Data Committee Chair
PH: 518/442-4773

Clara Reschovsky
Census Subcommittee Co-Chair
PH: 202/962-3332

Kristen Rohanna
Census Subcommittee Co-Chair
PH: 619/699-6918

CTPP Listserv

The CTPP Listserv serves as a web-forum for posting questions, and sharing information on Census and ACS. Currently, over 700 users are subscribed to the listserv. To subscribe, please register by completing a form posted at: On the form, you can indicate if you want e-mails to be batched in a daily digest. The web site also includes an archive of past e-mails posted to the listserv.

Updated: 1/31/2017
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