Intergovernmental Dynamics of Transportation Planning: Why
Some Towns Attract More Federal Funding Than Others?
Asim Zia (Corresponding Author)
Department of Community Development and Applied Economics
University of Vermont; 146 University Place, Burlington VT 05405
Phone: 802-656-4695; Fax: 802-656-4447; Email: Asim.Zia@uvm.edu
Christopher Koliba
Department of Community Development and Applied Economics
University of Vermont; 146 University Place, Burlington VT 05405
Phone: 802-656-3772; Fax: 802-656-4447; Email:ckoliba@uvm.edu
Matt Tucker
Department of Community Development and Applied Economics
University of Vermont; 146 University Place, Burlington VT 05405
Abstract
This paper explores the dynamics of intergovernmental relations in the distribution of
transportation funds from the viewpoint of local governments. We address the following
questions: (1) Why some towns attract more federal and state funding than other towns? (2) Are
there some balancing and reinforcing feedback loops that influence some towns attracting more
funds than others? We addressed these research q ions through developing a stakehold
informed system dynamic model with an explicit focus on the intergovernmental influence
(exogenous) and local town level technical and financial capacity (endogenous) dynamics. The
model is calibrated to two local towns in Vermont. The model simulates two balancing loops
(BL) and three reinforcing loops (RL). BL1: As a jurisdiction receives more transportation
funds, they are able to meet more of their transportation needs and require fewer funds in the
short term. BL2: With more development, there is less capacity to continue to build, so less
money is allocated for new development. Three RLs include more money received leading
towards more experience and thus greater technical capacity, more technical capacity directing
a jurisdiction to more support from the MPO, and more transportation needs requires more
transportation funds which ultimately gives a jurisdiction more financial capacity.
Key words: Intergovernmental relations, transportation policy, governance networks, system
dynamics modeling, technical capacity, city and regional planning, knowledge management,
intergovernmental management
Introduction
The dynamics of intergovernmental relationships have been extensively studied in the context of
governance and policy networks (Koliba et al. 2010, Sorensen and Torfing 2005, and Provan and
Kenis 2008). Using theories of complex system models of social theories (Miller and Page,
2007) and management (Sterman 2000), system dynamics models have been built for simulating
intergovernmental collaboration (Cresswell et al., 2002), budgeting (Grizzle and Pettitjohn,
2002), and information transfer across governmental agencies (Luna-Reyes et al., 2007).
Cresswell et al. (2002) concluded that dynamic modeling of collaboration and knowledge
sharing in intergovernmental networks is worthwhile in shedding light on these processes. Luna-
Reyes et al. (2007) seconded this sentiment and added that modeling and simulation is a useful
tool for theory-building. In transportation planning, simulation models are widely used to
predict travel and understand the dynamics between transportation and land use (Waddell 2002),
but we are not aware of any system dynamic model that simulates intergovernmental dynamics
of transportation planning. The purpose of this paper is to explore systems dynamics as method
of explaining intergovernmental relations and to model the funding distribution system in
Chittenden County, Vermont to describe the inner workings of the system, identify leverage
points and explore potential areas for change.
Intergovernmental relationships can influence the distribution of financial capital for
transportation projects (Rich, 1989). Local governments must cooperate with their regional
government to receive a higher priority ranking over projects in other jurisdictions. The regional
metropolitan planning organizations (MPOs), Regional Planning Commissions (RPCs) and State
Departments of Transportation (SDOTs) face a similar competitive structure in that MPOs vie
against other MPOs for funding from the state and states compete with other states for federal
funding. With multiple levels of government involvement, the capacity of governments to
coordinate across these levels of government can influence how the funds are distributed.
Throughout the past few decades, the federal transportation bills in the US incrementally
changed the relationships between the levels of government for transportation planning and
strengthened the role of regional government. Transportation planning involves multiple levels
of government in a network that has a multitude of decision making points. Local, regional, state
and federal governments each make choices that affect the planning and distribution process.
The dynamics of intergovernmental relationships that were set in 1950s with highway bills
changed with passing of the Intermodal Surface Transportation Equity Act (ISTEA) in 1991. The
ISTEA reversed the trend of federal control and gave state, local, and regional governments more
decision-making authority (Dilger 2010). This new legal framework changed the dynamics of
intergovernmental relations and mandated more power to MPOs and RPCs who now had greater
responsibilities for managing a diverse set of priorities (Gerber and Gibson 2009). This task,
however, proved to be difficult as MPOs and RPCs still had to manage state priorities and
federal fiscal power. After ISTEA 1991, next significant change in the intergovernmental
dynamics of transportation policy occurred with Safe, Accountable, Flexible, Efficient
Transportation Equity Act: A Legacy for Users (SAFETEA-LU) in 2005, which further
strengthened the role of MPOs and RPCs in transportation planning and program implementation
processes. The strengthening of RPCs through SAFETEA-LU changed the status quo in the
intergovernmental system of transportation planning that had provided overwhelming power to
the state governments vis-a-vis local and regional governments. Since SAFETEA-LU, two
transportation bills have been authorized: Moving Ahead for Progress in the 21st Century Act,
(MAP-21) in 2012 with 2-year funding authorization, and Fixing America’s Surface
Transportation Act (FAST) in 2015 with a 5-year funding authorization. Both MAP-21 and
FAST continue to support the RPC funding mechanisms, while state and local governments
continue to wield significant power over the allocation of surface transportation resources from
the federal government through a complex process of prioritization of transportation projects in
which regional governments were inserted as a mediating player (see Zia and Koliba 2015 for
more details on intergovernmental project prioritization processes).
2
Fiscal relations can influence the power available to regional governments through legal
regulation and financial autonomy (Greer 2006). Legal regulation refers to the procedures used
by federal government to restrict decision-making powers of lower levels of governments, and
financial autonomy is the extent to which a government controls its own revenue. Although U.S.
states and local governments can collect some of their own revenues, they still rely on central
spending power, so Greer cites the American system as an example of low fiscal autonomy
(2006). The United States federal government, like many other countries, now provides more
autonomy to its sub-national governments, but maintains its authority by imposing specific
constraints along with the grants (Joumard and Kongsrud 2003). Gerber and Kollman (2004)
outlined this kind of relationship in terms of “authority migration”. Decisions made by the
central government affected the structure of the financial flows in the system and the states and
federal government continue to compete for their authority. The outcomes of the bargaining
ultimately determine which actors have fiscal autonomy and decision-making power.
The U.S. Constitution leaves the majority of the tax-collecting powers to the federal
government and only Congress and the President can determine how the federal government uses
its spending power. This means the federal government has considerable power in raising
revenue and defining how it is distributed. State and local government representatives can only
influence this process by lobbying Congress (Watts 1999). With their fiscal power, the federal
government employs state and local governments as fiscal administrators of federal programs.
The sub-national governments have substantial authority, but the central government controls
their power by offering mostly conditional grants. The federal government uses these attached
constraints to accomplish three goals: incentivizing states to engage in national priorities,
persuading states to modernize, and helping states take part in redistributive and welfare
programs (Watts 1999).
The different federal transportation bills control the structure of the funding system. The
Intermodal Surface Transportation Equity Act (ISTEA) of 1991 also introduced a fiscal
constraint on the regional Transportation Improvement Program (TIP) and State Transportation
Improvement Program (STIP). This requires MPOs to coordinate with local governments to
generate an aggregate regional budget and identify specific federal funding sources for each
project. Their budget must coincide with the STIP and the available federal funds (Goldman and
Deakin 2000). Although most states favored this addition, local and regional governments were
bound by the available federal programs (and thus federal priorities) and MPOs found this
coordination to be a difficult task (Gage and McDowell, 1995). While some politicians have
attempted to eliminate the federal program “silos” (Dilger 2010), the constraints of these
programs can limit the availability of funds to certain geographic areas. The fiscal constraint
gave MPOs more authority on the prioritization of projects, but only within the boundaries of
federal priorities and budgets, so they also began to seek alternative funding sources (Bishop et
al. 1997). Some regions are considering options for raising and allocating regional or local funds
as a way to increase their financial autonomy (Sciara and Wachs 2007).
Some studies discuss capacity as an important measure of how capable local governments
are of managing the responsibility as it relates to Federal-aid funds (Honadle 2001, Warner
1999). Honadle (2001) described local government capacity as something that is frequently
changing and an important consideration in the context of increasing devolution. Warner (1999)
noted that urban and rural areas have the highest costs and greatest needs in providing services,
but they also have the most difficulty in raising local revenues. The ability to raise funds,
according to Warner (1999), is bounded in part by local well-being and fundraising abilities are
dispersed inequitably across the U.S. Honadle (2001) added that for local governments to
manage greater responsibility after devolution of federal power, it was essential to have
specialized expertise and professionalism in managing intergovernmental relationships. These
notions are apparent in Deil Wright’s concept of Intergovernmental Management (IGM) as
government actors are all working together to achieve a specific goal (Wright 1990). The ability
of a local government to cooperate or coordinate with other governments can determine their
ability to participate in Federal-aid programs. Honadle (2001) highlighted approaches for
effective IGM including focusing on the professionalism of public administrators and functional
cooperation through intergovernmental agreements. If State governments do not factor in the
disparities of local capacity, there will be a reinforcing cycle of an inequitable distribution of
funds, particularly for rural areas (Warner 1999). So, in the face of broader federalist structure of
intergovernmental relations, a compelling question arises from the perspective of local towns:
Why some towns attract more federal and state funding than other towns? Are there some
balancing and reinforcing feedback loops that influence some towns attracting more funds than
others? We addressed these research questions through a participatory system dynamic model
with an explicit focus on the intergovernmental influence (exogenous) and local town level
technical and financial capacity (endogenous) dynamics. The system dynamic model is built in
Stella Version 9.4, and calibrated to two local towns in Vermont.
Methods
We formed the structure of the model according to responses from two focus groups with
transportation professionals in the region and from the literature on the dynamics of federalist
relationships. Using data between 1998 and 2010 from the Chittenden County Metropolitan
Planning Organization’s (CCMPO) Transportation Improvement Program (TIP), we were able to
estimate the amounts requested from each jurisdiction, the amount obligated from the state to the
region, and the amount obligated from the federal government to the region. We retrieved data
on square miles of each town and population from the U.S. Census and collected gas price data
from the Energy Information Administration. To estimate gas demand in Vermont, we used
historical data from the Joint Fiscal Office on gas tax revenues. We also collected data on the
number of technical reports and appearances at transportation advisory council (TAC) meetings
from CCMPO’s website.
We tested the model for two jurisdictions with data on transportation expenses from the
towns’ most recent budgets as an indicator for past expenses. We collected data on the number
of professional planners from their website and assumed their interest in transportation planning
and the cost-benefit to the state.
Causal Loop Diagram
The causal loop diagram (Figure 1) in the distribution system includes two balancing
loops and three reinforcing loops. The first balancing feedback loop is with a jurisdiction’s
transportation needs. As a jurisdiction receives more transportation funds, they are able to meet
more of their transportation needs and require fewer funds in the short term. The second
balancing loop is with environmental capacity. With more development, there is less capacity to
continue to build, so less money is allocated for new development. Three reinforcing loops
4
include more money received leading towards more experience and thus greater technical
capacity, more technical capacity directing a jurisdiction to more support from the MPO, and
more transportation needs requires more transportation funds which ultimately gives a
jurisdiction more financial capacity.
Figure 1. Causal Loop Diagram of the Distribution of Transportation Funds
a :
ra +
transportation needs
-f reg priority rank +
B
| \
7,
R i ——..
@ state prior rank \
~ transp funds alloc
+ t@jutscietion |
Political Support
development i
ss R + f.
- +
Epdoaneniel agenens
+ apacity
J _—* B
jurisdiction's allocation +
for transportation technical capacity
infrastructure capital + R
Pi
{financial capacity a
The Model
The System Dynamics Model was built in Stella. Appendix I provides system of equations and
parametric values and functions specified for the Jericho version of the model shown in Figure 2.
The five main stocks of the model (Figure 2) include federal funds obligated to the region,
obligation amount for local projects, state transportation funds, transportation funds in
jurisdiction, and total transportation expenditures. We added a sixth stock called “depreciated
value” as a way to represent the capital stock value of transportation infrastructure in the
jurisdiction. This value affects the measure of local need in the jurisdiction. To reflect the
restrictions of the federal government, the amounts requested must meet the fiscal restraint
(meaning federal funds are available) for the jurisdiction to receive funds.
Figure 2. System Dynamics Model of the Distribution of Transportation Funds
depreciated value sq mios
oe © pooutaton
OO
dopreciation rate
Apportionment rate
Fed Funds obligated to region
Obligation to Reg
BE) Reo Prost Rank
‘Total Transp Exp
sq miles
chance of
being funded
Other Transp Exe
‘Transp Funds
In jurisdiction
Environmental Capacit
Interest in Transp
(CF envionment attude
Queens
depreciated value
rate of appearances
‘at TAC meetings
number of prof degrees
As discussed in the focus group and in the literature, the local jurisdictions are more
likely to attract transportation funds if they have more technical and financial capacity. Factors
that affect technical capacity include the number of professional degrees on staff, rate of
appearances at TAC meetings, number of technical reports published, and interest in
transportation planning. Political support represents the ability of local jurisdictions to
coordinate with multiple levels of government and any citizen pressure that may alter their plans.
The state priority ranking is a function of cost benefit analysis, political support and regional
priority ranking. This feeds into the variable “chance of being funded” which is a graphical
function from 0 to | that determines the amount of funding that will be dispersed from the
federal and state government and flows into the stock of local obligations.
Environmental capacity and local need are two variables that can balance the amount of
funds received by a jurisdiction. Local governments that have significant developments will be
limited by their environmental capacity of future development, hence this variable limits the
potential of retrieving future funds. This can be in instances where additional growth becomes
costlier or they are unable to meet the standards of the Environmental Impact Assessment. Local
need is a function of population density and the depreciated value of transportation
infrastructure. In the model, a jurisdiction with a greater depreciated value of capital stock has
less transportation needs.
Limitations of the Model
Since the model assumes an initial depreciated value of zero in the first period (1998), the
first three periods in the model do not accurately reflect the system. Not until period three
(2001) does the system normalize after assuming that each jurisdiction has no preliminary
transportation funds. To accommodate this, we analyze the results starting in 2001.
In order to test the model for specific jurisdictions, we assumed data for the cost-benefit
value for the state, the environmental attitude and the jurisdiction’s interest in transportation
planning. An indicator of utility of added mobility or accessibility in each jurisdiction would be
helpful in determining a true value for the cost-benefit analysis, but this data is unavailable.
Analyzing each jurisdiction’s interest in transportation planning could be best analyzed in future
studies with in depth interviews. In order to test the model until 2025, we assumed future data
beyond the twelfth period for gas prices, population, rate or appearances at TAC meetings, and
local budget expenses.
The equation for amounts requested is estimated to be 1.5 times the amount budgeted by
the local government, assuming that local governments ask for more money than was actually
obligated on the TIP. By doing this, we assume that the amounts budgeted from the local
government serve as an indicator of how much federal funds each jurisdiction needs or seeks.
Citizen pressure was assumed for each year and in future iterations, these numbers could be
empirically measured through interviews/surveys or focus groups.
Results
Testing the Model with Two Jurisdictions
By selecting Burlington VT, a city with over 3,500 people per square mile, and Jericho
VT, rural town with a density of about 140 people per square, we compared model results for
two jurisdictions with contrasting demographics. While the obligation amounts in the model for
Burlington were not correct for each year, the total in the model of $37,000,000 is close to the
actual amount of $39,000,000 between 2001 and 2010. The total obligation amounts to the
jurisdiction varied each year, and over time Burlington builds up its technical capacity and
political support (see Figure 3). The state and regional priority ranks cross around the third
period as local need drops, but they follow the same course over time (see Figure 4). This is
because the region ranks their projects according to local need, and the regional ranking accounts
for 20% of the state ranking.
Figure 3. Output from Model for Burlington for Obligation Amounts, Technical Capacity,
local need, and local allocation
® ‘obligation ...rLocal Projects 2: Tech Capacity 3: local need al
10150000%
1.00
1 5075000
2
S 0.504
4 0
3
3 0.00
T T T 1
00 675 13.50 20.25 27.00
Page 1 Years 3:11 PM Mon, Jun 06, 2011
\laee ? Burlington Results
Figure 4. Output from Model for Burlington for State Priority Rank and Regional Priority
Rank
9 +: Reg PriorityRank 2: State Priority Rank
7
0349
2,
@ oa en i WON
4 0.29)
2: 0.32
tr \ _
1:
1 0.26
2 T T T 1
00 6.75 13.50 20.25 27.00
Page 4 Years 3:11PM Mon, Jun 06,2011
at State and Regional Prior Rank
Nee ? 7
Results from Jericho show much lower technical capacity and the obligation amounts in
the model of $818,000 between 2001 and 2010 were a bit higher than the actual amount of
$690,000. Since interest in transportation and cost-benefit analysis were assumed, a sensitivity
analysis could produce results closer to the true values. Jericho’s technical capacity remained
relatively constant over the twenty-seven year period and their transportation needs slowly
declined. This caused the obligation amounts to rise, but only slightly starting in the third period
(see Figure 5).
Figure 5. Jericho Output for Obligation Amounts, Technical Capacity, Local Need, and
Local Allocation
s 1: Obligation ...r Local Projects 2: Tech Capacity 3: local need 4: local alloc
6000004
1.00
a ——
SeNe
300000
0.504
3500
Bene
|
|
|
|
T T 1
.00 6.75 13.50 20.25 27.00
pues Years 11:49 AM Wed, Jun 08, 2011
Naet ? Jericho Output
Sensitivity Analysis
To test the assumed variables, we ran a sensitivity analysis for interest in transportation
and cost-benefit analysis. For interest in transportation, a value below 0.33 may cause the
jurisdiction to have minimal technical capacity and be unable to spend any obligated funds. This
is because their technical capacity is too low to meet the requirement compliance (see Figures 6
and 7). This could produce some unlikely results for this model, but it could be useful if
incorporating annualized data on a local governments’ interest in transportation planning. There
may be certain years in which a jurisdiction did not attempt to receive transportation funds, and
this variable would stop the jurisdiction from receiving funds in that year. As for the different
outputs at each level of interest, there was about a 15% drop in funds received between a level of
1 and a level of .66 and about 15.5% drop between 0.66 and 0.33.
Figure 6. Graphical Output from Sensitivity Analysis of Interest in Transportation at 0,
.33, .66, and 1 and Technical Capacity in Burlington
&® Tech Capacity: 1-2-3-4-
1 14
ee 47 —
a.
Le
er nn ese a ee
et
bi 14 —— 3
en
eee aes
pee
# oF T T T
00 6.75 13.50 20.25
Page 31 Years
Neer ?
27.00
3:16 PM Mon, Jun 06, 2011
Figure 7. Graphical Output from Sensitivity Analysis of Interest in Transportation at 0,
-33, .66, and 1 and Total Transportation Expenditures in Burlington
Ss Total Transp Exp: 1- 2-3-4 -
x
2000000004
ail
1 10000000044
ae
ee
EE
a= °
—
a
1 of T T
00 6.75 13.50
Page 37 Years
Nee ?
10
Figure 8. Graphical Output of Sensitivity Analysis for Cost-Benefit at 0, .25, .5, .75, 1 with
Total Transportation Expenditures in Burlington
9D ota Transp Exp: 1-2-3-4-5-
1
er ae |
ml = ae
aa 3 ez)
Se ae /—2
sf So
A
F eal
00 6.75 13.50 20.25 27.0
Page 29 Years 11:13 AM Mon, Jun 06, 2011
NX 3 BF 2? Sensitivity Analy sis for Cost-Benefit
Environmental attitude, a measure of a jurisdiction’s interest in limiting excessive
development, was assumed as a graphical function over time in the model. In order to test the
sensitivity of this value, we changed it to a constant value and tested it with the values 0, 0.33,
0.66, and 1. The differences are minor in the first thirteen years, but the variance is much greater
after this time period (see Figure 7). With an attitude of 0, there was nearly twice as much
development in period 27 than with an attitude of 0.33, suggesting the results from the model
may be too extreme over time.
Figure 9. Sensitivity Analysis of Environmental Attitude at 0, 0.33, 0.66, and 1 in Relation
to Obligation Amounts for Burlington
&® obigation amt For Local Projects: 1-2-3 -4-
1 20000000
1 10000000-4 Le
4 a
—— ——F 3
4 sr Si a
a
A EA t 1
00 6.75 43.50 20.25 27.00
Page 41 Years 11:14 AM. Wed, Jun 08, 2014
SJ) aaF ? Sensitivity Testing for Environmental Attitude
11
Citizen pressure, which we reflect as a graphical function over time, works as a check on
a jurisdictions’ political support. Without this variable in the model, jurisdictions receive a
slightly higher amount of funds. Changing the amounts allocated from the local jurisdiction does
have an impact on the amount of funds the local government receives.
Discussion
The model results rely on the balance between transportation need and jurisdictional
capacity. An area with greater technical capacity and financial capacity may be able retrieve a
higher level of funds regardless of their transportation needs. Likewise, jurisdictions without
much capacity will receive funds because of their transportation needs. Another variable that
plays a significant role is the amount budgeted which is reflected in the amounts requested. In
this sense, it serves as another indicator of transportation needs and controls the potential
obligation amounts.
This model sheds some light on the rural-urban divide, or lack thereof in transportation
planning. While the Federal Programs attempt to rationalize the distribution of transportation
funds, the state government actually holds significant power in this distribution process. The
ability of jurisdictions with more interest, financial capacity, or technical capacity to retrieve
more funds can explain why some areas receive more transportation funds even when accounting
for population density and VMTs. This competition at the local level untangles the federal rules
of distribution and allows for a process at the regional and state levels. While this does allow for
more local input, it can also foster an attitude of “playing the system,” or at least one that favors
jurisdictions with the capacity to manage across levels of government. As Watts (1999) noted,
the federal government employs the state government as fiscal administrators of transportation
funds, and although there are Federal rules attached with this power, it also provides the state
significant power in awarding funds.
As noted in Cresswell et al. (2002) and Luna-Reyes et al. (2007), simulation modeling
can be useful in theory-building and for insights in intergovernmental relations. The structure of
this model could be used by local towns and regions for insights into the dynamics of the
planning process. Its validity would be strengthened with more complete data from in-depth
interviews with town administrators. Future research could build on this model by adding in all
jurisdictions to fully reflect the limited funds available to the region.
Conclusions
This paper develops a stakeholder driven system dynamics model to explore the
dynamics of intergovernmental relations in the distribution of transportation funds from the
viewpoint of local governments. The system dynamics model can shed light on issues such as
why some towns attract more federal and state funding than other towns. The simulation model
captures the structure of the system by positing five balancing and reinforcing feedback loops
that influence some towns attracting more funds than others. The feedback loops in the
stakeholder informed system dynamic model account for the intergovernmental influence
(exogenous) and local town level technical and financial capacity (endogenous) dynamics. The
model simulates two balancing loops (BL) and three reinforcing loops (RL). BL1: Asa
jurisdiction receives more transportation funds, they are able to meet more of their transportation
12
needs and require fewer funds in the short term. BL2: With more development, there is less
capacity to continue to build, so less money is allocated for new development. Three RLs
include more money received leading towards more experience and thus greater technical
capacity, more technical capacity directing a jurisdiction to more support from the MPO, and
more transportation needs requires more transportation funds which ultimately gives a
jurisdiction more financial capacity. The model was calibrated to two local towns in Vermont,
one urban and the other being rural, and can be extended to other towns in future applications.
The results explain the emergence of rural-urban divide, or lack thereof in transportation
planning. Town governments can leverage financial and technical capacity to attract more
transportation funds; however, changes in environmental attitude and land availability generate
complex dynamics. Information and knowledge management approaches through assessing
technical and financial capacities of town governments and their attitudes towards environment
and development can explain the design and emergence of transportation infrastructure across
urban to rural regions.
Acknowledgements: This research was conducted with funding from US-DOT and NSF-EPS-
1556770. Findings reported in this paper are solely the responsibility of the authors and not the
funding agencies.
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15
APPENDIX A
SYSTEM OF EQUATIONS AND PARAMETRIC VALUES USED FOR CALIBRATING
THE SYSTEM DYNAMICS MODEL SHOWN IN FIGURE 2 TO JERICHO, VT
depreciated_value(t) = depreciated_value(t - dt) + (new_expend - depreciation_rate) * dt
INIT depreciated_value = 0
INFLOWS:
new_expend = project_expenditures
OUTFLOWS:
depreciation_rate = depreciated_value*.142
Fed_Funds_obligated_to_region(t) = Fed_Funds_obligated_to_region(t - dt) + (Apportionment_rate -
fed_alloc) * dt
INIT Fed_Funds_obligated_to_region = 2.598e7
INFLOWS:
Apportionment_rate = GRAPH(TIME)
(0.00, 1e+007), (1.00, 1.2e+007), (2.00, 2.7e+006), (3.00, 2.5e+006), (4.00, 2.7e+007), (5.00, 2.9e+007),
(6.00, 4.2e+007), (7.00, 4.2e+007), (8.00, 5.5e+007), (9.00, 4.7e+007), (10.0, 5e+007), (11.0, 6.3e+007),
(12.0, 7.9e+007), (13.0, 8.2e+007), (14.0, 8.5e+007), (15.0, 8.8e+007), (16.0, 8.9e+007), (17.0, 9¢+007),
(18.0, 9.1e+007), (19.0, 9.2e+007), (20.0, 9.2e+007), (21.0, 9.2e+007), (22.0, 9.3e+007), (23.0, 9.3e+007),
(24.0, 9.3e+007), (25.0, 9.3e+007), (26.0, 9.3e+007), (27.0, 9.6e+007)
OUTFLOWS:
fed_alloc = (Amount_Requested*.8)*chance_of__being_funded
Obligation_Amt_For_Local_Projects(t) = Obligation_Amt_For_Local_Projects(t - dt) + (state_alloc +
fed_alloc + local_alloc - project_expenditures) * dt
INIT Obligation_Amt_For_Local_Projects = 300000
INFLOWS:
state_alloc = chance_of__being_funded*(Amount_Requested)*.15
fed_alloc = (Amount_Requested*.8)*chance_of__being_funded
local_alloc = Amount_Requested*chance_of__being_funded*.05
16
OUTFLOWS:
project_expenditures = if requirement__compliance <1 then 0 else Obligation_Amt_For_Local_Projects
State_Transp_Funds(t) = State_Transp_Funds(t - dt) + (Obligation_to_Reg - state_alloc) * dt
INIT State_Transp_Funds = 0
INFLOWS:
Obligation_to_Reg = Gas_Tax_Revenues/30
OUTFLOWS:
state_alloc = chance_of__being_funded*(Amount_Requested)*.15
Total_Transp_Exp(t) = Total_Transp_Exp(t - dt) + (project_expenditures) * dt
INIT Total_Transp_Exp = 0
INFLOWS:
project_expenditures = if requirement__compliance <1 then 0 else Obligation_Amt_For_Local_Projects
Transp_Funds_in_jurisdiction(t) = Transp_Funds_in_jurisdiction(t - dt) + (Budget_Exp - local_alloc -
Other_Transp_Exp) * dt
INIT Transp_Funds_in_jurisdiction = 0
INFLOWS:
Budget_Exp = GRAPH(TIME*Environmental_Capacity)
(0.00, 940000), (1.00, 1e+006), (2.00, 1e+006), (3.00, 1.1e+006), (4.00, 1.1e+006), (5.00, 1.2e+006),
(6.00, 1.2e+006), (7.00, 1.2e+006), (8.00, 1.3e+006), (9.00, 1.3e+006), (10.0, 1.3e+006), (11.0, 1.4e+006),
(12.0, 1.4e+006), (13.0, 1.4e+006), (14.0, 1.4e+006), (15.0, 1.4e+006), (16.0, 1.5e+006), (17.0, 1.5e+006),
(18.0, 1.6e+006), (19.0, 1.6e+006), (20.0, 1.6e+006), (21.0, 1.6e+006), (22.0, 1.6e+006), (23.0, 1.6e+006),
(24.0, 1.6e+006), (25.0, 1.6e+006), (26.0, 1.6e+006), (27.0, 1.6e+006)
OUTFLOWS:
local_alloc = Amount_Requested*chance_of__being_funded*.05
Other_Transp_Exp = Budget_Exp-local_alloc
Amount_Requested = Budget_Exp*1.95
Cost_benefit = .3
17
Development = Total_Transp_Exp/sq_miles
Fiscal_constraint_met = if Amount_Requested> Fed_Funds_obligated_to_region then 0 else 1
Gas_Tax_Revenues = Gas_Demand*.2
Interest_in_Transp = .4
number_of_prof_degrees = 1
number_of_tech_reports = .92
requirement__compliance = if Tech_Capacity < .2 then 0 else 1
sq_miles = 35.39
chance_of__being_funded = GRAPH(if State_Transp_Funds >0 then State_Priority_Rank else 0)
(0.00, 0.01), (0.1, 0.03), (0.2, 0.045), (0.3, 0.065), (0.4, 0.105), (0.5, 0.175), (0.6, 0.355), (0.7, 0.505), (0.8,
0.63), (0.9, 0.805), (1, 1.00)
citizen_pressure = GRAPH(TIME)
(0.00, 0.485), (1.00, 0.485), (2.00, 0.49), (3.00, 0.515), (4.00, 0.555), (5.00, 0.56), (6.00, 0.525), (7.00,
0.495), (8.00, 0.44), (9.00, 0.43), (10.0, 0.45), (11.0, 0.48), (12.0, 0.475)
Environmental_attitude = GRAPH(TIME)
(0.00, 0.465), (1.00, 0.46), (2.00, 0.46), (3.00, 0.46), (4.00, 0.455), (5.00, 0.455), (6.00, 0.455), (7.00,
0.45), (8.00, 0.43), (9.00, 0.425), (10.0, 0.43), (11.0, 0.46), (12.0, 0.49), (13.0, 0.5), (14.0, 0.5), (15.0, 0.5),
(16.0, 0.5), (17.0, 0.5), (18.0, 0.5), (19.0, 0.5), (20.0, 0.5), (21.0, 0.5), (22.0, 0.5), (23.0, 0.5), (24.0, 0.5),
(25.0, 0.5), (26.0, 0.5), (27.0, 0.5)
Environmental_Capacity = GRAPH(Development*Environmental_attitude)
(0.00, 0.97), (1e+006, 0.825), (2e+006, 0.7), (3e+006, 0.6), (4¢+006, 0.52), (5e+006, 0.45), (6e+006,
0.385), (7e+006, 0.325), (8e+006, 0.27), (9e+006, 0.24), (1e+007, 0.185)
experience = GRAPH(depreciated_value)
(0.00, 0.005), (1e+007, 0.065), (2e+007, 0.12), (3e+007, 0.18), (4e+007, 0.235), (5e+007, 0.28), (6e+007,
0.395), (7e+007, 0.505), (8e+007, 0.62), (9e+007, 0.81), (1e+008, 1.00)
Fin_Capacity = GRAPH(Budget_Exp/population)
(0.00, 0.03), (500, 0.1), (1000, 0.19), (1500, 0.28), (2000, 0.375), (2500, 0.495), (3000, 0.63), (3500,
0.685), (4000, 0.74), (4500, 0.805), (5000, 0.97)
Gas_Demand = GRAPH(Gas_Prices)
18
(0.00, 4e+008), (1.00, 3.3e+008), (2.00, 3e+008), (3.00, 3e+008), (4.00, 3.3e+008), (5.00, 3.1e+008),
(6.00, 2.9e+008), (7.00, 2e+008)
Gas_Prices = GRAPH(TIME)
(0.00, 1.05), (1.00, 1.16), (2.00, 1.52), (3.00, 1.43), (4.00, 1.36), (5.00, 1.57), (6.00, 1.88), (7.00, 2.29),
(8.00, 2.59), (9.00, 2.81), (10.0, 3.29), (11.0, 2.38), (12.0, 2.84), (13.0, 3.33), (14.0, 3.85), (15.0, 4.13),
(16.0, 4.31), (17.0, 4.62), (18.0, 4.90), (19.0, 5.46), (20.0, 5.99), (21.0, 6.51), (22.0, 6.65), (23.0, 6.69),
(24.0, 6.69), (25.0, 6.69), (26.0, 6.69), (27.0, 7.00)
local_need = GRAPH(depreciated_value/(population/sq_miles))
(0.00, 0.97), (2000, 0.715), (4000, 0.58), (6000, 0.475), (8000, 0.42), (10000, 0.375), (12000, 0.33),
(14000, 0.3), (16000, 0.27), (18000, 0.255), (20000, 0.21)
Political_Support = GRAPH(if Fiscal_constraint_met <1 then 0 else (Tech_Capacity+Fin_Capacity-
(citizen_pressure*.50)))
(0.00, 0.01), (0.2, 0.115), (0.4, 0.185), (0.6, 0.275), (0.8, 0.395), (1.00, 0.5), (1.20, 0.58), (1.40, 0.655),
(1.60, 0.745), (1.80, 0.89), (2.00, 1.00)
population = GRAPH(TIME)
(0.00, 4730), (1.00, 4785), (2.00, 5032), (3.00, 5058), (4.00, 5059), (5.00, 5066), (6.00, 5075), (7.00,
5073), (8.00, 5082), (9.00, 5113), (10.0, 5135), (11.0, 5135), (12.0, 5200), (13.0, 5320), (14.0, 5600),
(15.0, 5760), (16.0, 5800), (17.0, 5800), (18.0, 5800), (19.0, 5960), (20.0, 5960), (21.0, 5960), (22.0,
6040), (23.0, 6040), (24.0, 6040), (25.0, 6080), (26.0, 6080), (27.0, 6120)
rate_of_appearances_at_TAC_meetings = GRAPH(TIME)
(0.00, 0.18), (1.00, 0.18), (2.00, 0.18), (3.00, 0.18), (4.00, 0.25), (5.00, 0.5), (6.00, 0.855), (7.00, 0.3),
(8.00, 0.00), (9.00, 0.55), (10.0, 0.77), (11.0, 0.77), (12.0, 0.55), (13.0, 0.755), (14.0, 0.755), (15.0, 0.755),
(16.0, 0.755), (17.0, 0.755), (18.0, 0.755), (19.0, 0.755), (20.0, 0.755), (21.0, 0.755), (22.0, 0.755), (23.0,
0.755), (24.0, 0.755), (25.0, 0.755), (26.0, 0.755), (27.0, 0.755)
Reg_PriorityRank = GRAPH(local_need+Tech_Capacity+Political_Support)
(0.00, 0.01), (0.3, 0.06), (0.6, 0.105), (0.9, 0.15), (1.20, 0.21), (1.50, 0.29), (1.80, 0.345), (2.10, 0.47),
(2.40, 0.605), (2.70, 0.725), (3.00, 1.00)
State_Priority_Rank = GRAPH((Political_Support*.2) +( Reg_PriorityRank*.2)+(Cost_benefit*.6))
(0.00, 0.005), (0.1, 0.055), (0.2, 0.095), (0.3, 0.19), (0.4, 0.275), (0.5, 0.365), (0.6, 0.44), (0.7, 0.545), (0.8,
0.645), (0.9, 0.755), (1, 1.00)
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Tech_Capacity =
GRAPH((experience*rate_of_appearances_at_TAC_meetings)+((number_of_tech_reports/number_of_p
rof_degrees)*(Interest_in_Transp)))
(0.00, 0.015), (0.075, 0.09), (0.15, 0.165), (0.225, 0.245), (0.3, 0.305), (0.375, 0.375), (0.45, 0.53), (0.525,
0.655), (0.6, 0.775), (0.675, 0.86), (0.75, 1.00)
20
