Results, Challenges and Conclusions
The resultant averages of the key transportation measures, by region and by urbanicity, provide assurance as to the quality of the regressions employed by comparing the results across the 18 categories. For example, as expected for the mean of person miles traveled (Table 11), the urban person miles are the lowest (as compared to suburban and rural) for each region; the order of mileage for each region is consistently urban lowest, then, suburban, and finally rural with the highest. The Northeast Region has the smallest urban person miles at an average of 44.2 miles. The Pacific Division, not surprising, has the longest urban person miles – at an average of 59.0, but not the longest average of rural (the Pacific Division had a rural average of 65.4). The longest rural person miles are In the East and West South Central Divisions, at an average of 84.2 miles, with a close second in the rural area of the South Atlantic Division, of 83.4 miles.
Since the results for passenger trips are represented by a count variable, the averages are not as dispersed as the person miles results (Table 12). The trips range from 8.4 (for South Atlantic urban) to 9.8 (Pacific urban and Pacific suburban). For the Midwest region, the urban trips are the highest (9.0); but, for the remaining regions, the suburban trips are the greatest. (For the Pacific Division, the suburban and urban numbers of trips are the same.)
Looking next to the results for the vehicle miles (Table 13), the lowest average mileage of 26.1 is again for the Northeast urban; the highest, 57.4 miles, is for the South Atlantic rural. For all but the Pacific Division, the average urban mileage is less than the suburban, which is, in turn, less than the rural mile averages. For the Pacific Division, the suburban vehicle mile average is greater than the rural.
Lastly, the vehicle trip estimates (Table 14) differ slightly from the person trips. The lowest average number of trips is for the urban area in the Northeast Region (4.0), whereas the highest vehicle trips average is for the Mountain rural and Pacific suburban (6.1). In all cases, urban areas have the lowest average number of vehicle trips, but in most regions/divisions, the number of suburban vehicle trips is higher than the number of rural trips. Only two rural areas were higher than the suburban areas, the Northeast Region and the Mountain Division.
Estimation by Census tract
Employing the results from the regression models, the next stage of research was undertaken – providing the estimates of PT, PMT, VT and VMT for each census tract. These estimates are made by transferring census tract information from the ACS (with the exception of Manhattan and the tracts suppressed for reasons above). Maps of the four travel variables are provided below in Figures 5, 6, 7 and 8:
These estimates, by census tract, are available in state-by-state flat files, as well as in a SAS data file (the format of the SAS file is given in Appendix C). These files can be found on the BTS website: https://www.bts.gov/browse-statistical-products-and-data/statistical-products/surveys/national-household-travel-survey (under NHTS 2009 Transferability Statistics in the Detailed Data section).
Comments on Data
There are a few challenges associated with the data. The accuracy of the Census tract estimates could not be measured directly as there are no Census tract data to compare against the model results. Because the models explained only a limited amount of the variation in PT, PMT, VT, and VMT at the region level, the models are likely to explain even less at smaller geographies where statistical variability is expected to be higher. A limited comparison was made against NHTS data, where a reasonable number of households were sampled in a Census tract. These NHTS estimates proved similar to the estimates made by transferring the ACS data.
The ability to produce sub-national estimates is limited by the NHTS sample design. NHTS data are collected through random digit dialing (RDD) for a national sample and for select ‘add-on’ or oversampled geographic areas. The oversampled geographic areas are the areas where subnational level estimates can be best measured because of the larger sample size. The regions created here to estimate tract level PT, PMT, VT, and VMT include these oversampled geographic areas with areas covered by a much smaller sample. The characteristics of the areas with a smaller sample may be different from the oversampled areas and as such, the estimates of PT, PMT, VT, and VMT may be less accurate in these sparsely sampled areas.
The RDD design itself poses challenges in coverage and nonresponse bias. The response rate for the NHTS is relatively low (approximately 20 percent), which suggests the potential for nonresponse bias. This may be more extensive among demographic groups that are difficult to reach because they are highly mobile.
Finally, there are a few challenges associated with using the ACS data. ACS census tract level data are multi-year estimates. This adds variation to the data when changes occur over time.
Given these data challenges, however, the models still provide useful travel data, by census tract, that can be employed by planners and researchers alike.
It is recognized that transit accessibility and use impacts travel behavior, especially VMT. Future transferability projects could be improved by having GTFS (General Transit Feed Specification) for transit integrated with the project. This would help in getting more reliable results for census tracts in Chicago, San Francisco, Washington D.C, and other areas with higher transit usage.