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ARTICLE
A functional integrated land use-transportation model for analyzing transportation impacts in the Maryland-Washington, DC Region Sabyasachee Mishra, Xin Ye, Fred Ducca, & Gerrit-Jan Knaap National Center for Smart Growth Research and Education, University of Maryland, 1112J Preinkert Fieldhouse, College Park, MD 20742 USA (email: [email protected])
The Maryland-Washington, DC region has been experiencing significant land-use changes and changes in local and regional travel patterns due to increasing growth and sprawl. The region’s highway and transit networks regularly experience severe congestion levels. Before proceeding with plans to build new transportation infrastructure to ad- dress this expanding demand for travel, a critical question is how future land use will affect the regional transportation system. This article investigates how an integrated land-use and transportation model can address this question. A base year and two horizon-year land use-transport scenarios are analyzed. The horizon-year scenarios are: (1) busi- ness as usual (BAU) and (2) high gasoline prices (HGP). The scenarios developed through the land-use model are derived from a three-stage top-down approach: (a) at the state level, (b) at the county level, and (c) at the statewide modeling zone (SMZ) level that reflects economic impacts on the region. The transportation model, the Maryland Statewide Transport Model (MSTM), is an integrated land use-transportation model, capable of reflecting develop- ment and travel patterns in the region. The model includes all of Maryland, Washington, DC, and Delaware, and por- tions of southern Pennsylvania, northern Virginia, New Jersey, and West Virginia. The neighboring states are in- cluded to reflect the entering, exiting, and through trips in the region. The MSTM is a four-step travel-demand model with input provided by the alternative land-use scenarios, designed to produce link-level assignment results for four daily time periods, nineteen trip purposes, and eleven modes of travel. This article presents preliminary results of the land use-transportation model. The long-distance passenger and commodity-travel models are at the development stage and are not included in the results. The analyses of the land use-transport scenarios reveal insights to the re- gion’s travel patterns in terms of the congestion level and the shift of travel as per land-use changes. The model is a useful tool for analyzing future land-use and transportation impacts in the region. KEYWORDS: land use, urban planning, models, traffic management, travel, transportation, economic conditions
Introduction
Traffic congestion in the Maryland-Washington,
DC region causes an estimated loss of US$3 billion
per year because of lost time and traffic delays and
peak-hour traffic volume has increased more than
135% since 1985 (Schrank & Lomax, 2007). Along
with more traffic, new development has spread
farther from central cities, causing increased demand
for transportation services in developing areas and
placing strains on what once were rural road net-
works. Planning agencies need to understand the in-
teractions between these changing land-use patterns
and traffic and to develop strategies that will mitigate
the effects of growth. The Baltimore Metropolitan
Council (BMC) and the Metropolitan Washington
Council of Governments (MWCOG) are the two met-
ropolitan planning organizations (MPOs) in the re-
gion that currently have transportation models. The
travel-demand models of BMC and MWCOG are
well-suited for their respective jurisdictions. How-
ever, there are issues that must be addressed in the
context of a multi-state region. These include: (1) the
interaction of travel on the boundary between the two
MPOs, (2) the modeling of transportation in regions
outside the MPO boundaries such as western Mary-
land or the eastern shore of the Chesapeake Bay, and
(3) the estimation of the impact of travel that passes
through the multistate area, particularly freight travel.
The MPO models can partially address these issues
(or in some cases not address them at all), but to fully
reconcile them requires a broader view supported by
multistate analytic procedures.
The boundaries of the two MPOs are presented
in Figure 1. The individual MPO regions only cover
portions of Maryland and Virginia. The two major
cities within the region are Baltimore and Washing-
ton, DC. The two beltways and all freeways in the
region are shown in Figure 2.
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Figure 2 Major interstate highways in the Maryland-
Washington, DC Region.
The transportation impact on Baltimore is sensi-
tive to policy/travel changes in the Washington, DC
region. The effect on a regional scale, such as the
sensitivity of travel between the Baltimore and
Washington, DC areas, can only be explored by a
regional or statewide model. In addition, such models
can be used to assess impacts on sustainability by
measuring sprawl, congestion, and greenhouse-gas
(GHG) emissions. The remainder of the article is
structured as follows. The following section presents
a literature review on national statewide modeling
practices, followed by the scenario-development
steps and regional model-development methodology
proposed for this paper. The next section describes
the integrated land use-transportation model. Data re-
quirements are then presented followed by the re-
sults. Finally, we discuss our conclusions and future
scope of the work.
Literature Review
Statewide travel demand and forecasting models
address significant planning needs by estimating, for
a future date, the number of vehicles that use major
transportation facilities in a state. Statewide models
can forecast both passenger and freight flows, and
include a variety of modes including highways, urban
transit systems, intercity passenger services, airports,
seaports, and railroads. The earliest experiments in
statewide travel forecasting during the 1970s adapted
methods that had been developed specifically for ur-
ban travel forecasting, but those early statewide mod-
eling efforts were not elegantly designed to reflect
realistic land-use development and travel patterns
because of difficulties in adequately covering large
geographic areas in sufficient detail. During the past
ten years, state-transportation planners have seen
dramatic improvements in socioeconomic and net-
work databases, tools for accessing these databases,
and computational power (NCHRP, 2006).
The most mature statewide passenger-travel
models used in the United States are from Ohio
(Parsons Brinckerhoff, 2010), Michigan (MDOT,
2006), Oregon (PBQ&D, 1995), and Indiana (BL&A,
2004). These models have undergone considerable
refinement over the years and share many similari-
ties. Michigan, in particular, has exhaustively docu-
mented each step and each assumption made, so it is
possible to use this model as an indicator of the “state
of the practice.” Other states with existing models
include Connecticut (ConnDOT, 1997), California
(Caltrans, 2010), Florida (Bejleri et al. 2008), Ken-
tucky (Wilbur Smith Associates, 1997), and Vermont
(Weeks, 2010). A number of other states have models
in various stages of development (NCHRP, 2006).
While several states use transportation models,
very few have implemented integrated land use-
transportation models into practice. Most notably, the
California Department of Transportation (Caltrans) is
exploring the feasibility and benefits of the potential
implementation of a statewide integrated land use/
economic/transportation model. Caltrans aims to test
the model to assess and depict the interregional ef-
fects of land use, economics, and transportation on
energy, the economy, and the environment.
While every state uses its own methodology to
reflect travel behavior, the Maryland-Washington,
DC region is unique, with significant daily work trips
Figure 1 Baltimore-Washington, DC Region and surrounding area.
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from neighboring states. The MPOs have transporta-
tion models that are better suited to their individual
areas. The lack of a single comprehensive statewide
model provides an opportunity to develop a func-
tional integrated land-use transportation model to
reflect current and future travel behavior in the
Baltimore-Washington, DC region. Collecting land-
use data, transportation-network data (highway, tran-
sit (long and short distance), and feeder services), and
special generators poses a challenge in developing a
comprehensive travel-demand model. In addition,
travel behavior in rural areas (western Maryland and
the Eastern Shore) is a unique feature in this model.
The objective of the research is to develop an inte-
grated land use-transportation model and analyze the
travel impacts in the Maryland-Washington, DC re-
gion and the immediate surrounding area by con-
structing land-use scenarios depicting future growth.
Scenario Development and Methodology
A modeling process to assess the region’s future
growth can be formulated in three steps: (1) con-
struction of land-use scenarios; (2) development of a
regional travel-demand model; (3) development and
application of a functional regional integrated land
use-transport interaction model covering the entire
region.
Land-Use Scenarios The National Center for Smart Growth Research
and Education (NCSGRE) at the University of Mary-
land has been actively involved in the analysis of
land-use patterns in the state for close to a decade.
One of the activities of NCSGRE is to explore alter-
native futures for the state of Maryland and to iden-
tify what policies should be adopted today to max-
imize the likelihood of more desirable future out-
comes. The land-use scenarios are based on a three-
layer system, as presented in Figure 3. The three
stages are: (a) national level, (b) regional level, and
(c) local level.
National econometric model: 1 The national eco-
nometric model consists of two submodels: (1) The
Long-term Interindustry Forecasting Tool (LIFT), a
macroeconomic input-output model operating at the
national economy level, forecasts more than 800
macroeconomic variables that are then fed into (2)
the State Employment Modeling System (STEMS) to
calculate employment and earnings by industry for all
50 states and the District of Columbia. Output from
1 Econometrics is a tool that can be deployed to model land-use
characteristics. A set of discrete choice models is used to model national-level population, household, and employment.
LIFT serves as input to STEMS. Results from the
STEMS model are then allocated by region (political
boundaries are imprecise predictors of demarcations
for labor markets and economic regions) using cur-
rent proportions of state-level forecasts for each sec-
tor. A detailed description of LIFT and STEMS can
be found in the literature (McCarthy, 1991; Inforum,
2010).
Regional Model: The regional model depicts land-use variables at the county level. At the regional
level, the forecasting approach is based on near-total
reliance on empirically calibrated relationships. The
calibrated model involves 40 equations using pro-
gressively more inclusive sets of predictors. The allo-
cation model incorporates review of the benchmark
forecasts (Hammer, 2007).
Local Model: The local model results in land-use outputs at the statewide modeling zones (SMZ)
level. 2 The initial allocations are made based on
transportation costs and the basic employment distri-
bution. At the local level, a Lowry model-based allo-
cation is used to assign household and employment
by five income categories from the counties to the
SMZs.
From the perspective of development patterns,
two broad future scenarios are discussed in this ar-
ticle:
Business-As-Usual (BAU)
High Gasoline Price (HGP)
The BAU scenario is generated by introducing
the path of real oil prices and the Long-Range Trans-
portation Plan (LRTP), the proposed strategic im-
provement program for the transportation system. In
2 SMZs are polygon structures used in the statewide model and can
be considered similar to Traffic Analysis Zones (TAZs) in trans-
portation planning. The SMZs in the statewide model are equiva-
lent to TAZs in high-density development areas, or TAZs are nested under SMZs in low-density development areas.
Figure 3 Multilayer land-use model.
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the high gasoline-price scenario, four key parameters
are considered: (1) increase in crude oil price, (2)
increase in agricultural commodity prices, (3) in-
crease in federal defense spending, and (4) increase
in employment in professional service. These factors
were selected by a scenario-advisory committee with
the rationale of identifying exogenous trends that
would provide clustered urban development, more
jobs and housing close to transit stations, less devel-
opment on green infrastructure, fewer new imper-
vious surfaces, and fewer vehicle miles traveled
(VMT) without any change in government policy.
The path of higher oil prices is presented in Figure 4.
The three trend lines represent: BAU, annual energy
outlook (data from United States Energy Information
Administration), and the HGP scenario (data input
into LIFT). Similar graphs for other agriculture
commodities, federal defense spending, and employ-
ment in professional service are considered in the
HGP scenario. The changes in the key parameters
(including higher gasoline price) in the land-use
model result in different patterns of employment by
industry sector and spatial distribution of households.
The top-down land-use model is used to allocate em-
ployment and households from state to counties to
SMZs. The HGP scenario results in clustered urban
development as opposed to sprawl.
Development of a Regional Travel-Demand
Model The regional travel-demand model, titled the
Maryland Statewide Transportation Model (MSTM),
is designed as a multilayer model working at na-
tional, regional, and local levels. The study area cov-
ers all of Maryland, Delaware, and Washington, DC,
along with portions of New Jersey, Pennsylvania,
Virginia, and West Virginia (with 64 counties in the
region).
The MSTM model consists of 1,607 SMZs and
132 regional modeling zones (RMZs). 3 The 132
RMZs cover the complete United States, Canada, and
Mexico. Maps of SMZs and RMZs are presented in
Figures 5(a) and 5(b) respectively. A four-step travel-
demand model is developed to forecast passenger-
travel demand between origin-destination (OD) pairs
by various travel modes and time-of-day periods. The
next section discusses details of the transportation
model.
Integrated Land Use-Transportation Model The integrated land use-transportation model is
presented in Figure 6. As previously discussed, the
land-use model consists of three stages: (a) an eco-
nometric model at the state level; (b) a regional
3 Regional Modeling Zones (RMZs) are larger polygon structures
used in the statewide model to incorporate the source of long
distance, visitor, and external travel. The RMZs are much larger in
size compared to SMZs, as SMZs are used to incorporate the source of intrazonal trips.
Figure 4 Crude oil price path.
Figure 5(a) Regional Modeling Zones in MSTM.
Figure 5(b) Statewide Modeling Zones in MSTM.
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Figure 6 Integrated landuse-transportation model.
model at the county level; and (c) an econometric
model at the SMZ level. The transportation model
contains the following steps (NCSGRE, 2009):
Trip generation is a cross-classified model for production and attraction of nineteen types of trips
(home-based work, home-based shopping, and home-
based other trip purposes interact with five travelers’
income levels (fifteen trip purposes); home-based
school, journey to work, journey at work, and
nonhome-based other). 4
Trip distribution is a gravity model for distribut- ing nineteen types of trips into OD trip matrices.
5
Mode choice is a nested logit model for splitting OD trip matrices into eleven travel modes (three au-
tomobile modes and eight transit modes). 6 The three
automobile modes refer to single-occupant vehicles
(SOV), high-occupant vehicles with two occupants
(HOV-2), and high-occupant vehicles with three or
more occupants (HOV-3+).
4 The trip-generation step determines the number of trips produced
and attracted to the SMZ. 5 The trip-distribution step determines the origins and destinations
of trips between SMZs. 6 The mode choice computes the proportion of trips between each
origin and destination that use a particular transportation mode.
Time-of-day allocation is a model for splitting daily travel demand into demand over four daily time
periods (AM peak, midday, PM peak, and night).
Traffic assignment is based on a user-equilibrium method of assigning trips to the links by minimizing
travel time. 7
We are currently completing the development
and integration of freight demand and long-distance
travel components into MSTM. However, these com-
ponents were not completed at the time this article
was written.
Data
Data for MSTM are derived from a number of
national, state, and local agencies. The socioeco-
nomic data for the MPO region in Maryland and
Washington, DC are collected from the cooperative-
forecast data from BMC and MWCOG. The non-
MPO region socioeconomic data in Maryland is de-
rived from the Census Bureau’s Census Transporta-
tion Planning Package (CTPP) and the Quarterly
7 Traffic assignment allocates trips between an origin and destina-
tion by a particular mode to a route. Further, a route consists of a set of links in the transportation network.
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Census of Employment and Wages (QCEW). 8 The
land-use data for outside the Maryland-Washington,
DC region are acquired from several sources includ-
ing the Departments of Transportation in Virginia,
Pennsylvania, and Delaware. The socioeconomic data
are classified in households by number of workers,
persons per household, and household by income.
Five income categories are considered (less than
US$20,000, US$20,000–40,000, US$40,000–60,000,
US$60,000–100,000, and more than US$100,000).
Four types of employment are considered: retail, of-
fice, industrial, and other. The base year (2000) so-
cioeconomic data are collected from the aforemen-
tioned agencies. 9 The horizon year (2030) socioeco-
nomic data are obtained by the three-stage land use-
model approach. The transportation network is built
on a regional scale after combining the portions of
the networks received from various agencies.
The base-year network consists of more than
167,000 links, and contains sixteen functional classi-
fications including all highway, transit, walk access,
and transfer links. For external travel all the freeways
are included outside the modeling region. The toll
roads and HOV lanes are coded in the network with
the current user charges. The network also contains
all transit facilities in the region including metro rail,
light rail transit (LRT), bus, and commuter rail (both
regional and Amtrak). Proper connection is estab-
lished between highway and transit in the form of
park-and-ride, access, and transfer links.
Results
The results include a base case and two sce-
narios; a BAU scenario and a HGP scenario are pre-
sented in the following section.
Tables 1, 2, and 3 represent the trip flows among
each of the states and the District of Columbia. Mary-
land, Delaware, and the District of Columbia are
represented in their entirety while Pennsylvania, New
Jersey, Virginia, and West Virginia are partially
represented (see Figure 1). The trips represent home-
based work, home-based shopping, home-based
other, home-based school, and nonhome based (jour-
ney to work, journey at work, and other nonhome
based). The freight and long-distance passenger com-
ponents were not completed at the time this article
was prepared and were not used in these scenario
tests.
8 The QCEW data are collected on a quarterly basis from the
Maryland Department of Labor and Licensing Regulations
(DLLR). 9 The base year for the transportation model is 2000, confirming to
the last census year. For calibration and validation purposes an
intermediate year, 2007, was considered; however, the result for 2007 is not presented for brevity.
Origin and Destination of Travel Table 1 presents the OD flows within and be-
tween states for the year 2000 in the number of trips
per day. For this year, over 16.25 million (last col-
umn of Table 1) trip movements occurred in Mary-
land on an average day. Approximately 15.02 million
trips originated and ended within Maryland. Simi-
larly, for Washington, DC, over 1.80 million vehicu-
lar trips occurred on an average week day. Of these
journeys, 1.20 million trips originated and ended in
Washington, DC. For Delaware, over 2.39 million
trip movements occurred on an average weekday, of
which 2.15 million trips originated and ended within
Delaware. The “other” column represents movements
from Maryland, Washington, DC, and Delaware, to
and from the neighboring states. The state-level OD
matrix presents a measure of trip movement within
and between states. The OD matrix is critical to the
ultimate choice of link or route of travel. For the year
2000, a total of 36.59 million trips per day occurred
in the MSTM. Very few trips are made between
Washington, DC and Delaware in Table 1. The long-
distance passenger-component results of MSTM are
not presented here.
The OD matrix for 2030 BAU is presented in
Table 2. For Maryland, total trip movements are
20.62 million (last column, second row of Table 2),
compared to 16.25 million for the year 2000 (last
column, second row of Table 1). For Washington,
DC, total trips are 2.65 million (last column, third
row of Table 2), compared to 1.80 million for the
year 2000 (last column, third row of Table 1). A sim-
ilar increasing trend is observed for Delaware and the
neighboring states. The total trips in the region for
2030 BAU are 45.57 million.
Table 3 presents the 2030 HGP scenario OD
matrix. The HGP scenario suggests that there is less
travel when compared to the 2030 BAU. It is ex-
pected that with a high gasoline price fewer trips are
made, with most development near the workplaces or
the central business district of the corresponding re-
gions. For example, in Maryland, 18.93 million trips
are made per day (last column, second row of Table
3) compared to 20.62 million in 2030 BAU (last col-
umn, second row of Table 2), and 16.25 million (last
column, second row of Table 1) in 2000. Similarly,
fewer trips per day are observed in the 2030 HGP
scenario when compared to the 2030 BAU scenario.
Finally, note that under the 2030 BAU scenario
there are approximately 3.5 million more trips than
under the 2030 HGP scenario (45,159,547 versus
41,628,927). 10
With higher gas prices, travelers
10
For the BAU scenario there are 97.69% automobile and 2.31%
transit trips. For the HGP scenario there are 95.79% automobile
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change mode to transit or walk, accounting for some
of the difference. In addition, trips become shorter.
Very short trips are not represented in the highway
network, accounting for the remainder of the differ-
ences.
Critical Link Analysis Three critical locations (corridors) are considered
in the study area for demonstration of traffic volume
for the base year and two horizon-year scenarios.
Figure 7 presents traffic volume for the Capital Belt-
way, the Baltimore Beltway, and the section of Inter-
state 95 connecting the two beltways. For the year
2000, both the Capital Beltway and Interstate 95 car-
ried 90,000 vehicles per day (including cars and
and 4.21% transit trips. More transit trips are observed in the HGP
scenario.
trucks), while the Baltimore Beltway carried 68,000
vehicles per day. Traffic volume for the three critical
link groups in the 2030 BAU scenario is higher than
the 2030 HGP scenario. The lower traffic volume for
the 2030 HGP scenario is the result of less travel un-
der the higher gasoline-price scenario. Similar link-
level traffic volume for other major and minor streets
can be obtained in MSTM.
Statewide Transportation Impacts The statewide transportation-impact results are
presented with three measures of effectiveness
(MOE): (1) vehicle hours of travel (VHT), (2) vehicle
miles traveled (VMT), and (3) vehicles hours of de-
lay (VHD).
Table 1 OD travel pattern between and within states–2000.
MD DC DE Other** Total
MD 15,023,803 671,239 89,377 472,185 16,256,604
DC 377,266 1,200,544 * 224,511 1,802,473
DE 127,110 * 2,150,974 120,132 2,398,494
Other** 847,650 580,215 312,911 14,401,642 16,142,418
Total 16,375,829 2,452,276 2,553,414 15,218,470 36,599,989
Table 2 OD travel pattern between and within states–2030 BAU.
MD DC DE Other** Total
MD 18,743,367 904,481 149,920 823,045 20,620,813
DC 426,908 1,998,758 * 233,318 2,659,212
DE 136,217 * 2,812,907 151,809 3,101,325
Other** 950,800 645,409 370,020 16,811,968 18,778,197
Total 20,257,292 3,548,940 3,333,075 18,020,140 45,159,547
Table 3 OD travel pattern between and within states–2030 HGP.
MD DC DE Other** Total
MD 17,216,747 862,821 130,443 729,355 18,939,366
DC 435,166 1,583,163 * 222,863 2,241,382
DE 142,885 * 2,498,738 203,458 2,845,375
Other** 894,510 629,077 346,996 15,732,222 17,602,805
Total 18,689,308 3,075,855 2,976,367 16,887,898 41,628,927
* There were fewer than 80,000 trips between these regions. These trips are not presented. ** “Other” represents neighboring states such as portions of Virginia, West Virginia,
Pennsylvania, and New Jersey as shown in Figure 1.
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Vehicle Hours of Travel: VHT represents the time
spent by traffic at a system level, which is obtained
by aggregating VHT at the link level. The link-level
VHT is determined by multiplying the traffic volume
and travel time (assigned travel time as opposed to
free-flow travel time). The VHT for the states is pre-
sented in Figure 8. For the base year 2000, VHT for
Maryland is more than 3.5 million hours per day and
for 2030 BAU VHT is over 5 million hours per day.
For the 2030 HGP scenario, the VHT is less (than 5
million hours per day) compared to the 2030 BAU
scenario. Lower VHT for the HGP scenario can be
justified as reduced travel due to higher gasoline
prices. For Washington, DC and Delaware, similar
VHTs are observed in Figure 8. The other group in
Figure 8 represents the portions of Virginia, West
Virginia, Pennsylvania, and New Jersey. The study
region consists of parts of these states; therefore, the
results are not specifically mentioned as state VHTs
in Figure 8, but placed in the category “other.”
Vehicle Miles Traveled: VMT represents the total
number of miles traveled and is computed by mul-
tiplying the traffic volume and the corresponding
distance traveled. From the traffic-assignment results
the link-level VMT is computed first and then aggre-
gated to the state level. Figure 8 presents VMT for
the states in the study region. For Maryland in the
year 2000, VMT is over 120 million miles per day. A
Maryland Department of Transportation (MDOT)
report suggests that the observed annual VMT for the
year 2000 was 50.6 billion miles (MDOT, 2010). The
VMT presented in Figure 9, when converted to an-
nual VMT, is estimated to be 45 billion miles. The
difference of 5 billion annual VMT for Maryland is
attributable to long-distance passenger and commod-
ity travel. For Maryland, the 2030 BAU VMT is 158
million miles per day. The 2030 HGP scenario re-
sulted in less VMT than the 2030 BAU. The HGP
scenario results in fewer and shorter trips because of
higher gasoline prices, thereby reducing the VMT.
Similar results are observed for Washington, DC,
Delaware, and neighboring states (Figure 9).
Vehicle Hours of Delay: VHD is measured by sum-
ming the delay experienced by all the vehicles in a
link. Delay can be defined as the extra time needed
for the vehicle to traverse the length of a link when
compared with the free-flow travel time. Figure 10
presents the VHD for the states in the study region.
The VHD for Maryland in the year 2000 is 0.8 mil-
lion hours per day, and increases to 1.7 million hours
per day in 2030 BAU. The VHD increases at a much
larger rate than VMT. This can be explained by de-
mand increasing at a much higher rate than supply
(transportation-infrastructure development), which
results in more congestion, and higher delay. The
2030 HGP scenario VHD is lower than the 2030
BAU. Similar results are observed for the other states
in the region (Figure 10).
Summary
The transportation impacts for the base year
2000, horizon year 2030 BAU, and horizon year
2030 HGP are presented at the link level and at the
state level. At the link level, three major corridors,
the Capital Beltway, the Baltimore Beltway, and In-
terstate 95 between the two beltways, are selected to
Figure 8 State vehicle hours of travel.
Figure 9 State vehicle miles traveled.
Figure 7 Daily traffic for three major facilities (Note: Interstate 95 runs between the Capital Beltway and the Baltimore Beltway).
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assess traffic-volume impacts. The Capital Beltway
carried higher traffic volume than the other two fa-
cilities for all three years analyzed. Traffic volume
for the three facilities in the 2030 BAU scenario is
higher than the 2030 HGP scenario. Transportation
impacts for the state level are presented with three
measures of effectiveness: VHT, VMT, and VHD. As
expected, the MOEs for the 2030 BAU are always
higher than those for the 2030 HGP. The HGP sce-
nario shifts development closer to city centers (esti-
mated at a 17.34% increase in households). This
change in development patterns combines with lower
total commuting travel due to fewer and shorter trips.
Conclusion
With growing traffic congestion and continued
urban development, it is critical that states have the
capability to analyze the interactive effects of land
use and transportation. The unique contribution of
this research is twofold. First, this work develops an
integrated land use-transportation model with real-
istic scenarios. Second, we apply the integrated
model to determine consistent and defensible esti-
mates of how different patterns of future land use will
result in changes of key measures of transportation
performance. The MSTM by design is a multilayer-
modeling framework at national, regional, and local
levels. Preliminary model results indicate that it can
analyze travel patterns in the base and horizon years
within the state of Maryland and the immediate sur-
rounding area for different land-use scenarios. Two
land-use scenarios, BAU and HGP, are analyzed. The
BAU scenario is generated by introducing the path of
real oil prices and LRTP, the proposed strategic
transportation-improvement program for the trans-
portation system. The HGP scenario is generated by
introducing the path of increased oil prices and fed-
eral defense expenditures to reflect travel behavior in
the region with changes in land use. The MSTM is a
unique tool to analyze land-use and transportation
impacts in the region.
The region-level OD matrix provided the travel
pattern within and between the states. Link-level
analysis demonstrated the traffic volume on selected
critical corridors in the region. Sensitivity tests of the
model respond well to alternative future scenarios,
showing that higher energy prices result in fewer
trips and decreasing VMT and VHT at the statewide
level. These tests have shown that traffic volume in
the Baltimore, Washington, DC, and connecting areas
also declines with higher energy costs. The model is
currently being improved with the addition of inter-
regional trips and freight and long-distance passenger
flow. The MSTM can be used to assess the impact of
major facilities proposed or under construction, in-
cluding the freeway-intercounty connector (ICC),
new commuter rail lines being established by the
Washington Metropolitan Area Transit Agency; ma-
jor highway-rail freight flows, and electronic toll
lanes on Interstate 95. This model provides a criti-
cally needed understanding and analysis of future
land-use and transportation interactions and patterns
in the Maryland-Washington, DC region. In the
broader vision, MSTM can evaluate a number of in-
tegrated land-use and transportation scenarios in-
cluding freight, improved transit, congestion pricing,
and emission estimates in the region, as well as
sprawl. The integrated land use-transportation model
is a useful tool to model travel behavior and to de-
termine transportation sustainability at statewide and
regional scales.
Acknowledgement This article is the outcome of research over the last three
years at NCSGRE. We are thankful to the Maryland State
Highway Administration (MSHA) for research support for
MSTM development. This work would not have been
possible without the constant motivation and help of project
manager Subrat Mahapatra. The authors are grateful to
Patricia Gallivan, the GIS coordinator at NCSGRE, for her
help in database preparation. We would also like to ac-
knowledge many individuals at BMC, MWCOG,
DELDOT, VDOT, and PennDOT for their kind support in
providing socioeconomic, demographic, and network data.
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