Analytical Support for the Statewide Multimodal Long-Range Transportation Plan:
Application To Heavy Rail Transport
A Thesis
In TCC 402
Presented to
The Faculty of the
School of Engineering and Applied Science
University of Virginia
In Partial Fulfillment
of the Requirements for the Degree
Bachelor of Science in Systems and Information Engineering
by
David Cowden
March 24, 2003
On my honor as a University student, on this assignment I have neither given nor
received unauthorized aid as defined by the Honor Guidelines for papers in TCC Courses.
Signed_______________________________
Approved______________________________
Technical Advisor- James H. Lambert
Date______________________
Approved______________________________
TCC Advisor – Betsy Mendelsohn
Date______________________
TABLE OF CONTENTS
ABSTRACT ...................................................................................................................... iii
CHAPTER 1: INTRODUCTION .................................................................................... 1
1.1 FEDERAL LEGISLATION ..................................................................................... 1
1.2 BACKGROUND ...................................................................................................... 1
1.3 TEAM PROJECT ..................................................................................................... 3
1.4 INDIVIDUAL CONTRIBUTION ............................................................................ 3
CHAPTER 2: LITERATURE REVIEW ....................................................................... 5
CHAPTER 3: AREAS OF IMPACT .............................................................................. 9
3.1 ECONOMIC IMPACTS ........................................................................................... 9
3.2 SOCIAL IMPACTS ................................................................................................ 10
3.3 POLITICAL IMPACTS.......................................................................................... 11
CHAPTER 4: METHODS AND MATERIALS .......................................................... 12
4.1 BACKGROUND RESEARCH .............................................................................. 12
4.2 METRIC DEFINITION .......................................................................................... 13
4.3 COMPARISON TOOL SOFTWARE .................................................................... 14
4.4 DATA GATHERING ............................................................................................. 15
4.5 HEAVY RAIL CASE STUDY ............................................................................... 16
CHAPTER 5: DATA AND RESULTS ......................................................................... 17
CHAPTER 6: INTERPRETATION AND ANALYSIS .............................................. 19
CHAPTER 7: CONCLUSION....................................................................................... 22
7.1 SUMMARY ............................................................................................................ 22
7.2 INTERPRETATION............................................................................................... 22
7.3 RECOMMENDATIONS ........................................................................................ 23
BIBLIOGRAPHY ........................................................................................................... 25
APPENDIX A: TEA-21 CRITERIA ............................................................................. 29
APPENDIX B: METRICS BROKEN DOWN BY MOTIVATION .......................... 31
APPENDIX C: GANTT CHART .................................................................................. 36
ii
ABSTRACT
In accord with the vision of VTrans2025 “to build a world-class multimodal
transportation system,” there is the need for analytical methods to improve the
communication and cooperation among the various modal transportation agencies of the
Commonwealth of Virginia (VTC 2002). This Capstone team provides systems analytical
support through the extension and application of a transportation project comparison tool
to multimodal systems involving aviation, transit, rail, port, and roadway projects. This
comparison tool represents the extent to which transportation projects and multimodal
systems promote economic development, intermodalism and mobility, quality of life,
fiscal responsibility, systems management, and transportation safety and security.
Through modal case studies, this team recommends metrics of multimodal system
performance while addressing a twenty-year planning horizon and ensuring that
technological initiatives are emphasized. For this report, a case study of the comparison
tool was completed through interaction with Virginia Department of Rail and Public
Transit, and the Virginia Department of Transportation. This thesis contains a heavy rail
case study that provides, methods for performance metric selection, sample project data
collection, and graphical displays illustrating project priority. The case study results
prove that it is possible to analytically prioritize heavy rail transportation improvement
projects in Virginia.
iii
CHAPTER 1: INTRODUCTION
1.1 FEDERAL LEGISLATION
The Intermodal Surface Transportation Efficiency Act (ISTEA) and the
Transportation Equity Act for the 21st Century (TEA-21) establish the need for states to
consider alternate transportation modes when planning and prioritizing projects
(Fontaine, Miller 2002). This legislation urges states to examine diverse collections of
transportation improvement projects that fit together into a larger multimodal framework.
Improved multimodal planning implies a balanced, well-integrated multimodal system
plan that facilitates better connection and communication among modes. The Virginia
Department of Transportation (VDOT) has requested that the UVA Capstone team help
the Commonwealth of Virginia by providing analytical support for the Multimodal LongRange Transportation Plan. Thus the need for a comparison tool to prioritize multimodal
systems has been established.
1.2 BACKGROUND
Policy makers in Virginia are currently troubled with the task of improving
transportation infrastructures in order to ensure a better economy, environment and
quality of life. Currently state transportation agencies receive limited funding to maintain
and improve local infrastructures and transportation systems. Because of this, thousands
of proposed construction projects compete for the same limited funds. In the past, agency
officials have based their prioritization decisions on subjective metrics such as, purpose,
scope, need, size and location of certain projects. As a result of this subjective decision-
1
making, planning officials have had a difficult time of selecting which potential projects
to undertake. Prioritization is defined as the selection of projects to be funded as well as
the order in which the projects should be undertaken. With the recent TEA-21 legislation,
U.S. states are now urged to follow certain guidelines for prioritizing improvement
projects that qualify for federal funding. Under this new mandate, states are more likely
to receive Federal funding if they comply with the TEA-21 guidelines. Therefore, it is
necessary for all transportation modes (aviation, transit, rail, port and roadway) to
implement a new method for comparing potential improvement projects (VTC 2002).
At the 2002 Virginia Transportation Conference (VTC), the Statewide
Transportation Steering Committee delivered the following statement, “Our vision is to
build a world-class multimodal transportation system that sets the standards for the rest of
the nation.” Along with this vision, transportation planners presented a plan called
VTrans2025. VTrans2025, the statewide multimodal long-range transportation plan,
encourages connectivity among all modes of transportation and is the result of
coordination between federal, state and regional agencies (VTC 2002).
Figure 1 contains a flow chart, which is part of the VTrans2025 plan that was
presented at the 2002 VTC. This chart shows a future plan where individual modes
submit project proposals to a planning committee that will then determine how well each
project fits into a multimodal system. A multimodal system by definition is a proposed
construction project that consists of two or more modes of transportation. Multimodal
systems will then be scored using a comparison tool. Individual projects that are part of a
larger system will receive bonus points in their respective modal priority selection. As a
2
result, only projects that benefit a larger multimodal system shall receive state and federal
funding.
1.3 TEAM PROJECT
Last year, the University of Virginia and the Virginia Transportation Research
Council developed a tool that presents the cost-benefit-risk tradeoffs in roadway
improvement projects. However, this tool was confined to prioritizing roadway projects.
By the recent requests of VDOT, Dr. James Lambert and my Capstone team including
Mohammed Ali, Brister Barrett, Kenneth Peterson, Ariel Pinta and Jared Zane are
working on a modified tool that prioritizes multimodal systems. This phase of work is
denoted by circle #2 in figure 1. This research helps transportation planners score
multimodal systems so that they can inform individual modes as to which projects should
receive funding.
1.4 INDIVIDUAL CONTRIBUTION
For an individual contribution, I performed a case study on heavy rail
transportation. Heavy rail consists of freight or cargo rail carriers and excludes passenger
rail transit. This step in the flow chart process is denoted by circle #1 in figure 1. Each
member of my Capstone team performed case studies on different modes of
transportation. Mohammed Ali worked with the Department of Aviation (DOAV), Brister
Barrett worked with the Virginia Port Authority (VPA), while Jared Zane and myself
worked with the Department of Rail and Public Transport (DRPT). This study consisted
of collecting and defining performance metrics that specifically apply to heavy rail
3
transportation. Next I collected information on sample improvement projects and entered
the data into the Capstone team’s comparison tool software. Finally, to increase VDOT’s
comprehension of the study, results were shown using various graphical representations.
Each State Project
Receives Bonus Points in
its Respective Modal Priority
Process
RankSystems
-Quantitative
-Qualitative
-Political
Each Mode Implements
Individual Priority Model
-Federal & State Requirements
-Governing Board
-Funding Source(s)
-Industry Measurements
Score each System using
Priority Model
2
Devlop Implementation Plan
-Schedule
-Lead Agency
-Source of Funding
Develop Transportation
Systems that have
Regional & State Interests
VPA
VDOT
DOAV
1
Legend
Review 6-Year Plans for
Eligible System Projects
Agency Actions
IMAT Actions
VDRPT
Figure 1: VTrans 2025 Flow Chart: Statewide Multimodal Planning (VTC 2002)
4
CHAPTER 2: LITERATURE REVIEW
Scientists, engineers, researchers and students have developed many different
methods for comparing projects as well as multimodal systems. A report prepared by
Michael Fontaine and John Miller in August 2002 entitled “Survey of Statewide
Multimodal Transportation Planning Practices” closely examines innovative practices
used by other states (Fontaine, Miller 2002). The report served as a starting point by
providing insight on the various techniques that can be used for prioritizing multimodal
systems. After reviewing other state’s methods, the team concentrated on performance
measure selection. For information on this process, a report from the 2002 Transportation
Research Board conference provided helpful assistance. The conference was entitled,
“Performance Measures to Improve Transportation Systems and Agency Operations.”
Information from this report included guidance for defining performance metrics,
implementing performance measures and choosing the “right” measure selection
(Conference Proceedings 26). The report also offered examples of performance metrics
chosen by other states. Another report provided by the Transportation Research Board
entitled, “Trends and Issues in Transportation” discusses the importance of performance
measure selection (TRB Field Visit Program 1997).
The current Capstone project is an extension of a comparison tool for roadway
projects that was created over the course of the past two years. Last year, Professor Jim
Lambert and his Capstone team worked on a comparison tool that allowed VDOT to
prioritize roadway projects. Figure 2 below shows a graphical display of their comparison
tool results (Lambert 2002).
5
Figure 2: Roadway Comparison Tool (Lambert 2002)
The display compares the crashes avoided (per year) to the travel time saved (in
minutes) for 30 different roadway projects. The size of the circles relates to the cost of
each project. Many of the metrics and methods used in the previous two years were
referenced and re-used in my research. The comparison method shown in Figure 2 serves
as a foundation for my research. The software used in Figure 2 was expanded is now used
for prioritizing projects within other modes of transportation. Kenneth Peterson, a
graduate BS/MS student, assisted our team in modifying the comparison tool. Kenneth
has also been working on different methods of graphically displaying the comparisons.
For my individual section concerning heavy rail transport, Wilbur Smith
Associates has been doing recent work on integrating rail lines with multimodal systems.
Wilbur Smith Associates is an international consulting organization providing specialized
professional services to the transportation industry. In a report entitled, “Railway
6
Services,” WSA discusses multimodal systems development. They believe that a systems
approach to transportation planning and engineering is fundamental to the successful
development of transportation infrastructure services (Railway Services 2002). In
addition they believe that a functional multimodal system can provide a well placed “info
structure” that facilitates the flow of information between the network operators
throughout the multimodal system (Railway Services 2002). They touch on ways to
decrease “choke points” or “bottle necks” through a process called intermodal correction.
They presented their future goals involving freight rail’s increasing presence in Virginia
at the 2002 Virginia Transportation Conference (VTC 2002). Another piece of literature
that was especially useful was the Freight-Rail Bottom Line Report compiled by the
American Association of State and Highway and Transportation Officials. The report
entitled, “Transportation: Invest in America” describes the nation’s freight rail system, its
issues, and its needs (AASHTO 2002). The report discusses concerns about the capacity
of the nation’s freight rail system and how it will try to keep pace with the expected
growth of the economy in the next 20 years. According to this repot, relatively small
public investment in the nation’s railroads can be leveraged into relatively large public
benefits (AASHTO 2002). Moreover, the report provides a picture of the benefits of
freight transportation to the nation and the value of strategic transportation investments.
Additional literature includes “An Aid To The Comparison of Major
Infrastructure Improvements” written by James Lambert (Lambert 2002). This report
discusses how communication and cooperation amongst the transportation agencies in a
state transportation system can be improved so that funds can be allocated in a more
efficient manner. A report entitled “Commonwealth Rail Issues” that was presented at the
7
2002 Virginia Transportation Conference discussed Virginia railroads’ stake in economic
development (VTC 2002). A National Cooperative Highway Research Program
(NCHRP) Report 436 entitled “Guidance for Communicating the Economic Impacts of
Transportation Investments” contains the results of research into communicating linkages
between transportation investments and economic performance (Ostria 1999). In addition
to the large amount of literature reviewed, policy-meeting minutes provided by Katherine
Graham, via e-mail, allowed our team to follow the progress of the Long Range Policy
Committee for Virginia. Our Capstone advisor also forwards copies of e-mail
correspondence among Virginia transportation planning officials. This is helpful since the
Long Range Policy Committee is often times changing their goals or scope and it is
important that our team remains well informed on current issues.
8
CHAPTER 3: AREAS OF IMPACT
Many economic, social and political impacts arise from the implementation of this
comparison tool. Groups affected will include, Virginia citizens, politicians and
transportation officials. Should the tool be applied to other states, there is potential for
every citizen in the U.S. to somehow be affected.
3.1 ECONOMIC IMPACTS
Virginia’s transportation facilities are major assets for the promotion of economic
growth. Railroads carry 16 percent of the nation’s freight by tonnage and make a
substantial contribution to the national economy as well as to the economies of most
states (AASHTD 2002). Freight rail provides shippers with cost effective transportation,
especially for heavy and bulky commodities and can be a critical factor in retaining and
attracting industries that are central to state and regional economies (AASHTD 2002).
Figure 3 shows future freight growth in volume as predicted by the U.S. Department of
Transportation in 2002. Currently, each mode of transportation in Virginia has many
proposed construction projects that require funding. For example, the Northern Virginia
2020 Transportation Plan estimates under-funded transportation needs over the next
twenty years at $15 billion or $750 million per year (NVTC). Although the amount of
federal funding that Virginia transportation agencies receive has increased from 35% in
1996 to 60% in 2000, this still leaves Virginia paying 40% of the annual transportation
costs (NVTC). Having an increase in the number of projects along with a decrease in the
amount of available funds is going to leave Virginia in a serious budgeting crisis.
9
Figure 3: Freight Activity: 1998, 2010 and 2020 (U.S. DOT, FHA 2002)
The proposed comparison tool alleviates this crisis by allowing transportation planning
committees to analytically choose which construction systems to undertake. This ensures
that taxpayers’ money goes towards the most important projects, thus decreasing the
amount of money that is spent on unnecessary improvements.
3.2 SOCIAL IMPACTS
The second impact of my research deals with various social aspects, specifically
human mobility. Human mobility is defined as the ease at which people are able to move
from one place to another. By using this comparison tool, agency officials are able to
fund individual projects or operations that are part of larger multimodal systems. This
allows people and cargo to quickly travel from mode to mode and experience fewer
10
problems or delays along the way. Network operators will be able to communicate more
effectively which will allow them to pool their resources, knowledge and data in order to
maximize efficiency. Furthermore, as the nation moves toward homeland security,
integrated multimodal systems that provide a better flow of communication between
modes will become essential components of national security.
3.3 POLITICAL IMPACTS
The third major impact involves political aspects. Historically, policy makers
have chosen to undertake projects in a subjective manner. In other words, purely
qualitative ranking methods were used. My comparison tool allows policy makers to
apply quantitative measurements so that all of their decisions can be data driven. This
alleviates some of the pressures felt by politicians when deciding on which transportation
projects will best benefit Virginia’s infrastructure. This tool provides policy makers with
a set of metrics or performance measures that pertain to all modes of transportation.
These metrics can be used to gather numerical data that will be displayed in a manner that
is comprehensible to politicians, transportation planners, and the general public.
11
CHAPTER 4: METHODS AND MATERIALS
For my individual contribution, I performed a case study on heavy rail
performance. Heavy rail consists of freight or cargo rail carriers and excludes passenger
rail transit. The study is based on the principle that using both quantitative and qualitative
measures in multi-criteria decision-making can provide efficient resource allocation. This
study investigated how a given set of heavy rail improvement projects can be analyzed
and prioritized. To increase VDOT’s comprehension of the results, I graphically
represented my findings using multivariate displays and graphical representations.
The activities completed during this research fall into five main steps:
Background Research, Metric Definition, Comparison Tool Modification, Data Gathering
and Case Study for Heavy Rail. I worked closely with my Capstone group up until the
last two steps when I performed some individual analysis.
4.1 BACKGROUND RESEARCH
In September, my Capstone team completed preliminary research on
transportation planning and prioritization methods. I spent time searching through
different state and national transportation websites and journals mentioned in the
literature review section in order to familiarize myself with how agency officials
currently attempt to prioritize multimodal projects. Three of the most useful sites are the
Virginia Department of Rail and Public Transit (www.vdrpt.com), the Virginia
Department of Transportation (www.vdot.com) and the Transportation Research Board
12
(www.trb.org). Kenneth Peterson explained how last year’s comparison tool works and
how he plans to modify it to prioritize multimodal systems. Furthermore, to gain a better
understanding of what VDOT expects of our Capstone team, I attended a steering
committee meeting on September 11 in Richmond to listen to agency officials discuss a
long-term multimodal transportation plan.
4.2 METRIC DEFINITION
After completing our background research, the team’s next goal was to compile
an extensive list of metrics that was general to all modes of transportation. Next we
grouped the metrics based on their relevance to the six motivations set forth in the TEA21 legislation. The motivations are: (Fiol 2002)
1.) Safe and Secure Transportation
2.) System Management
3.) Intermodalism and Mobility
4.) Economic Competitiveness
5.) Fiscal Responsibility
6.) Quality of Life
Placing the long list of metrics under the six TEA-21 motivations was a subjective
process and one in which there was no clear method to follow. The original list of metrics
is broken down my motivation in Appendix B. The next step was for each individual on
the team to compile a list of metrics that are mode specific and can be quantitatively
measured. For comparison tool analysis it was necessary for each of use to choose two
metrics per mode that we felt were the most significant. The two metrics that I chose are
track miles and number of active cars on-line per year. Track miles are defined as the
total track distance that is owned by a railroad company. This metric was chosen because
13
it quantitatively specifies the size of the various railroad companies in Virginia. When
ranking projects, this is an important performance metric because it indicates how much
of an impact an improvement project can have on any particular railroad. The Virginia
Railway Association as well as CSX and Norfolk Southern provided such data. Cars online were chosen as the second performance metric. Cars on-line are defined as the
number of active or moving cars that that complete one trip on any particular railroad
company’s track. This metric was chosen because it indicates the size and cargo capacity
as well as track usage of Virginia railroads. This data is collected by railroad companies
and is reported as a yearly statistic.
4.3 COMPARISON TOOL SOFTWARE
This step involved taking the roadway comparison tool display shown in Figure 4
and modifying it to compare projects from other modes. To achieve this we inserted the
six TEA-21 criteria as motivations for each project. Next we changed the data tables so
that different performance metrics could be used for different modes. The final
modification involved adjusting the scale on the coordinate axes since different
performance metrics have different quantitative data. Figure 4 shows the comparison tool
that was developed by last year’s Capstone team. This example has average daily traffic
(ADT) and number of crashes as the two selected performance metrics. It contains seven
attributes chosen by last year’s team that are very similar to the six motivations set forth
in the TEA-21 legislation. The bubbles in Figure 4 represent roadway improvement
projects. Further description of the tool is discussed in the heavy rail case study section.
14
Legend:
500
300
A
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e
bi ssib
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iy ity
/M
o
Crash Rate
700
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ns
Average Daily Traffic
Figure 4: Roadway Comparison Tool (Capstone Team 1999)
4.4 DATA GATHERING
To gather the necessary quantitative data presented quite a challenge. I first
contacted Billy Ketron the Virginia Department of Rail and Public Transportation in
October and he provided me with a list of fiscal year 2002 funding allocations and 20
year cost needs for 8 rail lines in Virginia. Next I found the Virginia High Speed Rail 6year plan, which contained data for various improvement project proposals (High Speed
6-Year Plan 2002). Additional information came from on-line databases at CSX and
Norfolk Southern web sites. CSX and Norfolk Southern are the two largest cargo rail
companies in Virginia. On November 4, 2002 I contacted Kevin Page who works with
15
rail transportation planning at VDRPT and he provided me with data on Rail Industrial
Access Funding from fiscal year 1999 to present. I also contacted Alan Tobias from
VDRPT on November 11, 2002 and he provided a summary of Virginia’s passenger and
freight rail projects. From each of these sources I extracted data in the following areas:
description of each project, project cost ($), leveraging a defined by state funding vs.
federal funding, track miles of existing system (miles owned by rail company), and total
freight cars on-line in existing system (per year). It is important to note that data gathered
for the two metrics applies to existing railroad systems that are requesting funding for
improvement projects. It would be ideal if actual data could be forecasted, however that
is one of the limitations of this research.
4.5 HEAVY RAIL CASE STUDY
After collecting all of my data I entered it into the modified comparison tool. In
addition to the quantitative data, it was necessary to assign two of the six TEA-21
motivations to each project. It is important to note that this is a subjective process. Since
it is uncommon for transportation agencies that are proposing the projects to provide the
correct motivations for their projects, it is therefore my duty to assign motivations to each
project. Finally, using Microsoft Excel software and Capstone Lab computers, I entered
the data and represented the results both graphically and statistically.
16
CHAPTER 5: DATA AND RESULTS
Figure 5 shows the data table from the comparison tool. From left to right, the
first column contains the project ID # which is an arbitrary number assigned to each
project. The next four columns contain cost ($ thousands), leveraging (% non-state), track
miles and cars on-line (per year). The next column contains the primary attributes or
motivations behind each project as defined by the TEA-21 legislation. The motivations
are denoted in abbreviated form but correspond to the following:






EC: Economic Competitiveness
FR: Fiscal Responsibility
IM: Intermodalism and Mobility
QL: Quality of Life
SM: Systems Management
SS: Safety and Security
The last column contains the system description, which is the title of each heavy rail
improvement project.
Figure 6 shows the graphical results from the comparison tool. Each bubble inside
the squares represents a heavy rail construction project and the size of the bubble
corresponds to the cost of that particular project. The TEA-21 motivations, which are
again denoted in abbreviated form, are found in columns in such a way that each project
is associated with two TEA-21 motivations. The coordinate graphic in the top right
corner of figure 6 shows the two performance metrics, track mile and cars on-line, along
with the scale for each metric. Further explanation of these results is presented in the
following Interpretation and Analysis section.
17
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DC-Richmond (Main St. Station)
Richmond-Newport News (Hampton Roads)
Richmond-Petersburg (Tri-Cities)
Petersburg-Norfolk(South Hampton Roads)
Petersburg-North Carolina Line
TransDominion Express
Virginia Railway Express
I-81 Rail Corridor
Buckingham Branch Railroad
Commonwealth Railway
Eastern Shore Railroad
North Carolina and Virginia Railroad
Norfolk and Portsmouth Belt Line RR
Shenandoah Valley Railroad
Virginia Southern Railroad
Winchester and Western Railroad
Total Freight Cars On-Line (Per Week)
Figure 5: Data Table For Heavy Rail Case Study
FR
EC
> 300000
250000
200000
150000
100000
50000
0
10
100
Track Miles
SS
SM
Q
L
IM
oj
e
Pr
0001
0002
0003
0004
0005
0006
0007
0008
0009
0010
0011
0012
0013
0014
0015
0016
Figure 6: Graphical Results For Heavy Rail Case Study
18
>
1000
CHAPTER 6: INTERPRETATION AND ANALYSIS
By examining the coordinate graph in figure 6, it is to be interpreted that a bubble
(i.e. project) falling in the upper right corner is much more desirable than a project falling
in the lower left. This is because a project in the upper right corner is part of a system that
has a large number of track miles and a large number of cars on-line, thus projects such
as this lead to larger impacts and benefits for an entire system. Furthermore, a small
bubble size indicates low cost, which is always an important and desirable feature. Figure
7 shows a zoom-in on the projects that are grouped at the intersection of the row and
column motivated by System Management and Intermodalism and Mobility. Two
important conclusions to draw from this graph are the fact that the majority of projects
(eight) are motivated by System Management and Intermodalism and Mobility and also
that over half of the projects seem to be very expensive as seen by the large size of the
bubbles. Further information can be drawn from figure 7. The bubble located the farthest
to the upper right corresponds to project 0008 which is the I-81 Rail Corridor
improvement project. By looking at the data table in figure 5, one can see that this
improvement project will cost $356,000,000, and will be built on a railroad that has 356
miles of track and 225,179 cars on-line. According to this comparison tool, this project
should receive highest priority since its impact will be larger. However, it is also
important to look at the cost and motivations of each project. Although the I-81 Rail
Corridor received a high priority, it is also very costly and is only motivated by System
Management and Intermodalism and Mobility. Improvement project 0011 requested by
the Eastern Shore Railroad corresponds to a smaller bubble(i.e. lower cost) and is
19
centered on 70 track miles and 108,760 cars on-line. By zooming in on squares
corresponding to other motivations, similar analysis can be performed in order to
determine project priority.
0008
0011
Figure 7: Zoom-In of Graphical Results
Analysis in figure 8 shows the total number of projects that fall under any two
motivations. Conclusions to draw from figure 8 are that System Management and
Intermodalism and Mobility motivate half of the total improvement projects. Similar
functions will display useful statistics such as total cost of all projects, as shown in figure
9, that correspond to any two motivations. Similarly, average cost, state funding, federal
funding and leverage can be examined.
20
1
FR
0
2
1
IM
0
0
0
0
QL
0
2
2
8
0
0
0
0
0
0
0
SS
EC
0
SM
0
Figure 8: Statistical Results: Total Number of Projects Corresponding to Each
Motivation
0
EC
0
37000
0
0
120687 20000
5
0
FR
IM
QL
0
0
0
0
0
444
6
708
8
99879
4
0
0
0
0
0
0
SM
0
SS
Figure 9: Statistical Results: Cost of Projects ($ thousands)
21
CHAPTER 7: CONCLUSION
7.1 SUMMARY
This report has shown that it is possible to analytically prioritize heavy rail
improvement projects through the use of comparison tool software. This report began
with a description of the future transportation requirements recently set forth by federal
legislation. It then discussed the different economic, political and social impacts that arise
as a result of this comparison tool design. This report has shown that it is important to
define performance metrics that can be used as standard measures across all heavy rail
improvement projects. Results from the case study showed that projects having a high
number of track miles or cars on-line are generally preferred to those projects that have a
low number of track miles and cars on-line. In addition, results showed that many
different things often motivate improvement projects and assigning only two motivations
to each project is very difficult.
7.2 INTERPRETATION
This report is significant because it provides analytical support for comparison of
major infrastructure investments. By improving prioritization methodology, funds given
to transportation infrastructure can be allocated in a more efficient manner and
multimodal transportation systems can be made more accessible and efficient for users.
This comparison tool allows up to 100 projects be compared simultaneously plus it easily
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adapts to other modes of transportation so that similar case studies can be performed. The
software is user friendly and provides clear direction on data input and analysis. One of
the limitations of this report lies in the fact that all of the projects used in the analysis are
future improvement projects, thus there is no way to check and see if my results are
indeed correct. However, this limitation is not critical since the “right” priority setting
does not usually exist in real life because there are so many unforeseen factors that can
arise in any improvement project. Beyond that, it is nearly impossible to accurately
predict usage and cost statistics for transportation systems that don’t yet exist. This tool
provides a framework for setting project priority. In the future, if the projects used in my
case study are actually funded and completed then it would be possible to compare my
priority setting with actual results.
7.3 RECOMMENDATIONS
This case study serves as a foundation for future work to come. The goal of this
Capstone team for the remainder of the semester and into next year is to integrate our
individual case studies into a multimodal comparison tool. This tool will attempt to
prioritize multimodal systems in much the same way that I prioritized heavy rail projects.
The team is currently working on defining a set of performance metrics that is general to
all transportation modes. Once this is completed, the team will further modify the
comparison tool, research multimodal systems in Virginia, collect data pertaining to the
systems and finally enter the data into the comparison tool and analyze the results. For
future work, examining actual multimodal transportation systems and having better
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access to performance statistics may enhance the utility of this comparison tool. The
long-term benefits of this research are in their beginning stages. Once the work presented
in this case study is integrated into a multimodal comparison tool, the results will provide
the residents of Virginia with first-rate transportation facilities that improve intermodal
connectivity and support economic growth across the Commonwealth.
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BIBLIOGRAPHY
Brich, Stephen C. Multimodal Transportation Planning in Virginia: Past Practices and
New Opportunities. Technical Assistance Report. Charlottesville: September
1994.
“Conference Program.” Virginia Transportation Conference - VTrans 2025. 16-18
October 2002. Lexington, Virginia.
Fiol, Marsha. “Goals and Objectives.” Virginia Department of Transportation.
Steering Committee Handout. 13 September 2002.
Fontaine, Micahel D. and Miller, John S. Technical Assistance Report: Survey of
Statewide Multimodal Transportation Planning Practices. Virginia
Transportation Research Council. Charlottesville, VA. August 2002.
Graham, Katherine A. Virginia Department of Transportation.
Available: Katherine.Graham@virginiadot.org
ISTEA. Transportation Equity Act For The 21st Century Plan. 21 September 2000.
Available: http://www.istea.org/guide/sm.htm
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Lambert, James H. Proposal For Research: An Aid To The Comparison Of Major
Infrastructure Investments. Center for Risk Management of Engineering Systems,
University of Virginia. 2002.
“Online” A Tool to Aid the Comparison of Highway Improvements. 13 September 2000.
1999 Capstone Team, University of Virginia.
Available: http://www.virginia.edu/~risk/VDOT/.
“Online” Trends and Issues in Transportation: Transportation Research Board’s 1997
Field Visit Program. 20 March 2002.
Available: http://gulliver.trb.org/publications/trnews/field2.html
“Online” CSX Intermodal.17 September 2002.
Available: http://www.csxi.com/content/index.cfm?fuseAction=whoWeAre
“Online” Freight Considerations For Local Transportation Systems. 17 September 2002.
Available: http://www.odot.state.or.us/intermodalfreight/Reports/Frt_consid_4tsps.pdf
“Online” Intermodal Association of North America (IANA). 7 December 2002.
Available: http://www.intermodal.org
“Online” Northern Virginia Transportation Alliance (NVTA). 16 October 2002.
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Available: http://www.nvta.org/transfunding.html
“Online” U.S. Department of Transportation: Office on Intermodalism. 22 October 2002
Available: http://www.dot.gov/intermodal/
“Online” Virginia High Speed Rail 6-Year Plan. September 2002.
Available: www.ctaonline.org/documents/vhsrdc.pdf
Ostria, Sergio J. “Need in Communicating the Economic Impacts of Transportation
Investment.” NCHRP Report 436: Guidance for Communicating the Economic
Impacts of Transportation Investments. 15 March 1999.
Available On-line: http://nationalacademies.org/
Peterson, Kenneth. University of Virginia. Graduate Systems Engineering Student.
August, 2002-Present.
“Railway Services.” Engineers Planners Economists. Wilbur Smith Associates.
Columbia, SC. October 2002.
Report of Conference Proceedings 26. Performance Measures to Improve Transportation
Systems and Agency Operations. Transportation Research Board. 2001
Available: http://gulliver.trb.org/publications/conf/reports/cp_26.pdf
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Spence, Kim. “Steering Committee Meeting” Richmond VA. September 11, 2002.
“Transportation Planning.” Northern Virginia Transportation Commission 2002
State Legislative Agenda. 6 December 2001.
Transportation: Invest in America. Freight-Rail Bottom Line Report. American
Association of State Highway and Transportation Officials (AASHTO).
2002.
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APPENDIX A: TEA-21 CRITERIA
Metric 1:
Safe and Secure Transportation
 Consider flexibility with design standards where it is appropriate and does not
degrade safety.
 Improve the safety locations where modes intersect.
 Promote education and enforcement of speed limits, driving restrictions, and other
travel-related regulations.
 Apply efficient and effective transportation security measures.
Metric 2:
System Management
 Maintain an efficient and reliable transportation system balancing safety and
effective operation.
 Promote the use of appropriate technology to maximize system effectiveness.
 Encourage development and implementation of planned access.
 Provide accessible and reliable traveler information.
Metric 3:
Intermodalism and Mobility
 Encourage intermodalism to maximize the accessibility, use, and efficient
connectivity of the overall transportation system.
 Provide effective and economical transportation choices and alternatives for
people and goods across the state.
 Improve communication and coordination among state, regional and local
transportation agencies.
 Expand freight planning
Metric 4:
Economic Competitiveness
 Provide for smooth and efficient transfers for passengers and freight between
ports, airports, railroads, and highways.
 Develop a transportation system that supports statewide economic development,
commerce, and tourism.
 Coordinate transportation planning and local land use planning.
Metric 5:
Fiscal Responsibility
 Ensure balance and effective transportation investments.
 Develop realistic transportation programs based on accurate cost estimates.
 Explore the use of alternate funding mechanisms on accurate (e.g. Public/Private
Transportation Act (PPTA), user fees, tax referenda, etc.).
 Increase the flexibility in the use of federal and state transportation funds.
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Metric 6:
Quality of Life
 Improve coordination and communication among stakeholders throughout the
transportation planning and implementation process and ensure that the process is
accessible for all communities and citizens.
 Enhance and protect the natural environmental quality and cultural and historic
resources.
 Ensure the compatibility of transportation service and the communities they serve.
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APPENDIX B: METRICS BROKEN DOWN BY MOTIVATION
SAFETY AND SECURITY
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Accident rate
Number of at-grade railroad/roadway crossings
Number/type of geometric deficiencies
Passenger fatalities per million passenger trips
Passenger injuries per million passenger trips
Claims as % of freight costs
Number of highest-risk runway incursions per 100,000 operations
Number of deficiencies in width/grade/alignment
Decrease in crash rate per DVMT
Decrease in fatality rate per DVMT
Decrease in injury rate per DVMT
Decrease in property damage per DVMT
Decrease in pedestrian fatalities per DVMT
High accident locations
Highway Pavement Condition Interstate Average Ride Index
Highway Pavement Condition NHS Average Ride Index
Highway Pavement Condition Primary Average Ride Index
Safety - Highway safety improvement program (HSIP) priority
Number of correctable crash sites – interstate
Number of correctable crash sites – NHS
Number of correctable crash sites – Primary
Decrease in crash rate per trip
Designation as an evacuation route
Assaults and robberies
Decrease in dollar value of cargo stolen in operations at the port
Decrease in percentage of cargo damaged in operations at the port
Decrease in dollar value of cargo damaged in operations at the port
Decrease in percentage of cargo stolen in operations at the port
Property damage per million trips
Project involves mode separation
Highway safety improvement program (HSIP) priority
Reduction in operational errors/ failures per million activities
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SYSTEM MANAGEMENT
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Percentage of truckload capacities utilized
Optimal mode selection
Passenger miles per train miles
Passengers per trip
Freight tons per trip
Percent of truckload capacities utilized
Freight bill accuracy
Percent of flights arriving more than 15 or 30 minutes late
Average departure delay per flight
Standard deviation of highway airport access travel time, weighted by the
distribution of trip ends
Departmental M&O costs and priority
Mode split
Service life
Maintenance requirements
Infrastructure condition
Maintenance costs per trip
Preservation costs per year
Surface Rehabilitation
Deficient Bridges
System Classification
Private Investment (in millions)
Net Present Value-Cost-Ratio (NPVC)
Incident management/evacuation route
INTERMODALISM AND MOBILITY
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Transfer time between modes
Number of intermodal transfer points
Number of modes involved
Maximum allowed speed
Frequencies of connections
Number of connections
Alternative modes
Intermodal transportation-mode connection
Intermodal transportation-redundancy reduction
Access management
Transit support amenities
Land use connectivity
Intermodal connectivity
Increase the number of available modes by
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Improves the level of service of existing modes
Minimum Level of Service (MLOS) required to meet the goals of the
transportation plan
MLOS required to meet the goals of the transportation plan for a user group
(elderly, disabled, etc)
Highway Pavement Condition Primary Average Ride Index
Highway Pavement Condition NHS Average Ride Index
Highway Pavement Condition Interstate Average Ride Index
Corridor congestion relief
Reduction in V/C Ratio
Segment Completions (connectivity of the same mode)
Existing congestion (existing v/c)
Horizon-year congestion (horizon year v/c)
Volume
Intersection delay
Travel time savings
Travel time savings per trip
Total riders
Total passenger or vehicle miles
Accessibility
People per square mile within 2-mile corridor
Pedestrian connections
Passenger mode to mode connections
Percent of air trips served by nonstop flights
Percent of air trips without nonstop service but served by connections through an
airline hub or one-stop service
Percent of air trips with at least 6 nonstop, one stop or connecting flights per day
Average delay experienced in traveling to and from the airport
Average delay experienced during the flight
Percent of air trips served by three or more carriers with nonstop, one stop or
connecting service
Percent of international departures with at least two carriers
Percent of air trips for which the nearest commercial airport provides direct or
connecting air service through one intermediate hub
Percent of air trips for which the nearest commercial airport provides direct jet
service to the destination
Percent of air trip ends within 45 min. highway travel time of the nearest
commercial service airport
Percent of air trip ends within 45 min. highway travel time of the nearest
commercial service airport used
Average airport access highway travel times under free-flow travel conditions,
weighted by the distribution of trip ends
Percent of air trip ends within five miles of stops served by scheduled airport
ground transportation services
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Percent of air trip ends in communities served by air port shared-ride van services
Percent of air passenger airport access trips using shared-ride public
transportation
Bicycle improvement
Bicycle connections
Tons moved per day
Shipping centers within 2-mile corridor
Freight mode to mode connections
Percentage increase in the number of containers shipped via rail or roads in
Virginia
Percentage decrease in container vacancy per vessel
Percentage increase in the number of containers that can be handled
Percentage increase in the number of vessels that can be handled
Percentage increase in the depth of the channel
Decrease in delay time from vessel arrival to the beginning of unloading
Decrease in delay time availability of vessel loading to actual loading
Decrease in average time to unload a container
Fuel consumption
Use of alternative energy sources
ECONOMIC COMPETITIVENESS
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Consistency with statewide goals
Priority in statewide goals
Job creation (jobs created)
Job retention (jobs retained)
Total revenue
Revenue per mile
Cost as a % to sales
Average fare paid per mile for interstate trips
Commercial airport productivity
Jurisdiction Unemployment Rate (Relative to Statewide Rate)
Ratio of State DOT Cost to Jobs Created (in $100,000)
Percentage increase in revenues
Consistency with MPO or PDC plan
Priority in MPO or PDC plan
Average fare paid per mile for air trips from Virginia to domestic destinations
outside the state
Average fare paid per mile to Virginia from domestic origin outside the state
Average fare paid per mile for air trips from Virginia to international destinations
Average fare paid per mile to Virginia from international origin
Local Needs Ranking: Where project falls as a % of all projects submitted in
regional needs ranking
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Number of potential transfer points
Total transfer time
Provides access to major intermodal facility, corridor, or activity center
FISCAL RESPONSIBILITY
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Projects dependent on this project
Percentage part of a greater financial investment
Sustainability of investment/life cycle cost
Funds already programmed for project
Local/Regional Dollars: Where the local contribution/capita for that projects falls
as a % of others being ranked
Project involves innovative funding mechanism
Project involves innovative use of funds
Project uses economies of scale in purchasing/contracting
Cost per length of project
Cost-benefit ratio
Cost per trip
Cost per vehicle/person/ton mile traveled
Price of dredging per square foot per foot deep
Decrease in costs per container handled
QUALITY OF LIFE
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Transportation alternatives available
Environmental justice community served
Transportation-challenged community served
Environmental impact
Environmental resources and constraints within 300 ft. right of way
Air, water, soil, noise quality or pollution
Percentage increase in damaged ecology of the harbor
Percentage increase in eyesores caused by industrial development along the water
Consistency with air quality goals
Consistency with Chesapeake Bay Agreement
Environmental readiness
Reduction in pollution as a result of shorter connecting truck hauls
Change in development density
Compatibility with community served
Jobs created or supported
Community support (plan consistency)
Right-of-way area and impacts
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APPENDIX C: GANTT CHART
9/5/02
10/25/02
12/14/02
Background Research on
Existing Prioritization Methods
Metric Definition
Comparison Tool Modification
Data Gathering
Case Study
Conclusions
Thesis Submission
Capstone Presentation
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2/2/03
3/24/03
5/13/03
7/2/03