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Developments (TODs). Prepared for Transportation Northwest (TransNow), Seattle, WA (May,
2006b).
Baltimore Region TOD and Smart Growth Analysis
Situation. Like most U.S. metropolitan areas, particularly those in the industrial Northeast, the
Baltimore region has undergone major transformation over the past 30 years. An industrial econ-
omy has given way to one more focused on technology, and population and jobs have steadily dis-
persed from the urban core to the surrounding suburbs and one-time rural areas. Between 1980
and 2000, Baltimore City lost more than 135,000 people and 43,000 jobs, despite the fact that the
overall region grew by 341,000 people and 393,000 jobs. The effects of these shifts--from places of
urban character (moderate to high density, mix of uses, and accessible by transit or walking) to
outlying areas (typically automobile-oriented land use)--on transportation system demand,
mobility, and air quality have been substantial. While population has grown by only 16 percent
during this period, vehicle miles of travel (VMT) has grown by 73.5 percent, and transit share for
commuting has dropped from 10 to 6.3 percent. As a result, congestion has been steadily increas-
ing. Currently, the region is served by two rail transit routes that do not constitute a regional sys-
tem, and as a result leave holes in coverage and connectivity. The region continues to be designated
a "severe" ozone non-attainment area. A substantial number of households remain in poverty,
lacking appropriate skills for and access to employment.
Action. Several strategies to deal with the economic development, poverty, mobility, and envi-
ronmental issues are being attempted. Chief among these strategies are (1) a major new investment
in rail transit and (2) stimulation of TOD around new and existing rail stations. In March 2002, a
plan was adopted that lays out a system of rail transit routes to provide high quality transit acces-
sibility to the entire region. One of the proposed lines, the "Red Line," runs through some of the
city's most distressed neighborhoods on the west side. Opening the line would link the two exist-
ing rail transit facilities into a regional system.
The Baltimore Metropolitan Council (BMC) has taken steps to update and enhance its regional
travel models to evaluate TOD scenarios that focus future growth around the proposed integrated
transit network. BMC has conducted research on the transportation/land use connection which it
hopes to incorporate in its forecasting for the Red Line as well as for related evaluations of alter-
native land use concepts.
Analysis. A primary goal of the Red Line project is to stimulate TOD around the proposed stations
and have an effect not only on future ridership but also on community and economic development.
For Baltimore, a successful strategy would hope to attract new households and jobs to the transit-
served areas, which ideally would be households or jobs that might otherwise locate in the outer
suburbs and contribute disproportionately to highway demand, congestion and air pollution.
Previously, BMC was accounting for the effects of land use in its trip generation model by
using a stratification based on residential density to differentiate trip generation rates among
four settings: CBD, inner city, suburban, and rural. Household trip production rates (by
household income and vehicle ownership) were developed for six different trip purposes:
home-based work, home-based school, home-based shop, home-based other, non-home-
based, and work-based other. Separate trip generation rates (tables) were developed for walk
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trips. The 2001 regional household travel survey permitted analyses to be conducted to determine
whether the existing process could be enhanced to provide a higher level of discernment among
different land use contexts than simply using the four density-related area-type codes.
Results. Initial analyses performed at an aggregate level on a selection of neighborhoods affirmed
the apparent importance of density in explaining travel differences. A sub-sample of about 1,200
households from the 3,500-household 2001 travel survey was selected in a manner to define 32 dis-
tinct "places" in the Baltimore region. Each place was subsequently analyzed as a distinct data
point when comparing travel, demographic, and land use characteristics. Comparing the places
based solely on residential density and daily vehicle miles of travel (DVMT) per capita (a primary
measure of auto dependency) a fairly strong logarithmic relationship was indicated, as evidenced
by an R-Squared of 0.727. The relationship is displayed in Figure 17-6.
Figure 17-6 Daily per capita VMT by residential density
While the displayed relationship was striking, BMC researchers recognized that residential den-
sity alone was probably not the full story behind this relationship. First, factors like affluence
(number of wage earners, income, and vehicle ownership) tend to follow spatial patterns, with
more affluent households generally choosing to reside in lower density suburbs. Second, residen-
tial density can often be a mask for other important land use characteristics, namely the mix and
balance of different uses, the "design" by which these uses are connected in a pedestrian/transit
friendly setting, and the degree of access to regional opportunities enabled by both regional loca-
tion and transit and highway connectivity.
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To understand the importance of these various influences, a disaggregate analysis was undertaken
in which household travel activity was studied in simultaneous relationship to demographic char-
acteristics, regional accessibility, and local land use. Several multiple regression models were
developed to ascertain the statistical importance and relative contribution of these various factors
to household travel, including a model of household DVMT and a model of household vehicle
ownership. Table 17-55 presents these models. The regression analysis showed that the attributes
of location--both regional accessibility and local land use context--have a profound bearing on
household travel behavior.
A special challenge was representing local land use characteristics. These characteristics are sufficiently
fine-grained that they cannot be represented with conventional TAZ-level information. Instead, GIS
procedures were developed to analyze parcel-level data and calculate a variety of measures reflecting
mix, balance, dispersion, and walkability within 1/2 mile of the household. Measures of entropy
(land use mix) and dissimilarity (balance) were developed using methods pioneered by Cervero
and Kockelman. However, rather than use a subjective pedestrian/environmental friendliness-
type measure to reflect selected land use characteristics, BMC created a measure of "local opportu-
nities." This variable incorporated information on the classification and proximity of commercial
activities within 1/2 mile of actual distance from the individual household as measured along the
street network. Such "opportunities" would likely be greater for households in a TOD as compared
to a non-TOD setting.
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Table 17-55 BMC Multiple Regression Models for Predicting Daily Household (HH)
DVMT and Vehicle Ownership
HH DVMT HH Vehicles
Model Model
Variable Coefficients Coefficients
Household Characteristics
HH Members: Number of members in household. 2.33 0.535
HH Members Under Age 18: Number of members in
-- -0.531
household younger than 18 years of age.
HH Workers: Number of workers in household. 8.36 --
HH Vehicles: Number of vehicles owned by household. 8.38 --
HH Income: Annual income, coded in $5,000 increments. 1.38 0.069
Regional Accessibility
Regional Jobs Accessibility: For each mode (auto and transit),
calculate the number of jobs reachable, divided by the travel
time to reach the opportunity (as determined by the regional -1.19E-04 -6.3E-06
gravity model). Sum figure for auto and transit. Coefficient is
small because this sum is a relatively large number.
Local Land Use
Entropy: Measure of mix of different land uses within
1/2 mile of the household. To calculate, the area in the
1/2-mile buffer is divided into 49 separate hectares (2.5 acre
-6.55 -0.471
grid cells) and one of seven primary land uses is assigned to
each cell based on the dominant land use. Standard entropy
formula is then applied.
Log (Opportunities): Measure uses information on the
location and SIC-code of commercial activities within 1/2 mile
of the household. The shape of the street/road grid is used to
determine proximity. Log transformation is used to moderate -1.84 -0.064
the effect of significant differences between sites with
numerous "opportunities" versus sites with few proximate
opportunities.
Model Constant 12.75 0.634
R-Squared 0.405 0.526
Notes: Each model estimated with 2,707 degrees of freedom.
All coefficients significant at the 99 percent confidence level.
The coefficients in the model of household DVMT production have the expected sign and realistic
magnitudes: household DVMT is predicted to increase with the number of members, number of
workers, number of vehicles, and annual income, and to decrease with higher values of regional
job accessibility, entropy, and local opportunities. In addition to the daily model, similar models
were estimated for home-based work and non-work VMT, with the general difference being that
the model for work travel showed the regional accessibility measure of land use to be important
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to the exclusion of entropy and opportunities, while the non-work model found the local land use
measures--entropy and opportunities--to be very important and regional accessibility less
important.
The household automobile ownership model also reflected the importance of land use factors.
Vehicle ownership is predicted to increase with the number of members and annual income, but
decrease with the number of household members younger than 18. Vehicle ownership is also
predicted to decrease with higher values of regional job accessibility, entropy, and local opportu-
nities. This is an important finding since it says that while income is an important factor, the
regional and local land use characteristics also play significant roles in vehicle ownership
decisions.
Point elasticities for the coefficients in both the household DVMT and household vehicle owner-
ship models are provided in Table 17-56. Point elasticity is calculated as the percent change in the
dependent variable (HH DVMT or HH Vehicles) in response to a one percent change in the given
independent variable (calculated in infinitesimally small increments).27 The results indicate that
the key demographic variables--household size, workers, children, income--are the primary and
most important determinants of DVMT and vehicle ownership, but the land use and accessibility
variables also have a non-trivial impact. Of particular note is the role of the land use variables in
not only influencing DVMT production directly, but also indirectly through vehicle ownership.
More . . . BMC is now in the process of assessing how these findings can be used in ongoing model
update and enhancement activities, particularly in developing ridership and travel forecasts for
the Red Line. BMC is also contemplating using these findings in sketch planning tools to help local
jurisdictions explore alternative land use plans and programs.
27 A negative sign indicates that the response operates in the opposite direction of the change in the variable,
i.e., an increase in the value represented by the dependent variable results in a decrease in the value of the
independent variable. For additional information on elasticity types, calculation, and application, see
"Concept of Elasticity" in Chapter 1, "Introduction," and Appendix A, "Elasticity Discussion and Formulae."
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