Matthew Schroeder

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					                 FINAL CONTRACT REPORT


                             DRAFT


Scenario-Based Planning for the Regional Impacts of Statewide
             Multimodal Transportation Polices

                      JAMES H. LAMBERT
                     Center Associate Director
Research Associate Professor of Systems and Information Engineering

                   MATTHEW J. SCHROEDER
                   Graduate Research Assistant




        Center for Risk Management of Engineering Systems
                       University of Virginia




                      DRAFT August 6, 2008




DRAFT
                                PROJECT TEAM


University of Virginia

James Lambert, Associate Director, Center for Risk Management of Engineering Systems
Matthew Schroeder
Megan Kersh
Ward Williams
Asad Saqib

Project Steering Committee
Wayne Ferguson, VTRC
Katherine Graham, VDOT
Mark McCaskill, Roanoke MPO
John Miller, VTRC
Kimberly Pryor Spence, Multimodal Office
Mary Lynn Tischer, Multimodal Office

Acknowledgements

Ralph Davis, VDOT
Joost Santos, UVa
Chad Tucker, VDOT
Ben Mannell, VDOT
Michael Garrett, VDOT




DRAFT
                                         Abstract

       The Office of the Virginia Secretary of Transportation identified twenty-one
transportation policies and a set of forty-two policy performance evaluation criteria in
Virginia‟s long-range multimodal transportation plan, VTrans2025. The latest long-range
planning effort VTrans2035, which is ongoing, has provided direction for the research
described herein.

        While there has been considerable discussion of the potential impacts of the
VTrans policies to the Commonwealth as a whole, there have been few attempts to assess
and characterize the individual local and regional impacts of the policies. Moreover the
sensitivity of the policies to various scenario assumptions about the future has been in
need of further exploration on the statewide, regional, and local levels. An upcoming
summit of transportation thought leaders will convene in November 2008 will have use
for knowledge of how VTrans policies have differential impacts to various regions and
with various scenarios of the future.

        This research effort develops and tests methodology for scenario-based
assessments of the impacts of the VTrans polices for multiple regions of the
Commonwealth of Virginia. The methodology is implemented in an MSExcel workbook
available for download at www.virginia.edu/crmes/multimodal2. This report describes
the application of the methodology for a locality or regional planning organization, e.g., a
Metropolitan Planning Organization (MPO) or Planning District Commission (PDC), to
assess the impact of statewide multimodal policies across several of its long-range
planning scenarios. This report includes an application of the methodology to the
Roanoke and Hampton Roads regions of Virginia, a review of scenario-based planning,
documentation of future scenarios, a survey of Metropolitan Planning Organizations in
Virginia for their best practices in scenario-based transportation planning, and
recommendations for next steps.

        The effort also describes several other implementations to Northern Virginia and
Richmond regions in Appendix A. A survey and study of scenario-based planning best
practices is featured in Appendix B. Appendix C describes an input/output analysis of
economic growth based on transportation investments that was requested by the
Multimodal Office.




DRAFT
                                   INTRODUCTION


       There are several questions motivating the current research, as follows:
      Can scenario-based planning help regions better coordinate transportation and
       other planning with the state?
      How do regional differences such as geography, demographics, and economy
       affect multimodal transportation planning?
      Is there a way to standardize planning to consider a wide array of future events
       when planning and forecasting?
      Do the varying needs of regions make it difficult to prioritize statewide policy?
       Can scenario-based planning be used to help policy makers interpret the varying
       needs of regions and better coordinate and prioritize multimodal transportation
       policies on a regional level?

        Past transportation planning efforts have focused on cost-benefit and impact
models. However, data collection methods proved to be heavily assumption-based and
unrepeatable (Cervero & Aschauer, 2007). Even when the cost-benefit models produce
reliable results for specific transportation investments, policymakers need a better
understanding of the impacts of the investment on a region enhances the decision-making
process. Scenario-based planning is an alternative method for obtaining this knowledge.
Scenario-based planning “highlights the major forces that may shape the future and
identifies how the various forces might interact” (Federal Highway Administration,
2007). In the transportation sector, scenario-based planning emphasizes the relationship
between transportation and the environment, the economy, and society. Scenario-based
planning illustrates how the transportation infrastructure can accommodate variations in
characteristics of a region. For instance, if the United States Census Bureau estimates a
twenty percent increase in the population of a region over the next five years, the
transportation infrastructure will need to expand to grow to match the increased demand.

      Scenario-based planning is an add-on or complementary to the following analysis
methods:

      Return on investment
      Cost-benefit analysis
      Risk analysis
      Impact analysis
      Sensitivity analysis
      Systems analysis and integration

       The establishment of goals and performance measures for long-range
transportation planning is useful on statewide and regional and local levels for
understanding a variety of activities, including:
     Funding and investments

DRAFT
      Land use
      Connectivity
      Priority setting

         VTrans2035 and the Office of Multimodal Transportation Planning and
Investment (Multimodal Office) are using twenty-one policies and forty-two performance
criteria for evaluating statewide transportation policies. Example policies include
investing in public transit, planning multimodally, and improving travel mode
connections. The criteria encompass safety and security; preservation and management;
efficient movement of people and goods; economic vitality; quality of life; and program
delivery.

         The Multimodal Office has two challenges for applying scenario-based planning.
The first challenge is that a set of high-level goals and associated criteria have been
developed for prioritizing multimodal transportation policies, but they have not yet been
integrated into existing prioritization methods. The second challenge is the existence of
several obstacles to multimodal transportation prioritization that have occurred in
Virginia in the past the few years. Three major obstacles are; the inability to achieve
consistency in planning factors and assumptions across modes, different regulatory
processes for modes of transportation, and a lack of methodology for computing a
transportation project‟s return on investment (ROI). Scenario-based planning addresses
the first obstacle, inconsistent planning factors, and also provides a method that adapts to
uncertainty in transportation-related futures.


                                PURPOSE AND SCOPE

      The intended customers of this effort are the Commonwealth of Virginia and the
Metropolitan Planning Organizations (MPOs) and Planning District Commissions
(PDCs) of the Commonwealth, as depicted in Figure 1.




Figure 1. PDCs and MPOs in the Commonwealth of Virginia [Rappahannock Rapidan Regional
Commission, 2008]


DRAFT
        The effort develops a methodology motivated by guidelines of the Federal
Highway Administration (2007) to study the impacts of statewide multimodal
transportation policies on localities and regions under various future scenarios. An MS
Excel workbook is developed to process the inputs from various regions and generate a
local assessment of the impacts of the transportation policies. The report describes four
demonstrations of the workbook to several regions of Virginia, Northern Virginia,
Hampton Roads, Roanoke, and Richmond. The report surveys regional planning
methodologies. For example, in Northern Virginia, TransAction2030 (2007) provide
multimodal analysis to create vision, goals, and strategies for future transportation. In the
Hampton Roads region, the report analyzes the affects of future scenarios for a port-
centered economy as well as the return on investment and economic impacts of the ports.

        The effort explored the scenarios used by different transportation departments and
planning groups across the nation. The effort adopts scenarios developed by the
Metropolitan Washington Council of Governments (2004), Delaware Valley Region
Planning Committee (2007), Roanoke Valley Area Metropolitan Planning Organization
(2005) and Thomas Jefferson Planning District Commission (n.d.). The effort sorted the
selected scenarios into four main categories – spatial, demographic, economic, and other,
which include environmental issues and natural disasters. The four categories encompass
major scenarios that transportation planners have to account for in the future.

        The effort analyzed the regional impacts of transportation policies across
performance criteria, and under various future scenarios. The effort developed an
automated workbook that enables a regional planner to characterize the impacts of the
policies for a region of Virginia under a base scenario and five other scenarios. The
scenarios are S.2 - acceleration of sprawl, S.17 - mass retirement of an aging population,
S.18 - region-wide natural disaster, S.3 - accelerated growth of information technology
amenities, and S.19 - significant reduction in air quality, all of which were identified via
a review of twelve regional plans and interviews with regional planners. The results
describe which policies are most sensitive to the scenarios. The policy of investing in
technology scores high for all six scenarios.

      A survey and analysis of best practices in scenario-based planning of Virginia
Metropolitan Planning Organizations is provided in Appendix B.

       Economic growth resulting from transportation investments was also studied via
input-output models with the results provided in Appendix C.


                      BACKGROUND/LITERATURE REVIEW

       This section reviews some fundamentals of scenario-based planning for
transportation systems. The section also examines the methodology recommended by the
Federal Highway Administration and other regional planning organizations.



DRAFT
                                Scenario-Based Planning

        Scenario-based planning lends itself to many misconceptions about its processes
and aims. Zergas , Sussman, and Conklin (2004) allay misconceptions by researching the
origins and applications of scenario planning. The terms scenario-based planning and
scenario planning are often used interchangeably in literature. „Scenario planning‟ means
the focus on the scenario part of scenario-based planning.

        Zergas et al. (2004) follows the evolution of scenario planning from corporate
strategies to transportation planning. Royal Dutch Shell is generally first credited with
using a scenario-based approach to aid strategic business planning. Rapid changes in the
business environment motivated Shell to search for a planning approach that could better
deal with uncertainty. Shell adopted scenario-based planning in order to address a wide
range of situations and aid planners in preparing for a variety of business scenarios.
Zergas et al. (2004) emphasize the use of scenario planning for Shell by mentioning that
the application of scenario planning contributed to Shell‟s ability to anticipate and
prepare for the oil crisis of 1973 and its economic aftermath.

                   Scenario Planning as a Supplemental Technique

        Currently, there appears to be no major statewide transportation departments that
significantly incorporate scenario-based planning to multimodal transportation planning.
A possible reason for slow adaptation of scenario-based planning by transportation
planners is that scenario-based planning involves long term planning and future
scenarios, and as results, years may elapse until the efficacy of scenario-based planning
can be properly evaluated. Criticisms by transportation planners may be another reason
for the slow adaptation of scenario based planning. Cole (2001, p. 373) states another
reason for the lack of popularity for scenario-based transportation planning approaches,
many policy makers prefer specific answers, as opposed to several different possibilities.
Zergas et al. addresses criticisms of scenario planning and clears up misconceptions of its
use and results. Critics attempt to discredit scenario planning by claiming that it is
ineffective in predicting the future. Zergas et al. states that, “Scenario planning is not a
replacement for traditional planning techniques such as forecasting; instead it aims to
help organizations better prepare for the unexpected. In short, scenario planning helps to
make robust strategic choices” (2004, p. 8).

       In their own use of scenario planning, Zergas et al. (2004) applies systematic
scenario planning to transportation planning in the Houston metro area. The study used
scenario planning methodology to examine key issues for the area, the scope of the
problem, and finally generating relevant scenarios. The scenarios generated are
subsequently used to evaluate transportation policies in the area. The results from the
study showed that although the scenario planning could not be directly evaluated, the
concept provided insight into transportation futures for transportation planners.

        Bartholomew (2005) describes the usage of using scenarios in planning for land
use issues in transportation. The FHWA (2007) also is using scenario-based planning in

DRAFT
transportation land use issues. Bartholomew (2005) conducted a survey in 2003-2004 of
MPOs and found that out of one-hundred fifty-two recipeients of the survey, 45%
indicated that they had at least one scenario planning project. Scenario-based planning
allows for increased community involvement in planning and is recommended in
scenario-based planning literature (FHWA 2007; Bartholomew, 2005; Zergas, 2004).

                  An Existing Methodology with Scenario Planning

         The Federal Highway Administration (FHWA) recommends using scenario
planning (About Scenario Planning, 2007) as part of transportation planning. The FHWA
developed a recommended scenario-based planning methodology in an attempt to
familiarize other transportation groups with scenario-based planning. The FHWA
scenario-based planning approach echoes the methodology presented above by Zergas et
al. (2004) through its emphasis on “scenario planning as an enhancement of, not a
replacement for, the traditional transportation planning process” (Zergas et al., 2004, p.
8). In its approach, the FHWA regards the main purpose of scenario planning as aiding in
preparation of potential transportation issues instead of predicting futures.

        The FHWA scenario planning methodology involves six steps. The first three
steps of the FHWA methodology involve creating and planning scenarios. The first step
recommended by the FHWA is to research „driving forces‟. Driving forces are, “the
major sources of change that impacts the future” (About Scenario Planning, ¶12). Trends
in local land use, levels of congestion, and local demographics are commonly used
driving forces. The second step is to determine patterns of interactions. Determining
patterns of interactions between driving sources can be done in a variety of ways. The
FHWA recommends that transportation planners use a matrix and develop a metric
related to positive or negative outcome. The third step involves creating scenarios from
planners by fitting realistic situations to patterns between the driving forces. An example
of a scenario is an event where jobs and city population increase. The FHWA describes
the goal of creating scenarios is to “bring life to the scenarios in a way that community
stakeholders can easily recognize and connect the various components” (About Scenario
Planning, ¶16). The fourth through sixth step of the FHWA methodology involve
stakeholders analyzing and evaluating the scenarios. The fourth step is to analyze the
implications of the scenario. Transportation planners can use scenario-planning software
tools to present scenarios visually. Scenario-planning software also gives stakeholders
and planners a better view of the consequences of a scenario. During this step,
transportation planners and stakeholders can develop potential transportation policies that
mesh with the scenarios. Evaluating scenarios is the fifth step in the FHWA‟s
methodology. FHWA details a variety of methods to accomplish scenario evaluation,
such as using various criteria and presenting the scenarios to the community stakeholders.
The sixth and last step is monitoring relevant indicators to the scenario. Scenario
planning is a dynamic methodology, and transportation planners can generate new
scenarios as events occur (About Scenario Planning).

                  Virginia Applications of Scenario-Based Planning

       Several applications of scenario-based planning in Virginia are described below.

DRAFT
Thomas Jefferson Planning District

        Thomas Jefferson Planning District (TJPD) and the Roanoke Valley Area
Planning Commission (RVARC) both apply scenario-based transportation planning, but
take different approaches in application. The RVARC generally uses the approach of
developing detailed objective scenarios that guide planners to relevant policies. These
detailed scenarios are then converted into numbers that apply to transportation area
zones. The numbers are then put into models that such as IMPLAN, an economic impact
modeling system. After planners use forecasting tools, other analysis and decision
making is done by transportation planners and relevant policies are generated. In some
cases, the models are not used and scenarios more used as a post-process tool for relevant
policies (M. McCaskill, personal communication, February 25, 2008).

        The Thomas Jefferson Planning District (TJPD) uses scenario-based planning in
the Eastern Planning Initiative project. The Eastern Planning Initiative focuses on the
future of regional growth. Thus, TJPD uses scenarios that focus on growth possibilities
specifically (Jefferson Area EPI, n.d.). TJPD uses the growth scenarios to gauge
stakeholder‟s opinions on the possible futures. Planners develop potential policies based
on the evaluation of scenarios and other constraints.

        Though the use of scenario-based planning for TJPD and RVARC are different,
the use and scope of scenarios both are constrained by deadlines and set budgets. Also,
for major transportation forecasting, both organizations rely on the Virginia Department
of Transportation. The project‟s work with Roanoke will also consider the approach of
TJPD as well as other approaches transportation planners use outside of Virginia.

Metro Washington Council of Governments

        An application of scenario planning was recently used to predict relevant
transportation futures in Northern Virginia. “What if…The Washington Region Grew
Differently?” (2006) is a regional mobility and accessibility scenario study by the Metro
Washington Council of Governments (MWCOG). The study identifies four key issues
facing the Washington D.C. area, and matches scenarios to each issue. The issues come
from the topics of population growth, economics, and demographics. MWCOG chooses
job growth outpacing household growth as the first key. The second issue is workers
living farther away from their jobs. The third issue is the divide between the eastern and
western part of the region in terms of demographics and economics. The fourth issue is
that most growth areas are located outside transit station areas.

        MWCOG‟s approach (2006) of matching a scenario to a key issue uses a
combination of the methods similar to that recommended by the Federal Highway
Administration (FHWA) (2007) and Zergas et al (2004). However, instead of using
objectives and policies, MWCOG treats scenarios as potential policies that transportation
planners can evaluate. MWCOG also develops each key issue is on a large enough scale
to match a scenario whereas FHWA and Zergas et al. both consider key issue
interactions to generate scenarios. The MWCOG approach yields more direct scenario

DRAFT
addressing the key issues, however, the approach may miss important scenarios that
planners can discover by looking at the interaction between key issues. The proposed
thesis project generates scenarios by using a combination of the Zergas et al. (2004), the
FHWA (2007), and the Metro Washington Council of Government (2006) approach in
consultation with transportation planners.

            Scenario-Based Planning Multimodal Planning of Other States

       Transportation Planning groups outside of Virginia have also integrated scenario-
based approaches into planning. Envision Utah and the Sacramento Region Blueprint are
two out of seven noteworthy scenario-based planning application the Federal Highway
Commission (FHWA) features on its website. Most of the noteworthy scenario-based
planning applications focus on growth-based or environmental approaches to scenario-
based planning. Both Envision Utah and the Sacramento Region Blueprint are examples
of focuses on growth and land-use in scenario-based planning.

        Envision Utah (http://www.envisionutah.org/process-scenario.phtml) guides the
development of Utah growth patterns similar to the Thomas Jefferson Planning District
approach. The scenario focus of Envision Utah includes the environment, economic
strength, and quality of life. Thus, part of the process of Envision Utah is to develop
scenarios that consider multiple modes of transportation in order to accomplish objective
relating to environment, housing, and mobility.

        The Sacramento Region Blueprint is a transportation land-use study developed to
(http://www.sacregionblueprint.org/sacregionblueprint) aid growth in Sacramento,
California. The project uses scenarios to evaluate transportation projects and land-use
strategies. Although the Sacramento Region Blueprint uses scenarios as forecasting tools,
which is contrary to scenario-planning expert advice, the project has received planning
awards for its progress from the Environmental Protection Agency and the Federal
Highway Commission.


                                    METHODOLOGY

       This effort develops a methodology to analyze the impacts of statewide
multimodal transportation policies on regions under various scenarios, as described in
Figure 2. A Microsoft Excel workbook is automated to process the inputs from various
regions and give a local prioritization of transportation policies as data are entered by a
regional transportation planner.




DRAFT
    Figure 2. Scenario-based planning methodology for evaluating regional impacts of statewide
                                multimodal transportation policies.

        The first step in the methodology is to Select Regions to use scenario-based
planning in assessing the impacts of statewide multimodal transportation policies.
        The second step is to Select Scenarios that are relevant to the regions selected.
The process of selecting scenarios is done through a three step process that involves
Identification of Key Issues that effect the region, Identification of Key Factors, and the
Discussion and Combination of Issues and Key Factors. For example, two key issues for
many regions in Virginia are retirement and gas prices. Two key factors that respectively
drive these issues are the baby-boomer population reaching retirement age and the
economy. Thus, transportation planners and policy makers may choose to create two
future scenarios based on the combination of the issues and factors: increased retirement
and transit-oriented development.
        The third step involves Scoring Transportation Policies Based On Evaluation
Criteria. In the methodology, the scoring is based on if the policy has a significant,
moderate, or minimal/no impact on the evaluation criteria.
        The fourth step is to Weigh Evaluation Criteria with Scenarios. This step uses the
scenarios developed in the second step and the evaluation criteria in the third step.
Transportation planners and policy makers decide whether given the future scenarios if
the evaluation criteria will have anywhere from a major increase in importance to a major
decrease in importance. The importance of the evaluation criteria affects the policy
scoring in step three.
        The fifth step is to Assess Policy Performance Sensitivity to the Region. Each
future scenario generates a score for a transportation policy that was evaluated in the third
step. The scenario generates the policy score by modifying the policy score for each
evaluation criterion based on the importance of the criterion in the scenario as determined
in step four. For example, a policy scores a „10‟ for evaluation criterion one.

DRAFT
Transportation planners determine that for Scenario A the evaluation criterion one will
have a „major increase of importance‟. Since the criterion is more important, it receives a
weight that increases the policy score under the criterion to „15‟. If Scenario A had
evaluated the criterion as having a „major decrease of importance‟ then the policy score
under the criterion could be decreased to „5‟.
        The methodology produces a set of scores for a statewide multimodal
transportation policy over relevant regional future scenarios.


                             RESULTS AND DISCUSSION

        This effort applies the methodology to the Roanoke region in Virginia using the
Microsoft Excel Workbook tool. Appendix A features demonstrations the methodology
in the Hampton Roads, Northern Virginia, and Richmond Region.

                               Finding Relevant Scenarios

        The second step to apply the methodology to Roanoke is to Select Scenarios. For
Roanoke, the effort involves research that includes talking to transportation planners in
the Metro Planning Organization and researching the relevant topics that affect
transportation in the region. For the statewide scope of the tool, the research is based on
the work of other states and localities such as the Sacramento Region Blueprint
(http://www.sacregionblueprint.org/sacregionblueprint) and Envision Utah
(http://www.envisionutah.org/process-scenario.phtml). The methodology for scenario-
planning adapts techniques and recommendations from methodologies used by Zergas et
al. (2004), Pearman (1986), the Metro Washington Council of Governments, and the
Federal Highway Administration.
        The scenario-based planning methodology first identifies key issues in the area
that may affect the future. Several broad categories exist for these factors that include
demographics, land usage, and population shifts. This suggests individual categories for
each factor. Second, the design identifies factors that create the issues. Third,
transportation planners discuss potential scenarios that may occur because of these
factors and key issues, which are then combined to create scenarios. Scenarios that
transportation planners generate from the methodology should cover a wide scope of
issues to provide insight for transportation planners and allow anticipation of future
events that affect transportation. Figure 3 shows an example of general statewide
scenarios chosen for the effort.




DRAFT
                 Figure 3 - Scenarios for Assessment of Transportation Policies

                                   Case Study Scenarios

         In order to demonstrate the methdology, the effort conducted a case study of the
region based on five scenarios. Bartholomew (2007, p. 14) recommends choosing an
amount of scenarios that is “not too many to confuse participants, but enough to allow for
divergent thinking and coherent story telling (Godet, 2001; Ringland, 1998, 2002).” First,
the effort interpreted the scenario, S.2 - Urban sprawl. Urban sprawl is an issue that
affects many places of growth in the region and the nation. As developers continue to
plan future residential and commercial buildings around the region and as the number of
sites to build in the city decreases, urban sprawl will increase. If the current growth rates
continue in the region, urban sprawl is inevitable despite public opposition. Thus,
regional transportation planners will need to consider the impacts of urban sprawl when
prioritizing multimodal transportation policies.
         Second, the effort analyzed the impacts of scenario, S.17 - Retirement. As the
demographics of an area changes, the transportation system must adapt to meet new
demands. This is an important issue as the baby-boomer generation, a major cross-section
of the population in the United States, is reaching the retirement age. An aging population
“implies additional transit needs, changing housing needs, the need for heightened safety
standards, and residents with inflexible financial situations” (Federal Highway
Administration [FHWA], 2007). Businesses must even change how they operate, by
developing new products to target the current demographics and compensating for the
expected labor shortages (MIT Center for Transportation & Logistics, 2007). The
retirement scenario is particularly important for the region in the case study, since the
area is considered one of top places in the country to retire, as 40% of the population of
the region is 45 or older (The Roanoker Magazine, 2007). This age demographic is
concerned with maintaining their mobility, within the limits of their physical and
financial capabilities. The older population requires different forms of public transit,

DRAFT
especially ones that link them with residential, retail, and health centers (The Roanoke
Valley Area Metropolitan Planning Organization [RVAMPO], 2005).
         Third, the effort studied scenario S.18 - Natural disasters relevant to the region.
Areas across the country are subject to natural disasters that cost millions of dollars in
damages.
Through the study of past disasters and local environmental factors, the hazard analysis
workgroup of the region identified flooding and wildfires as the two most likely natural
disasters for the area. The streams running through the steep terrain of the region subject
the area to periodic flash flooding. To highlight the importance of this scenario, flood
related research and documentation from the past shows that there are an estimated 5,400
structures that could possibly be impacted by flooding in the region (RVAMPO, 2000).
         Fourth, the effort consider the potential of scenario S.3 - IT amenities growing in
the region. This high priority scenario is due to the large information technology and
engineering base that has developed in several surrounding areas. Many companies have
chosen to locate their work facilities and headquarters throughout the surrounding areas
due to its highly skilled technology workforce, policies that encourage business growth,
and advanced IT infrastructure (Commonwealth of Virginia, Office of the Governor,
2007).
         Fifth, scenario S.19 - Decrease in air quality, is more specific to the region.
Intermodal and multimodal sources of transit stations are needed throughout the country
to support a wider range of transportation solutions for public, commercial, and several
other uses. Recently, ten areas that are in close proximity to the region were selected as
possible locations for a new rail and truck intermodal transit station. However, there has
been opposition to building transit stations in the region from environmental groups
(Christopolus, 2007). Health hazards from higher levels of soot include a 30% increased
risk of death for individuals with heart disease, lung disease, and diabetes. The current
soot level of the region already approaches the EPA soot limit of PM 2.5. Thus, a new
transit station could cause dangerous levels of soot for the local communities.

                                 Identification of Policies

        In preparation for the next step of the methodology, Score Policies Based on
Evaluation Criteria, the effort identifies multimodal transportation policies. The effort
used the 21 policies established in the statewide long-range transportation
plan, VTRANS 2025 (Virginia Department of Transportation [VDOT], 2004). The
policies fall into four main categories: funding/investment, land use, connectivity, and
priority setting. Examples of the policies include improving connections between modes,
considering state versus local rules, increasing rail funding, and starting a trust fund for
transportation.

        The effort identified policies that are special to the region to allow for more direct
options for the regional transportation planners. The policies are meant to correlate with
the regional- specific scenarios that the effort developed. The additional policies relevant
to the region include P.22 - Smart growth, P.23 - Bicycle and pedestrian facilities feeder
system, P. 24 - Environmental focus, and P. 25 - Diesel and filter regulation.



DRAFT
         Policy P.22 - Smart growth is a growth strategy that impacts multimodal
transportation use. Smart growth focuses on building desirable communities for residents.
In a public workshop, it was shown that the public rejected urban sprawl scenarios in
favor of more “Smart-growth” patterns. Smart growth patterns mainly refer to having
denser town and urban centers. Proponents of Smart growth claim that increasing
density of population centers will decrease driving time, traffic congestion, and preserve
farmland (Thomas Jefferson Planning District Commission, 2007). Implementation of
Smart Growth would place more influence on multimodal transportation because with
more dense population centers, there is potential to use different modes of transportation.
Therefore, policy P.22 - Smart growth, will be added to the list of policies considered for
the region.
         Next, the effort consider Policy P.23 - Bicycle and pedestrian facilities feeder
system (RVAMPO, 2007). Effective mobility of the baby boomers can be maintained by
providing better walking and biking facilities, as many areas lack safe or formal paths
(RVAMPO, 2006). The implementation of 12 ft. lanes to the current infrastructure
provides room for bicyclists, walkers, and users of any other personal ride-along type
devices to travel. The lanes could connect shopping and residential districts, bus stops,
and park and ride lots. Many bikes are publicly available and buses have been equipped
with bike racks. Policy P.23 - Bicycle and pedestrian facilities feeder system, will give
the aging population more transportation options, while allowing them to maintain a
healthy lifestyle and help the environment.
         Policy P.24 - Environmental focus, is another potential policy for the region.
Some of the more recent natural disasters have been linked to global warming
(Environmental Protection Agency, 2007). Scientists have suggested that the recent
drought is linked to climate changes associated with global warming. The land being
extremely dry increases the likelihood of wildfires. If the region and the nation set forth
efforts to reduce carbon emissions, the more recent climate extremes and possibility of
natural disasters would be reduced (RVAMPO, 2007).
         Last, the effort study Policy P.25 - Mandate of ultra low sulfur diesel and filters
for all new trucks (Christopulos, 2007). To allow the intermodal truck and train transit
station to be built in the region, all new trucks must use ultra low sulfur diesel with air
filters. The filter rule would only apply to trucks of model year 2007 and beyond, and
eventually to all trucks by the year 2030. The policy would reduce the sulfur emissions
by 90% or more, particulate emissions up to 80%, and nitrogen oxide up to 20% . The
reduction of emissions would help to maintain levels of soot under the EPA monitored
levels of PM 2.5. Ultra low sulfur diesel mandates would also apply to all other diesel
vehicles using the facility, such as off-road vehicles.


                           Selection of Performance Criteria

        Next, the effort identified performance criteria from the statewide long-range
transportation plan to measure the impact of a policy and to weight different scenarios.
The performance criteria consist of safety, efficiency, economic vitality, quality of life,
and feasibility. Sub-criteria are used to more specifically define the different aspects of
the performance criteria. For example, sub-criteria, C.1.1.a under safety asks if the policy

DRAFT
will, "Improve safety for system users and operators within the system and at mode
origins/destinations (e.g., improve safety at at-grade crossings, improve bicycle and
pedestrian safety, correct sub-standard (safety) designs and other geometric/pathway
(e.g., runway obstructions, channel depth, bridge clearance, etc) deficiencies)."
         Within the subcriteria are the performance measures, which directly evaluate the
policies. The performance measure under sub-criteria C.1.1.a is, "Does the policy
significantly reduce crashes and/or incidents?" There are 13 criteria and 34 sub-criteria
and performance measures currently included in the tool. To determine the overall scores
for policies, the effort evaluated the impact of each policy across each of the performance
measures. The methodology uses a rating of 0 for a minimal or an unknown impact, 0.5
for a moderate impact, and 1 for a significant impact.

                     Scoring Policies Based on Evaluation Criteria

Figure 5 shows how in step three of the methodology transportation planners score
policies using a high, medium and low/not sure scale. In the application the
methodology, the effort uses a 0, 0.5, 1, scale for scoring the impacts.




       Figure 5 - Multimodal Transportation Policy Assessment Before Scenario Evaluation




                          Using Scenarios for Scoring Policies

       The fourth step of the applied methodology is to Weigh Evaluation Criteria with
Scenarios. Since the scenarios weigh the policies, they must cover a wide range of topics
and concerns. Moreover, scenarios do not serve to predict the future, they do not have to

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be proven inevitable or probable; they must represent transportation planning concerns.
Thus, the survey tool allows transportation planners to add new scenarios as time
changes. Therefore, transportation planners can easily update the survey tool. Because the
survey tool is built with Microsoft Excel, most planners will be able to edit the survey
tool with little difficulty.

Modifying Scenario Impacts for Users

       The weighting of the scenarios by transportation planners is one of the most
important aspects of the methodology. The Virginia Department of Transportation
provides high level criteria for regional transportation planners to evaluate against the
scenarios. The criteria include safety, land preservation, efficient movement of people
and goods, economic vitality, quality of life, and program delivery. Transportation
planners rank the criteria importance from major increase to minor decrease in the case of
a scenario as shown in Figure 4.




                   Figure 4 – Policy Evaluation Criteria Weighting By Scenario

 The survey tool has the criteria set to equal values for default. If a transportation planner
increases the weight for criteria, the score that each policy receives in the criteria will
increase for the scenario. Transportation planners choose major increase for criteria that
may have a large increase in an importance during a scenario. For example, in the event
of a natural disaster, the efficient movement of people would have a higher importance.
Conversely, transportation planners chose major decrease for criteria that may indicate a
large decrease in importance during a scenario. There were not any major decreases for
the Roanoke case. Most of the impacts for the Roanoke case are minor increases and
decreases. Major increases and decrease affect the criteria by doubling the magnitude of
the score, minor decreases and increase the magnitude of the score by 1.5 times. The
increase in weight for the criteria is relative, meaning that if all criteria importance
increase or decrease in the same manner for a scenario, the criteria will still have the
same weight. In the results section of the survey tool, the tool displays the policies and
their scores in different scenarios.

                  The Sensitivity of Policy Ranking on Scenario Evaluations

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        The fifth and final step, Assess Policy Performance Sensitivity to Region, involves
synthesizing the information developed in the previous steps of the methodology. To
inform the transportation planners of the effects of the scenarios on the policy
prioritization, there are several displays in the survey tool representing the policy scoring.
The survey tool uses a table to display the scores of the policies against the scenarios as
well as the mean score of each policy. The tool includes another table that shows the
ranking of each policy against other policies for each scenario. Scatter plot graphs
accompany both tables in the survey tool. The graphs show the wide range of policy
effects. The displays of policy scores are critical for the survey tool to fulfill the
objective of providing a method for transportation planners to get more acquainted with
the affects of multimodal transportation policies in varied futures.
        After planners enter input into the survey, the survey tool outputs scores for the
policies. Transportation planners can choose from different methods to prioritize these.
The mean score is presented as one method; however, different policies score higher
depending on scenarios. Figure 6 shows the results of the policy scoring for Roanoke.




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        Figure 6 - Overall Policy Impact Scoring




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The mean score of the policy for all the scenarios is the main scoring method the team
uses in this iteration of the survey tool. Figure 7 shows that most of the range of scores
for each policy, using the highest score and lowest score for a scenario. Many of the
scores overlap, which indicates that transportation planners need to consider more than
just the mean score of the policies.




                Figure 7 - Range of Policy Impact Scores with Top Scores Circled




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         Figure 8 and 9 shows the relative ranking for each of the policies. The highest
scoring policies are highlighted in green in figure 8.




                      Figure 8 - Rankings of Policies Based on Scenarios




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                 Figure 9. – Graph of Rankings of Policies Based on Scenarios


       The top five policies based on the mean score are P.2 - support transit, P.15 -
think multimodally, P.13 - invest in technology, and P.22 - smart growth. The specific

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policies chosen in the prioritization are less important than the methodology and ability
for transportation planners to adapt and effectively use the methodology to prioritize
policies.
        The top rated policies, P.1 - invest in technology and P.15 - think multimodally,
both have the same amount of first and second place rankings throughout the scenarios.
Policies P.22 - smart growth and P - support transit also have the same score; however,
they rank higher and lower in different scenarios. The next chapter discusses the results
of the applying the methodology with the survey tool as well as scenario-based planning
in tools and transportation planning methodologies.

       A recommendation for the survey tool is that transportation planners consider
both mean score and individual scenario rankings. Some of the possible evaluation
methods for transportation planners are weighting scenarios, using one scenario as a tie-
breaker or most important scenario, or using the standard deviation in rankings. The
survey tool provides the information to the transportation planners to allow for several
ways to prioritize the policies.

                                  Uses of the Methdology

         The effort designed the methodology and survey tool to be adaptable to several
metropolitan planning organizations (MPO) and regions of Virginia. Therefore, one of
the major benefits of the survey tool is its flexibility. Transportation planners can add and
take away scenario and policies as well as change the weighting of policy evaluation
criteria compared to scenarios. The survey tool contributes a formal methodology for
prioritizing multimodal transportation policies as requested by the sponsors of the effort,
the Multimodal Office. The scenario-based approach of the survey tool provides an
improvement over other methodologies in terms of providing transportation planners with
a more holistic view of the future in addition to forecasting methods.

                       Demonstrations in Other Virginia Regions

        The purpose of these examples is to demonstrate the robustness of regional
scenario-based planning for evaluating statewide multimodal transportation policies. The
examples include regions in the commonwealth of Virginia that have varying long term
transportation planning needs based on geographical location, demographics, and other
characteristics. Each example portrays the utilization of scenario-based planning on a
regional level and its effects on the prioritization of transportation policies. The result of
these examples illustrates the ability for scenario-based planning to coordinate statewide
multimodal transportation policy prioritization among regions.
        In order to demonstrate the needs of the region and the efficaciousness of
scenario-based planning, the effort performed a case study of four major regions in the
commonwealth of Virginia. These regions are based on the boundaries for the Virginia
Association of Planning District Commission. In addition to R.1 – Roanoke, the regions
chosen were Region R.4 - Richmond, R.2 - Hampton Roads, and R.3 - Northern Virginia.
For each region, the effort observed and analyzed regional factors such as population,
employment, researched long term predictions and planning methodologies and

DRAFT
interviewed transportation planners (reference survey?). In the case studies, after
expounding on regional factors, the effort applied the scenario-based planning workbook
tool to the region. The demonstrations are featured in Appendix A. The final result
provides a policy prioritization recommendation that indicates the results of the diverse
needs of the differing areas.

                             Findings from Demonstration

The case studies in the diverse regions of Virginia displayed the variety of potential
futures based on the unique characteristics of each region. One of the top policies for all
regions was Policy P.2 – Support Transit. The other demonstrations are detailed in
Appendix A. However, there were other policies that scored similarly and robustness
against future scenarios must be taken into consideration during policy prioritization by
transportation planners. The effort utilized scenario-based planning to account for the
different futures of the region and improve coordination potential among the regions by
considering the regional impacts of scenario-based planning on statewide multimodal
transportation policies. The variety in these regions shows an increased need for
coordination of future plans. Scenario-Based planning provides a way to better anticipate
future state and regional transportation needs.

             Scenario-Based Planning Integrated with Current Methods

         Transportation planners can use the survey tool in conjunction with other current
methods. In Roanoke, transportation planners can use the survey tool as a filter to decide
which policies transportation planners will forecast and evaluate (M. McCaskill, personal
communication, February 25, 2008). Also, transportation planners can use scenario-based
planning to account for different characteristics in multiple regions. Scenario-based
planning can also be applied to transportation planning other than policy planning.
Feedback from the Roanoke MPO and Multimodal Office indicate that the scenario-
based planning methodology has potential to integrate with current planning methods and
provide more insight into the possible impacts of multimodal transportation policies in
the future (M. McCaskill, personal communication, February 25, 2008). Transportation
planners often need to evaluate long-term transportation projects and transportation
planners can use scenario-based planning in similar way as they can with prioritizing
long-term transportation policies. Financial constraints are another large concern for
transportation planners. Transportation planners can integrate financial future scenarios to
use scenario-based planning to better understand financial risks for transportation policies
and projects. For transportation planners in Virginia to use scenario-based planning
effectively, the approach must be understood by planners and reasonably integrated into
current planning methods. The scenario-based approach‟s ability to integrate into other
Virginia regional transportation areas is integral to further proving scenario-based
planning use in Virginia and may serve as the topic of a future project.
        Economic, demographic, spatial, and environmental scenarios are more important
than ever before, and it is essential that local, state, and federal government agencies
properly plan for the scenarios. The survey workbook incorporates scenario-based
thinking into long-range transportation planning and is the foundation of a model that

DRAFT
will be the next generation of transportation planning.
  In order to expand the use of the survey workbook and scenario-based planning
approach, the team researched scenario-based planning throughout the state and
conducted a preliminary survey of other Metropolitan Planning Organizations (MPO) in
the state. The survey included high level scenarios such as industrial growth, population
growth, and natural disasters. Regional needs for scenario-based planning differ. The
results of the survey indicated that adjustments are necessary to the survey workbook and
the scenario-planning approach to effectively aid transportation planning.


                                      CONCLUSION

         The application of the policy prioritization methodology in Roanoke successfully
introduced scenario-based planning to the Roanoke Valley Metropolitan Planning
Organization. The effort exceeded most applications of scenario-based planning in
literature by working with transportation planners to revise and improve the survey tool
for usage in other regions of Virginia. As discussed in the literature review, one of the
major criticisms of traditional transportation planning is the lack of single resulting
strategy and lack of holistic approach for future predictions. The project‟s scenario-based
planning approach exposed transportation planners in Roanoke to the holistic approach of
considering multiple futures and categories of events when evaluating long-term
transportation policies. The project prepares scenario-based planning for usage in other
regions in Virginia and potentially in other states. The Multimodal Office also approved
of the concept of the survey tool, and its potential to coordinate state transportation policy
prioritizations and evaluation criteria with regions within the state.
         The survey tool may have to be thoroughly revised for other regions based on
existing methods, but the focus, scenario-based planning, can integrate more smoothly
with other regions. The survey workbook can be customized and used in different regions
and localities. The Office of Multimodal Transportation Planning will able to use
regional and local policy prioritizations to support its overall statewide plan. With more
testing and evaluation, the survey workbook can be used to help states, regions, and
localities more effectively prioritize multimodal transportation policies for the long-term
and positively impact transportation. Additional scenarios and regions may be added in
the future. The ability of the survey workbook to integrate scenario-based planning
maximizes its effectiveness in ranking additional policies to be added in the future. Long-
range planning decisions can be made by determining the potential impacts on additional
regions and their scenarios.
         The survey tool is a simple demonstration of the use of scenario-based planning.
The main reason for the ease of integration for the scenario-based planning approach the
project uses is its flexibility. For example, the survey tool uses Virginia Department of
Transportation criteria to evaluate policies. A Metropolitan Planning Organization (MPO)
can easily change the evaluation criteria to the federal mandated criteria. The scenario-
based planning approach can be made more complex or simpler to account for other
regions transportation processes. Transportation planners and policy makers can also use
scenario-based planning to evaluate transportation projects and their financial constraints,
in addition to the main focus of the project, prioritizing transportation policies. As the

DRAFT
literature states, there is still hesitation from transportation planners for using scenario-
based planning as part of a formal transportation planning methodology. The approval of
metropolitan planning organizations such as Roanoke will help scenario-based planning
to quickly gain more acceptance as a formal tool for transportation planning.


                                    RECOMMENDATIONS

        In the future, the scenario-based planning approach will need several more test
cases and reviews by transportation planners. Since there are many different applications
for scenario-based planning, it is not necessary that the scenario-based planning approach
is unique to the survey tool. Surveys and polls of how transportation planners can use
scenario-based planning in the transportation process will provide guidance to the
creation of a more formal scenario-based planning approach. Appendix E details a brief
survey this effort undertook. Appendix D shows an additional draft-survey that could be
issued to transportation planners in Virginia. The focus for testing scenario-based
planning should be mainly among the metropolitan planning organizations (MPO‟s) in
Virginia. The Virginia Department of Transportation should also be strongly involved in
developing scenario-based planning so the process can be better standardized throughout
the state.
        Transportation is an industry that affects all residents of Virginia and many more
people that travel in Virginia. Transportation policy makers and planners acknowledge
the importance of being able to properly plan for transportation in the future as
population increases and more people use transportation (M. McCaskill, personal
communication, February 25, 2008). Scenario-based planning provides a new way for
transportation planners and policy makers to holistically plan for a complex future. Thus,
the attempt to implement and use scenario-based planning as part of the transportation
process is a beneficial and worthwhile project.

                       COSTS AND BENEFITS ASSESSMENT

This report has described initial steps and associated lessons for the use of regional
scenario-based planning. The potential benefits of such a framework include:

      Coordination of regional long term planning with other regions and
       commonwealth
      Characterization of the future to improved policy decisions
      Facilitation of communication among regional planners
      Identification of regional needs through future scenarios
      Potential for quantifying policies better with future scenarios



The costs of implementing such a framework whose development is approached in this
report include:


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      Resources needed to survey and hold workshops for regions
      Time needed to update and adjust regional long term planning standards
      Resources to accommodate shift in planning approach
      Time and resources needed to coordinate efforts regionally

                             ACKNOWLEDGMENTS

       We would like to thank members across Virginia‟s transportation agencies that
provided a wealth of information and advice, notably: Mary Lynn Tischer of the
Multimodal Office, Kimberly Spence, Katherine Graham, and Michael Garret of VDOT,
Mark McCaskill of RVAMPO, Wayne Ferguson and John Miller of the Virginia
Transportation Research Council, members of the Multimodal Advisory Committee, and
Ralph Davis, Deputy Secretary of Transportation for Virginia.




DRAFT
                                   REFERENCES

TransAction2030 (2006). Transaction 2030: Transportation for Today and Tomorrow.
Northern Virginia Transportation Authority. 12 pp.

Bartholomew, K. (2005). Integrating Land Use Issues Into Transportation Planning:
Scenario Planning. Retrieved June 3, 2008, from http://nepa.fhwa.dot.gov/.

Cervero, R., & Aschauer, D. (1998). Economic impact analysis of transit
investments:Guidebook for practitioners. Retrieved October 30, 2007, from
http://trb.org/news/blurb_detail.asp?id=2582

Christopulos, D. (2007). Sierra Club Opposes Intermodal Transfer Station in Roanoke
Region. Retrieved December 3, 2007, from
http://virginia.sierraclub.org/roanoke/intermodal.pdf

Cole, Sam (2001). Dare to Dream: Bringing Futures into Planning. Journal of the
       American Planning Association, 67(4), 372 – 383.

Commonwealth of Virginia, Office of the Governor. (2007). Governor Kaine
Announces New Jobs for Arlington County-GridPoint, Inc. to Invest $5 Million
and Move Headquarters to Virginia. Retrieved November 24, 2007, from
http://www.yesvirginia.org/About_Us/NewsArticle.aspx?newsid=900.


Delaware Valley Regional Planning Commission. (2005). Regional Analysis of What-If
Transportation Scenarios. Retrieved October 31, 2007 from
http://www.dvrpc.org/LongRangePlan/2030/WhatIfFinal.pdf.


Environmental Protection Agency. (n.d.). Climate Change – Health and Environmental
Issues. Environmental Protection Agency. Retrieved December 12, 2007, from
http://www.epa.gov/climatechange/effects/extreme.html

Federal Highway Administration. (2007). FHWA Scenario Planning Initiatives.
Retrieved June 4, 2008, from
http://transportation.ky.gov/planning/Scenario/1%20McCullough%20FHWA.pdf

Federal Highway Administration. (2004). Scenario Planning Peer Workshop –
Binghamton, NY. Retrieved October 18, 2007, from
http://www.fhwa.dot.gov/planning/scenplan/nyscenplanrpt.htm

Federal Highway Administration. (n.d.). Scenario planning. Retrieved October 24, 2007,
from http://www.fhwa.dot.gov/planning/scenplan/about.htm



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ICF International. (2007). The Potential Impacts of Global Sea Level Rise on
Transportation Infrastructure. Retrieved June 6, 2008, from
http://climate.dot.gov/publications/potential_impacts_of_global_sea_level_rise

MIT Center for Transportation & Logistics and The Zaragoza Center at the ZLC. (2006).
Proceedings of the Supply Chain 2020 Project’s European Advisory Council Spring 2006
Meeting. Retrieved October 18, 2007, from
http://ctl.mit.edu/public/spring_2006_sc2020_eac.pdf

National Capital Region Transportation Planning Board. (2007). Financially Constrained
Long-Range Transportation Plan. Retrieved June 6, 2008 from http://www.
Mwcog.org/clrp

National Capital Region Transportation Planning Board and the Metropolitan
Washington Council of Governments. (2004). What If The Washington Region Grew
Differently?: The TPB Regional Mobility and Accessibility Scenario study. Retrieved
October 4, 2007, from
http://www.mwcog.org/transportation/activities/regional/documents/Generic%2
0for%20Web%207-07.pdf

Richmond Area Metropolitan Planning Organization. (2007). 2031 Long-Range
Transportation Plan. Retrieved July 5, 2008, from
http://www.richmondregional.org/Urban%20Transp-MPO/MPO_Div_Cats/toc.htm

Roanoke Valley Area Regional Commission. (2006). Pedestrian Access to Commercial
Centers: Connecting residential and commercial Land uses. Retrieved December 1,
2007, from http://www.rvarc.org/work/access06final.pdf

Roanoke Valley Area Regional Commission. (2006). Roanoke Valley-Alleghany
Regional Commission Pre-Disaster Mitigation Planning. Retrieved October 17, 2007
from http://www.rvarc.org/disaster/index.htm

Roanoke Valley Area Metropolitan Planning Organization. (2005). Planning for Elderly
and Disabled Mobility. Retrieved October 18, 2007, from
http://www.rvarc.org/work/mobilityfinal.pdf

Roanoke Valley Area Metropolitan Planning Organization. (n.d.). What happens when
the ‘baby boom’ generation retires? Retrieved November 3, 2007, from
http://www.rvarc.org/temp/retirement.pdf

The Roanoker Magazine. (2007) Where to Retire. Retrieved November 1, 2007, from
http://www.theroanoker.com/visitorguide/wheretoretire/index.cfm.

Rovner, N. (2008). Governor’s Commission on Climate Change. Presentation to the
Hampton Roads Planning District Commission.



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Thomas Jefferson Planning District Commission. (n.d.). Jefferson Area Eastern Planning
Initiative. Retrieved December 3 2007, from http://www.tjpdc.org/community/epi.asp

Transportation Accountability Commission. (2007). Final Report of the Transportation
Accountability Commission to the Governor and General Assembly of Virginia. Retrieved
November 3, 2007, from
http://www.transportation.virginia.gov/Initiatives/TransportationAccountability/Final_dra
ft%20_%20full_report_9_5_07.pdf

Virginia Department of Transportation. (2004). Virginia’s Statewide Multimodal Long-
Range Transportation Plan: Phase 3 and Final Report to the General Assembly.
Retrieved December 3, 2007, from
http://www.virginiadot.org/projects/vtrans/resources/revisedPhase3Reportforctb.pdf

Zergas, C., Sussman, J., Conklin, C. (2004). Scenario Planning for Strategic Regional
       Transportation Planning. J. Urban Planning and Development, 130(1), 2-13.




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     APPENDIX A: SCENARIO-BASED PLANNING DEMONSTRATIONS


Region R.2 - HAMPTON ROADS

Overview

Hampton Roads is located in southeast Virginia and borders the Chesapeake Bay as well
as the Atlantic Ocean. Figure 10 depicts the Hampton Roads Planning District
Commission boundaries.




Figure 10. Hampton Roads Planning District (Hampton Roads Planning District Commission, 2008)

The boundaries of the planning district are the focus for the case studies. The
geographical location of the area, along with the demographics and cities give the area
several unique characteristics. Multimodal transportation policies that consider increaing
population and employment will be considered, as well as the relevant scenario of sea
level rising.

Policies

The Hampton Roads area is a growing region and thus must account for the complexities
associated with population growth and employment shifts. For example, according to the
long term transportation plan (cite), there is an expected increase in population in the
area by over 442,000 between 2000 and 2030, an annual growth rate of 0.8%. Virginia
Beach will experience the largest locality increase in population, with an increase of over
105,000 people (17). In terms of population, The HRMPO area is expected to add an
additional 243,000 employees between 2000 and 2030, an annual growth rate of 0.8%
(18). The following policies were used in conjunction with the workbook tool:

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P.4 – Fund Rail

The Intermodal Management System Regional Study for Hampton Roads was published
in 2007 (cite). The study analyzes and summarizes the important of freight movement for
the Hampton Roads area.
        One of the major ways freight movement is performed is through rail. Four major
rail routes that connect the nations east-west rail system begin and end in Hampton roads.
According to the study, for “Hampton Roads to remain competitive in attracting new
business, it must facilitate the rapid and efficient movement of new materials and finished
products using…trains” (2). For example, $322 million project cost is predicted for
improving the Heartland Corridor (103). As shown in figure X, the dollars per ton for
Hampton Roads freight is expected to increase in the future.

  Table 1. Dollars Per Ton for Hampton Roads Freight (Intermodal Management System Regional
                                      Freight Study, 2007)




P.24 - Going Green

Environmental concerns are relevant to the Hampton Roads area. Given the high
population of the area and increasing freight activity, there is the potential for a high
amount of pollution. The Hampton Roads 2030 Transportation Planning report (cite)
contains a section for environmental mitigation activities. Potential policies and methods
for going green mentioned in the report include “avoiding impacts altogether,
minimizing a proposed activity/project size or its involvement, rectifying impacts
(restoring temporary impacts), precautionary and/or abatement measures to reduce
construction impacts, employing special features or operational management measures
to reduce impacts, and/or compensating for environmental impacts by providing
suitable, replacement or substitute environmental resources of equivalent or greater
value, on or off-site.” (99).

Scenarios

Because of the high population of the Hampton Roads area and the wealth of multimodal
transportation, there are several scenarios that should be considered in order to properly
prioritize transportation policies. The scenarios that this thesis analyzed were sea rise and
IT amenities growth.

S.20 - Sea Rise

DRAFT
One of the region‟s main concerns is global warming. Deputy Secretary of Transportation
for Virginia Ralph Davis gave a presentation on (some date) about the dangers of global
warming and the potential effects of climate change. (further info, numbers and stuff)
The Hampton Roads area is especially prone to the negative effects of global warming
given its environment. One of these effects is sea level rising.
         In 2007, ICF International published a report entitled “The Potential Impacts of
Global Sea Rise on Transportation Infrastructure”. The report covers the District of
Columbia, Maryland, North Carolina, and Virginia. Norfolk Harbor was identified as an
area of concern given the density of its economic activity and location. The figure below
is from a presentation by Nikki Rovner, Deputy Secretary of Natural Resources on the
Governor‟s Commission on Climate Change (2008). The dark blue areas represent areas
at-risk for flood damage.




            Figure 11. Risk Areas for Flood Damage around the Hampton Roads Area

The presentation discusses the effects of sea-rise and the areas most at risk in the
Hampton Roads Area, as well as throughout Virginia. Planners in the Hampton Roads
area should be consider this scenario when developing future transportation policies.

S.5 - Transit Oriented Development

In a survey sent out to Planning District Commissions in Virginia, the Hampton Roads
Planning District mentioned transit oriented development as an important scenario for
consideration in the future. One of the goals for the area in the long term transportation
plan is to “Increase the accessibility and mobility options available to people and for
freight. And set aside funding for mass transit projects.” Two current long term projects
in the Hampton Roads district are the Norfolk Light Rail and Peninsula Fixed Guideway.
Both these projects will help the area in increasing transit usage and capability.

Workbook Analysis
DRAFT
We used the background information and simulated the use of our workbook tool to
recommend a prioritization of multimodal transportation policies for Hampton Roads.

We used the background information and simulated the use of our workbook tool to
recommend a prioritization of multimodal transportation policies for Hampton Roads.
Figure 12 shows the weighting of the critieria against the future scenarios for the
Hampton Roads region.




  Figure 12. Survey Workbook Criteria Weighting over Scenarios for the Hampton Roads Region

The weighting of the criteria based on the scenarios relevant to Region R.2 – Hampton
Roads results in the prioritization of the five policies shown in figure X.




The lines extending from the points represent the minimum and maximum scores of the
policies using the scenarios S.5 – Transit Oriented Development and S.20 – Sea Rise.
Policy P.2 – Support Transit ranked the highest overall, however, the policy score

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overlapped with Policy P.4 – Fund Rail. Policy P.23 – Bicycle and Pedestrian Facilities
Feeder System and Policy P.24 – Going Green both scored lower than the other three
policies. The result does not suggest that these policies should not be considered, on the
contrary, these policies are important policies. The results indicate that these policies may
need to be examined for robustness in future scenarios and closely examined when being
combined with other policies and projects that implement the policies.

Conclusion

We found that several multimodal transportation policies score strongly against future
scenarios relevant to the region. Some policies score lower than others and thus may not
be as robust in future scenarios. The diversity of the Hampton Roads area poses many
challenges for transportation planners. As demonstrated in this case study, scenario-based
planning helps planners identify future factors and needs of the area that contribute to
current policy decisions.


Region R.3 - NORTHERN VIRGINIA

Overview

The northern Virginia area is one of the most populous in the commonwealth. The region
generates a significant portion of the wealth for Virginia. Regional planning in Northern
Virginia is managed by the Metro Washington Council of Governments. Figure 20
displays the boundaries of the Metro Washington Council of Governments.




Figure 20. Virginia Member Jurisdictions for Metro Washington Council of Governments
(http://www.mwcog.org/about/jurisdiction/, 2008)




DRAFT
        Fairfax, Loudon, Arlington and Prince William counties are included in the
region. The combined population of the four counties out of ninety five total
commonwealth counties sums to 1,863,407 (2006 census) or roughly 25% of the Virginia
population. As of January, 2008, Fairfax County, Loudon County, Arlington County, and
Prince William County rank first, second, ninth, and eighteenth respectively in a list of
the highest median in the nation. Along with the density and prosperity, the region also
contains a multitude of transportation planning issues.

Policies

P.2 - Support Transit

One of the five main strategies in the MWCOG vision plan is to encourage the use of
transit, especially for tourists. In the long range plan, there are 13 major transit related
project plans including Dulles Corridor Rapid Transit and increasing HOV lanes on the
National Capital Beltway.

P.22 – Increase Investment in Transportation

According to MWCOG, the traffic infrastructure in the region needs heavy maintenance.
Moreover, the highway system will not keep pace with the population growth in the
region as shown in Figure 21.




  Figure 21. Forecast Trends for Transportation Volume in Northern Virginia (National Capital
Region Transportation Planning Board and the Metropolitan Washington Council of Governments,
                                             2004)

As predicted population and jobs increase as shown by Figure 22 the area will continue to
need to invest in transportation.




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 Figure 22. Predicted Job and Household Growth Rate in MWCOG Jurisdiction (National Capital
Region Transportation Planning Board and the Metropolitan Washington Council of Governments,
                                            2004)



P.23 - Bicycle and Pedestrian Facilities Feeder System

Objective four in the TBP‟s vision plan is to create convenient bicycle and pedestrian
access. This policy also aligns with the Air Quality: Mobile Source Emissions plan
(National Capital Region Transportation Planning Board, 2007) in the long range plan.
There also several environmental iniativies in the area such as
http://www.commuterpage.com/ as presented at the Virginia Associate of Planning
District Commissions Summer Conference 2008.

Scenarios

S.5 – Transit Oriented Development

Smart growth, or higher density planning, is one of the concepts regional planners are
currently considering. In the long range plan (National Capital Region Transportation
Planning Board, 2007) indicates a vision of “healthy regional core and dynamic regional
activity centers with a mix of jobs, housing and services in a walkable environment.” The
scenario planning MWCOG document provides an example of potential “regional activity
centers” represented in red (National Capital Region Transportation Planning Board,
2007).




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 Figure 23. Scenario of Regional Centers (National Capital Region Transportation Planning Board
                 and the Metropolitan Washington Council of Governments, 2004)



S.4 - Job/House Shifts

Job and Households may shift in the region, especially in Fairfax county, where new jobs
and population is continually increasing. The jobs/houses may shift as a result of
commute times, as displayed in Figure 24.




   Figure 24. Commute Time During Morning Rush Hour in Northern Virginia Region and the
                  Metropolitan Washington Council of Governments, 2004)




S.5 - Transit Oriented Development (see survey)

Transit oriented development is a probable scenario that the region will face. It is
mentioned in the MWCOG “What If the Region Grew Differently?” Scenario planning
paper (2004). Figure 25 below represents potential extensions of transit in the region.


DRAFT
    Figure 25. Transit Oriented Development Scenario (National Capital Region Transportation
         Planning Board and the Metropolitan Washington Council of Governments, 2004)



The effect of transit oriented development may lead to more people moving closer to jobs
and vice versa. Housing and population shift are two major policy concepts that are
impacted by this scenario.

Workbook analysis

We used the background information and simulated the use of our workbook tool to
recommend a prioritization of multimodal transportation policies for Northern Virginia.
Figure 26 shows the weighting of the criteria against the future scenarios for the Norhtern
region.




 Figure 26. Survey Workbook Criteria Weighting over Scenarios for the Northern Virginia Region




The weighting of the criteria based on the scenarios relevant to Region R.3 – Northern
Virginia results in the prioritization of the five policies shown in Figure 27.

DRAFT
The lines extending from the points represent the minimum and maximum scores of the
policies using the scenarios S.4 – Region Divided and S.5 – Transit Oriented
Development. Policy P.2 – Support Transit ranked the highest overall, however, the
policy score overlapped with Policy P.1 – Invest More in Transportation. Policy P.4 –
Fund Rail scored lower overall than policy P.9 – Address Transportation/Land Use
Conflicts, however, it scored much higher in one of the scenarios and may need further
analysis during policy consideration and prioritization.

Conclusion

In conclusion, the northern Virginia area is a highly populated and prosperous area that
faces many transportation-related challenges in the future. The Metro-Washington
Council of Governments has used scenario-based planning in the form of normative
scenarios to help anticipate and improve future transportation conditions. Planning for the
future is an integral activity in this region as the region is crucial to the economy of the
commonwealth.

RICHMOND

Overview

The Richmond region is considered one of “the big three” among the Virginia
Association of Planning District Commissions due to its population. The city of
Richmond is home to the capital of the commonwealth as well as the governors office.
The city of Richmond deals with similar issues as Northern Virginia, though considers
different geographical situations. Figure 28 shows the areas associated with the
Richmond region.




DRAFT
                Figure 28. Richmond Planning District Commission Jurisdiction
                        (http://www.richmondregional.org/default.htm)



Policies

P.1. – Invest More in Transportation

Invest more in transportation is one of the policies transportation planners chose in the
scenario-based planning survey. Table 2 shows the planned spending by the region for
the long-range 2031 transportation plan. (2007)

            Table 2. 2031 Long-Range Transportation Plan Estimated Funds ($1,000's)
       (http://www.richmondregional.org/Urban%20Transp-MPO/MPO_Div_Cats/toc.htm)




P.6 - Strengthen Planning and Modeling

Strengthen planning and modeling was also selected by the Richmond regional
transportation planners in the survey. Planning and modeling standards are emphasized


DRAFT
by SAFETEA-LU regulations which require Metro Planning Organizations (MPOs)
within PDC‟s to create and adhere to long range plans.

Scenarios

Scenarios:

S.2 - Sprawl accelerates

Sprawl acceleration is a common scenario in regions with large cities, such as Richmond.
Forecasts by the region show an overall predicted population increase of 56.5% and a
percent change in households of 61.9% as shown by Table 3.

                    Table 3. Population and Households - Richmond Region
                      (http://www.richmondregional.org/Urban%20Transp-
                   MPO/MPO_Div_Cats/lrtp_files/Chapter_6_August_2008.pdf)




S.10 – In-Migration Increases

Increased urban population accompanies the previous scenario of sprawl acceleration.
Both scenarios may very well happen in conjunction as well as independently. The
Region predicts a 3.4% increase in population for Richmond in 2031. Figure 29 shows
the increased population density forecast for 2031, with high density increases predicted
in Richmond with the higher density in darker red and Richmond focused in the
rectangle.




DRAFT
           Figure 29. 2031 Predicted Population Density Forecast for Richmond Region
                        (http://www.richmondregional.org/Urban%20Transp-
                    MPO/MPO_Div_Cats/lrtp_files/Chapter_6_August_2008.pdf)

Workbook analysis

We used the background information and simulated the use of our workbook tool to
recommend a prioritization of multimodal transportation policies for Richmond. Figure
30 shows the weighting of the criteria against the future scenarios for the Richmond
region.




Figure 30. Survey Workbook Criteria Weighting over Scenarios for the Richmond Region

The weighting of the criteria based on the scenarios relevant to Region R.4 – Richmond
results in the prioritization of the five policies shown in figure 31.




DRAFT
The lines extending from the points represent the minimum and maximum scores of the
policies using the scenarios S.2 – Sprawl Accelerates and S.10 – In-Migration increases.
Policy P.2 – Support Transit ranked the highest overall, however, the policy score
overlapped with Policy P.1 – Invest More in Transportation. Policy P.4 – Fund Rail
scored lower overall than the two other policies; however, the policy maximum scores
overlapped the scores of the top two policies. Policy P.6 – Strengthen Planning and
Modeling is the second highest scoring policy and is robust against the two scenarios
which may make the policy a higher priority for planners.

Conclusion

Several of the policies evaluated using the workbook tool had similar overall scores but
varied with future scenarios. The variation of the impacts of regional scenarios on
statewide policies indicates the significance of scenario-based planning. The Richmond
region contains many similar challenges to Northern Virginia but differs in terms of
demographics and geography. Planning and investment in transportation stand as
important policies with the consideration of future scenarios that may affect
transportation.




DRAFT
 APPENDIX B: SURVEY AND ANALYSIS OF SCENARIO-BASED PLANNING
                       BEST PRACTICES


Example of Survey Sent to Metropolitan Planning Organizations




DRAFT
DRAFT
  
  
  
  
  


  
  
  
  


  
  


  
  
  


  
  




DRAFT
DRAFT
DRAFT
DRAFT
DRAFT
                           Thank you for your participation.


Analysis of Survey



The following PDCs and MPOs responded:
     George Washington Regional Commission
     Hampton Roads PDC
     National Capital Region
     Region 2000
     Blacksburg, Christiansburg, Montgomery Area MPO
     Roanoke Valley-Alleghany Regional Commission
     Northern Shenandoah Valley Regional Commission
     Crater Planning District Commission
     Lenowisco Planning District Commission
     Richmond Regional Planning District Commission

We recorded and analyzed the quantifiable results of the survey. To analyze the regional
differences in Virginia, we included analysis of the west and east PDCs and MPOs. The
west PDCs/MPOs include: Region 2000, Northern Shenandoah Valley Regional
Commission, Blacksburg/Christiansburg/Montgomery Area MPO, Roanoke Valley-
Alleghany Regional Commission, and Lenowisco Planning District Commission. The
east PDCs/MPOs include: Hampton Roads PDC, National Capital Region, Crater
Planning District Commission, George Washington Regional Commission, and
Richmond Regional Planning District Commission. Figures 14 and 15 show the overall
percent relevance of scenarios and policies to the PDCs/MPOs.
                 100.00%
                 80.00%
   Percent Use
                 60.00%
                 40.00%

                 20.00%
                  0.00%
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                                        Figure 14. Overall Relevant Policy Percentage




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                                       Figure 15. Overall Relevant Scenario Percentage

The policies P.23 - Bicycle and Pedestrian Facilities Feeder System, P.1 - Invest more in
transportation, and P.2 - Support Transit stand out in the survey as being overall
important policies in the commonwealth of Virginia. The scenarios of S.8 - Energy cost
rises and S.2 - Sprawl accelerates were found to be important in Virginia with a 70% and
60% use in the PDCs/MPOs surveyed.
Figures 16 and 17 show the breakdown of relevant policies for the east and the west
region.


                 100.00%
                  80.00%
   Percent Use




                  60.00%
                  40.00%
                  20.00%
                   0.00%
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                                                                    .                        nf.



                                   Figure 16. Eastern PDCs/MPOs Policy Importance




                 100.00%
  Percent Use




                 80.00%
                 60.00%
                 40.00%
                 20.00%
                  0.00%
                           P            S         I                          In        C       A        I                    S
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                                                                                                                                              .

                                  Figure 17. Western PDCs/MPOs Policy Importance

For the eastern PDCs/MPOs, P.1 - Invest more in transportation and P.23 - Bicycle and
Pedestrian Facilities Feeder System appear to be important, being picked 100% and 80%
Feeder System and P.2 - Support Transit are the most important, with 100% and 80% use.




                                                                                         Figures 18 and 19 show the breakdown of relevant scenarios for the east and west region.




                                                                                                                                                                                                                                                                         ts                                                                                                                                              ts
                                                                                                                                                                                                                                                                      in                                                                                                                                              in
                                                                                                                                                                                                                                                                   ra                                                                                                                                             tra
Technology appear to be more important in the west, whereas increase investment in




                                                                                                                                                                                                                                                                st
respectively. For the western PDCs/MPOs, P.23 - Bicycle and Pedestrian Facilities




                                                                                                                                                                                                                                                             on                                                                                                                                                ns
                                                                                                                                                                                                                                                           C                                                                                                                                                Co w s
In comparing the two areas, the policy P.7 - Manage Access and P.13 - Invest in




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transit and strengthen planning and modeling is more important in the east.




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                                                                                                                                                                                                                                          Po se e G os                                                                                                                                    G tr . I                   s




                                                                                                                                                                                                                                                                                                                                                                                                                              Figure 19. Western PDCs/MPOs Scenario Importance
                                                                                                                                                                                                                                                                                 Figure 18. Eastern PDCs/MPOs Scenario Importance
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                                                                                                                                                                                                                                                                                                                                                                                  0.00%
                                                                                                                                                                                                                                                                                                                                    100.00%
                                                                                                                                                                                                                                                                                                                                              80.00%
                                                                                                                                                                                                                                                                                                                                                       60.00%
                                                                                                                                                                                                                                                                                                                                                                40.00%
                                                                                                                                                                                                                                                                                                                                                                         20.00%
                                                                                                                                                                                                                                  0.00%
                                                                                                                                                                                    100.00%
                                                                                                                                                                                              80.00%
                                                                                                                                                                                                       60.00%
                                                                                                                                                                                                                40.00%
                                                                                                                                                                                                                         20.00%
For the eastern PDCs/MPOs the relevant scenarios are S.9 - Infrastructure investment
expands, S.8 - Energy cost rises, S.2 - Sprawl accelerates, S.5 - Transit oriented
development, and increased overall public transit. In the west, the scenarios of S.8 -
Energy cost rises, S.2 - Sprawl accelerates, and S.17 - Retirement are relevant to the
PDCs/MPOs. The scenario S.9 - Infrastructure investment expands is much more
important in the east than in the west (80% importance vs. 0% importance). The scenario
S.17 - Retirement appears to be more important in the west than the east (60% importance
vs. 0% importance).

We also recorded and analyzed the questions in the survey considering the effect of
scenarios and regional use of scenario-based planning. The results of the scenarios show
the regional differences in policy needs and future scenarios. Many regions are using
scenario-based planning in a variety of ways. Thus, it is crucial that there is coordination
among regions during policy prioritization and long term planning. Standardized
scenario-based planning will aid the region in planning coordination.
               APPENDIX C: ECONOMIC IMPACT PORT STUDY

TO: Dr. Mary Lynn Tischer, Multimodal Office

FROM:

Megan Kersh
Asad Saqib
Matthew Schroeder
Ward Williams
Professor James Lambert

University of Virginia
Center for Risk Management of Engineering Systems

DATE: January 28, 2008

SUBJECT: The impacts of increasing output in transportation on final demand/GDP in
Virginia

Executive Summary

We analyzed the impacts of transportation investment in Virginia and to overall output
and final demand in sixty sectors of the economy.

For this study, we purchased data from the Bureau of Economic Analysis
(http://www.bea.gov/regional/rims/index.cfm). The data set was released in 2005 and is
the most recent data available.

Our observations from Bureau of Economic Analysis data are as follows:

    The seven transportation sectors are interconnected with the sixty sectors through
     the expression x = Ax + c (expressed per year).
         o x - total output per year (million dollars)
         o A - input output matrix for output recycled to each sector [60 x 60]
         o Ax – recycled output into other sectors per year (dollars)
         o c - "final demand" output of the economy per year or GDP(dollars)
                  GDP = consumption + investment + (government spending) +
                    (exports − imports)
    Output in Virginia is broken down into 60 sectors for this problem.
    Of the sixty sectors the total output is $226B
    Of the sixty sectors, the final demand is $128B
    The seven transportation sectors are
         o Air transportation
         o Rail transportation
          o Water transportation
          o Truck transportation
          o Transit and ground passenger transportation
          o Pipeline transportation
          o Other transportation and support activities
      Output for the seven transportation sectors is $7.2B
      The seven transportation sectors constitute 2.5% of total output
      Final demand for the seven transportation sectors is $3.2B
      The seven transportation sectors constitute 3.2% of the final demand
      This database does not distinguish between public and private investment or
       between services and construction.

We found the following in complementary analysis:

    Stimulating total output: A 1% increase to transportation output, x, results in a
     $37M increase to final demand, c, across all sectors
        o The 1% increase in transportation output also resulted in over a 1%
            increase in final demand for all transportation sectors
    Stimulating final demand: (say something about graph results)
        o Mention difference between stimulating demand and increasing output
Results of Increasing Transportation Output

We analyzed the results of increasing transportation output by raising transportation
output in all the transportation sectors by 1%. Table 4 provides the sectors and increased
output in millions of dollars.

Table 4 - Transportation sector increase in output
                                                                          Increase in
                                                     Sector ouput         sector output
 Sector                                              ($ millions)         ($ millions)
 Air transportation                                          1801.7                    18.0
 Rail transportation                                          699.3                     7.0
 Water transportation                                         292.0                     2.9
 Truck transportation                                        2538.1                    25.4
 Transit and ground passenger
 transportation*                                                  344.1                   3.4
 Pipeline transportation                                           80.0                   0.8
 Other transportation and support
 activities*                                                    1443.3                   14.4

Using the formula x= Ax + c, new c values (output or final demand) values were
calculated. Figure 32 displays the increased output for the respective transportations
sectors.

           30
           25
           20
 millions 15

           10
            5
            0
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Figure 32 – Increase in output for transportation sectors given 1% increase in output

The total output increase for all transportation sectors is 64 millions dollars. The total
output increase for all sectors in Virginia is 37 million dollars. The difference between
the transportation sector gains and overall gains is due to increased transportation output
absorbing output from other sectors. It is important to note that there is still a strong
overall increase in output overall when increasing output for transportation by 1%.
Results from Stimulating Final Demand

We use the formula x = Ax + c to exchange the desired unknown c value with x. After
manipulating the formula we obtain c(I-A)-1 = x.

When stimulating demand, we found that output for all sectors increases, in contrast to
the first approach, which was increasing output for specific sectors. Figure 33 shows the
ten sectors with greatest percentage increase in output based on a 1% increase in final
demand in the transportation sectors.



                    3.50%
                    3.00%
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Figure 33 – Percent increase in output per 1% increase in final demand for transportation sectors



These following issues should be considered when deciding whether to increase output or
attempt to stimulate demand:
     Ease of stimulating demand in a sector compared to ability to increase output for a
       sector
            o Stimulating final demand for transportation sectors by 1% results in a 3%
               increase in petroleum and coal manufacturing output, however, it must be
               considered if this output is feasible
     Need to increase output for many sectors
            o Stimulating demand has a greater overall positive effect in increasing
               output over a variety of sectors
     Specific sectors that currently may be in higher demand or have a higher ability
       for production/output
            o For example, rail as a form of public transportation has become more
              popular in past years, thus making it easier to stimulate demand for the
              sector


(Appendice within appendice)
Results from increasing output across all manufacturing sectors

For comparison, we increased all manufacturing sector output values by 1% as described
in Table 5.

Table 5 – Manufacturing sector increase in output
                                                          Sector ouput Increase in sector
 Sector                                                   ($ millions)   output ($ millions)
 Wood product manufacturing                                       1805.2                18.1
 Nonmetallic mineral product manufacturing                         910.5                 9.1
 Primary metal manufacturing                                       753.3                 7.5
 Fabricated metal product manufacturing                           1642.4                16.4
 Machinery manufacturing                                          1774.7                17.7
 Computer and electronic product manufacturing                    2500.6                25.0
 Electrical equipment and appliance
 manufacturing                                                     749.1                  7.5
 Motor vehicle, body, trailer, and parts
 manufacturing                                                    1562.2                 15.6
 Other transportation equipment manufacturing                     2916.9                 29.2
 Furniture and related product manufacturing                      1424.0                 14.2
 Miscellaneous manufacturing                                       674.6                  6.7
 Food, beverage, and tobacco product
 manufacturing                                                    5960.6                 59.6
 Textile and textile product mills                                1585.6                 15.9
 Apparel, leather, and allied product
 manufacturing                                                     378.4                  3.8
 Paper manufacturing                                              2006.4                 20.1
 Printing and related support activities                          1482.3                 14.8
 Petroleum and coal products manufacturing                         127.7                  1.3
 Chemical manufacturing                                           2238.5                 22.4
 Plastics and rubber products manufacturing                       2083.7                 20.8


Table 6 provides output increases for the manufacturing sector compared to the
transportation output increases.

Table 6 - Comparison of transportation and manufacturing increased output results
                              Change in final demand for Change in final demand
                              transportation sectors per   for all sectors per one
 Agglomerated Sector          one unit change in output    unit change in output
 Transportation                                       0.89                       0.52
 Manufacturing                                          0.66                       0.37

The first column represents the increased final demand for the respective agglomerated
sector per one unit change in output for the agglomerated sector. Another interpretation
for the first column is an increase in final demand per $1 increased output in the
agglomerated sector. The second column represents the sum of increased final demand
for all sixty sectors per one unit output increase for the agglomerated sector. We found
that the transportation sector produces more final demand per increased output than
manufacturing, or in other words, requires less output increase to produce a comparable
level of final demand.

To justify the one percent increase in output, we calculated the resulting change in output.
The output change is displayed in Table 7.

                 Table 7 - Percent Increase of Output for Transportation Sectors
                                                                       Percent
                                     Sector                            change
                               Air transportation                       1.6%
                              Rail transportation                       1.7%
                             Water transportation                       1.2%
                             Truck transportation                       2.2%
                 Transit and ground passenger transportation*           1.5%
                            Pipeline transportation                     1.1%
                  Other transportation and support activities*          3.1%

Every percent increase is above 1%, the amount of increase in output. In the
manufacturing sector, five industries had less than 1% return.

Based on the data analysis above, it is predicted that increasing output in the
transportation sector will result in a beneficial output. Further analysis may be done to
focus on which transportation sectors provide the best output per increased output and
how other sector‟s increased outputs compare to transportation.
     Figure 34 – Input/Output sector output (A matrix)




                                         Table 8 - Sector Output (x)
Sector Codes      Sector Description                                      Sector ouput
                                                                          ($ millions)
CROP              Crop and animal production                                         920
FRST              Forestry, fishing, and related activities                          581
OILG              Oil and gas extraction                                             492
MING              Mining, except oil and gas                                         811
MINS              Support activities for mining                                        82
UTIL              Utilities*                                                       1,907
CNST              Construction                                                    19,147
WOOD              Wood product manufacturing                                       1,805
NMET              Nonmetallic mineral product manufacturing                          910
PMET              Primary metal manufacturing                                        753
FMET              Fabricated metal product manufacturing                           1,642
MACH              Machinery manufacturing                                          1,775
COMP              Computer and electronic product manufacturing                    2,501
ELEC              Electrical equipment and appliance manufacturing                   749
MOTR              Motor vehicle, body, trailer, and parts manufacturing            1,562
TREQ              Other transportation equipment manufacturing                     2,917
FURN              Furniture and related product manufacturing                      1,424
MFGM              Miscellaneous manufacturing                                        675
FOOD              Food, beverage, and tobacco product manufacturing                5,961
TEXT              Textile and textile product mills                                1,586
APPR      Apparel, leather, and allied product manufacturing                     378
PAPR      Paper manufacturing                                                  2,006
PRNT      Printing and related support activities                              1,482
PETR      Petroleum and coal products manufacturing                              128
CHEM      Chemical manufacturing                                               2,239
PLAS      Plastics and rubber products manufacturing                           2,084
WTRD      Wholesale trade                                                      9,557
RTRD      Retail trade                                                        16,126
AIRT      Air transportation                                                   1,802
RAIL      Rail transportation                                                    699
WATR      Water transportation                                                   292
TRCK      Truck transportation                                                 2,538
GRND      Transit and ground passenger transportation*                           344
PIPE      Pipeline transportation                                                 80
TRNM      Other transportation and support activities*                         1,443
WRHS      Warehousing and storage                                                754
PUBL      Publishing including software                                        3,557
MPIC      Motion picture and sound recording industries                          250
BRDC      Broadcasting and telecommunications                                  7,846
INFO      Information and data processing services                             4,382
BANK      Federal Reserve banks, credit intermediation and related services    6,102
SECU      Securities, commodity contracts, investments                         1,947
INSR      Insurance carriers and related activities                            4,350
FUND      Funds, trusts, and other financial vehicles                            873
REAL      Real estate                                                          5,566
RENT      Rental and leasing services and lessors of intangible assets         1,792
PROF      Professional, scientific, and technical services                    39,096
MNGT      Management of companies and enterprises                              8,697
ADMI      Administrative and support services                                  8,481
WSTE      Waste management and remediation services                              492
EDUC      Educational services                                                 3,080
HLTH      Ambulatory health care services                                      9,515
HOSP      Hospitals and nursing and residential care facilities                8,373
SOCL      Social assistance                                                    1,747
PERF      Performing arts, museums, and related activities                       852
AMST      Amusements, gambling, and recreation                                   958
ACCO      Accommodation                                                        1,541
FSRV      Food services and drinking places                                    6,372
OTHR      Other services*                                                      9,611
       60 Households
              APPENDIX D: DRAFT OF POTENTIAL MPO SURVEY

We also created a draft of a survey that transportation planners can fill out to help
identify scenario-based planning in the MPO‟s. The preliminary survey is shown below.

Survey

Survey Tool Draft:
Survey Tool – Scenarios - Example

Given the following criteria –

C.1 Safety and Security
C.2 Preservation and Management
C.3 Efficient Movement of People and Goods
C.4 Economic Vitality
C.5 Quality of Life
C.6 Program Delivery

What kind of impact would these scenarios on your area? (please highlight your choice)

S.2 – Sprawl Accelerates

         C.1 Safety and Security
              (Significant Impact) (Moderate Impact) (None or Unknown Impact)

         C.2 Preservation and Management
              (Significant Impact) (Moderate Impact) (None or Unknown Impact)

         C.3 Efficient Movement of People and Goods
              (Significant Impact) (Moderate Impact) (None or Unknown Impact)

         C.4 Economic Vitality
              (Significant Impact) (Moderate Impact) (None or Unknown Impact)

         C.5 Quality of Life
             (Significant Impact) (Moderate Impact) (None or Unknown Impact)

         C.6 Program Delivery
              (Significant Impact) (Moderate Impact) (None or Unknown Impact)

S.17 – Retirement

         C.1 Safety and Security
              (Significant Impact) (Moderate Impact) (None or Unknown Impact)
      C.2 Preservation and Management
           (Significant Impact) (Moderate Impact) (None or Unknown Impact)

      C.3 Efficient Movement of People and Goods
           (Significant Impact) (Moderate Impact) (None or Unknown Impact)

      C.4 Economic Vitality
           (Significant Impact) (Moderate Impact) (None or Unknown Impact)

      C.5 Quality of Life
          (Significant Impact) (Moderate Impact) (None or Unknown Impact)

      C.6 Program Delivery
           (Significant Impact) (Moderate Impact) (None or Unknown Impact)

S.18 – Natural Disaster

      C.1 Safety and Security
           (Significant Impact) (Moderate Impact) (None or Unknown Impact)

      C.2 Preservation and Management
           (Significant Impact) (Moderate Impact) (None or Unknown Impact)

      C.3 Efficient Movement of People and Goods
           (Significant Impact) (Moderate Impact) (None or Unknown Impact)

      C.4 Economic Vitality
           (Significant Impact) (Moderate Impact) (None or Unknown Impact)

      C.5 Quality of Life
          (Significant Impact) (Moderate Impact) (None or Unknown Impact)

      C.6 Program Delivery
           (Significant Impact) (Moderate Impact) (None or Unknown Impact)


S.3 – IT Amenities Grow

      C.1 Safety and Security
           (Significant Impact) (Moderate Impact) (None or Unknown Impact)

      C.2 Preservation and Management
           (Significant Impact) (Moderate Impact) (None or Unknown Impact)

      C.3 Efficient Movement of People and Goods
           (Significant Impact) (Moderate Impact) (None or Unknown Impact)
      C.4 Economic Vitality
           (Significant Impact) (Moderate Impact) (None or Unknown Impact)

      C.5 Quality of Life
          (Significant Impact) (Moderate Impact) (None or Unknown Impact)

      C.6 Program Delivery
           (Significant Impact) (Moderate Impact) (None or Unknown Impact)

S.19 – Decrease in Air Quality

      C.1 Safety and Security
           (Significant Impact) (Moderate Impact) (None or Unknown Impact)

      C.2 Preservation and Management
           (Significant Impact) (Moderate Impact) (None or Unknown Impact)

      C.3 Efficient Movement of People and Goods
           (Significant Impact) (Moderate Impact) (None or Unknown Impact)

      C.4 Economic Vitality
           (Significant Impact) (Moderate Impact) (None or Unknown Impact)

      C.5 Quality of Life
          (Significant Impact) (Moderate Impact) (None or Unknown Impact)

      C.6 Program Delivery
           (Significant Impact) (Moderate Impact) (None or Unknown Impact)
                   APPENDIX E: PRELIMINARY MPO SURVEY


We surveyed the regional MPO‟s to analyze how they use scenario based planning in
their transportation planning. Some MPO‟s used scenarios directly, and others used
scenario-based planning indirectly. Table 9 below shows the result of the MPO survey.

                             Table 9. Results of user MPO survey
MPO                              Use of Scenarios in
                                 Planning
1. Blacksburg-Christiansburg-    Some
Montgomery Area (BCM)
2. Central Virginia              Some
3. Danville                      Little
4 Charlottesville-Albemarle      Complete-direct
5. Richmond Area MPO             Some
6. Roanoke Valley Area           Complete-direct
MPO*
7. Tri-Cities Area MPO           Some
8. Fredericksburg Area           Some
MPO
9. Hampton Roads PDC             Little
10. Harrisonburg-Rockingham      Little
MPO
11. West Piedmont PDC            Little
(Danville MPO)
12. Winchester-Frederick         Little
MPO


We observed that smaller MPOs have less evidence of use of scenario-based planning,
whereas larger MPO‟s tend to use scenarios either directly or indirectly in planning.
Figure 34 displays an excerpt of a workbook that keeps track of the types of scenarios for
MPO‟s.




                  Figure 34. Excerpt from spreadsheet of MPO scenario usage

				
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