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Systems Integration, Cost-Effectiveness Analysis, and Prioritization of Multimodal Transportation Investments

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A Thesis Proposal Presented to The Faculty of the School of Engineering and Applied Science University of Virginia

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In Partial Fulfillment

Of the requirements for the Degree

Master of Science in Systems Engineering

By

Shadi M. Wadie

January 12, 2005 TABLE OF CONTENTS TABLE OF CONTENTS...... 2 ABSTRACT...... 4 CHAPTER 1: BACKGROUND...... 5 CHAPTER 2: NEED FOR RESEARCH...... 7 2.1 OVERVIEW...... 7 2.2 NEED FOR ANALYSIS OF MULTIMODAL INVESTMENTS...... 7 2.2.1 Theoretical Analyses...... 7 2.2.2 Performance-Based Analysis...... 7 2.3 NEED FOR ANALYSES COMPARING ALTERNATIVE MODAL INVESTMENTS...... 8 2.3.1 Removing Modal Bias...... 8 2.3.2 Cost Effectiveness Analysis...... 8 CHAPTER 3: GOAL AND OBJECTIVES...... 9 3.1 GOAL...... 9 3.2 OBJECTIVES...... 9 3.2.1 Objective 1 - Modal Needs vs. Revenue Comparison...... 9 3.2.2 Objective 2 – MIN Prioritization Methodology...... 9 3.2.3 Objective 3 – MIN Capital Cost Estimation...... 10 3.2.4 Objective 4 – Multimodal and Highway-Only Cost Comparison...... 10 CHAPTER 4: LITERATURE REVIEW...... 11 4.1 OVERVIEW...... 11 4.2 LONG-TERM TRANSPORTATION PLANNING...... 11 4.3 THEORETICAL ANALYSES OF MULTIMODAL INVESTMENTS...... 11 4.4 PERFORMANCE-BASED EVALUATION OF MULTIMODAL INVESTMENTS...... 12 4.5 COST ANALYSES OF MULTIMODAL INVESTMENTS...... 13 4.5.1 Methods for Identifying and Assessing Modal Tradeoffs...... 13 4.5.2 Evaluation of Single-Mode Transportation Investments...... 14 4.6 COST-EFFECTIVENESS OF TRANSPORTATION INVESTMENTS...... 15 CHAPTER 5: OUTLINE OF TECHNICAL APPROACH...... 16 5.1 OVERVIEW...... 16 5.2 MIN PRIORITIZATION METHODOLOGY...... 16 5.2.1 MIN Scoring Methodology...... 17 5.2.2 Performance-Based Scoring Algorithm...... 18 5.3 MIN COST ANALYSIS METHODOLOGY...... 19 5.3.1 Classification and Cost Estimation of MIN Objectives (Step 1)...... 19 5.3.2 Cost Estimation of Highway-Only Investment (Step 2)...... 21 5.3.3 Cost Savings of Multimodal Investment Option (Step 3)...... 23 CHAPTER 6: WHAT HAS BEEN DONE TO DATE...... 24 6.1 OVERVIEW...... 24 6.2 PERFORMANCE-BASED PRIORITIZATION OF MINS...... 24 6.3 SYSTEMS INTEGRATED COST EFFECTIVE ANALYSIS...... 24 CHAPTER 7: WHAT REMAINS TO BE DONE...... 30 7.1 OVERVIEW...... 30 7.2 MIN COST EFFECTIVENESS ANALYSIS...... 30 7.3 COST ANALYSIS INCORPORATION INTO COMPUTER TOOL...... 30

2 7.4 COST-BENEFIT ANALYSIS...... 32 7.5 COST EFFECTIVENESS ANALYSIS APPLICATION TO LARGE SCALE SYSTEMS...... 32 CHAPTER 8: EXPECTED RESEARCH CONTRIBUTIONS...... 33 8.1 OVERVIEW...... 33 8.2 SUMMARY OF SCHOLARLY CONTRIBUTIONS...... 33 8.2.1 Performance-Based MIN Prioritization...... 33 8.2.2 Systems Integration and Coordination of Multimodal Investments...... 33 8.2.3 Cost-Effectiveness Analysis Methodology...... 33 8.3 IMPACTS AND SIGNIFICANCE OF RESEARCH...... 34 CHAPTER 9: RESEARCH MILESTONES...... 35 REFERENCES...... 36

3 ABSTRACT

The purpose of this research effort is a systems integration, cost-effectiveness analysis, and prioritization of multimodal transportation investments. Systems integration in this context describes the coordination of transportation projects across all modes of transportation in Virginia– Rail, Highway, Transit, Aviation, Ports. Stemming from both the Intermodal Surface Transportation Efficiency Act (ISTEA) and the Transportation Equity Act for the 21st Century (TEA-21), efforts were required by state transportation departments to consider cost-effective spending to reduce future shortages in transportation funding. In Virginia, the twenty-year transportation plan implemented by the ‘VTrans2025’ committee has identified that there will be $108 Billion in un-met transportation by the year 2025. Consequently, efforts have been aimed to evaluate and prioritize specified Multimodal Investment Networks (MINs) – groups of transportation projects spanning any of the transportation modes - within the state of Virginia, subject to various metrics of performance. In conjunction with this analytical evaluation of the MINs, a cost effectiveness analysis has been developed to compare the capital expenditure of the multimodal investment strategy with that of a single-mode implementation option. For the case of this research, a highway-only implementation strategy was considered for comparison. The cost analysis is responsible for: 1) integrating transportation projects within a MIN into their respective mode of transportation, 2) estimating a capital cost for each MIN, in addition to 3) calculating a cost savings figure from the difference between the multimodal investment and a highway-only implementation. The cost effectiveness analysis methodology developed through this research allows high-level transportation decision-makers to compare expenditure tradeoffs between alternative transportation investment strategies. This cost analysis also provides decision-makers with the ability to identify the most cost-effective investment option for transportation spending. Through the research effort, the developed method is demonstrated, tested, and discussed using case studies coordinated in conjunction with the VTrans2025 effort. The eleven MINs under evaluation through the VTrans2025 effort are the Hampton Roads Multimodal Access MIN, Richmond to Hampton Roads Passenger and Goods Movement, Interstate 95 Passenger and Goods Movement, Interstate 81 Passenger and goods Movement, Interstate 73 Corridor/Franklin County Airport Access, Coalfields Access, Route 29, Northern Virginia (NOVA) Connections, Port Accessibility and Mobility, Virginia Bicycle and Pedestrian System, Emergency Transportation. The methodology from the cost analysis will be demonstrated on the Interstate 95 Passenger and Goods Movement and NOVA Connections MINs within this proposal.

4 CHAPTER 1: BACKGROUND

This section describes the background of the VTrans2025 effort, which has provided funding for this research effort. Section 33.1-23.03 of the Code of Virginia directs Virginia’s Commonwealth Transportation Board (CTB) to develop a multimodal long-range transportation plan with a statewide focus. Developed through the Office of the Secretary of Transportation in accordance with the four state transportation modal agencies – Department of Aviation, Department of Rail and Public Transportation, Virginia Port Authority, and Department of Transportation – this plan has been coordinated into three phases. Building on recent success in implementing sound transportation practices throughout the state, Virginia’s long-range transportation plan, VTrans2025, is aimed at creating a blueprint for shaping the transportation future in Virginia. By establishing common visions, goals, and objectives in guiding its decision- making across all transportation modes, this plan identifies the need for additional resources to achieve the vision of a cohesive and interconnected transportation system across all transportation agencies. In accordance 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 coordination among the various transportation agencies of the Commonwealth of Virginia. 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. Furthermore, this legislation urges states to examine diverse collections of transportation improvement projects that fit together into a larger, multimodal (multiple modes) framework. Specifically, tools are needed in order to reflect a systems integration of multi-agency planning, further relying on concurrent processes, namely: (i) bottom up, from the project level, and (ii) top down, from the recognition of statewide needs and the identification of candidate multimodal solutions. Compounding the lack of objective-based analysis tools for multimodal transportation investment alternatives with the continued need for cost-effective transportation investment, many transportation agencies across the world continuously face this predicament. In Virginia, the VTrans2025 committee has identified that over the twenty-year span from 2005-2025, approximately $108 billion will represent the figure of unmet transportation needs through this period ($74.2 billion for highways, $30.7 billion for rail and public transportation, $3.1 billion for aviation, and $0.4 billion for the ports of Virginia). Thus, a critical element of this long-term transportation-planning problem is such that these transportation improvements represent efficient and effective financial planning. As such, Multimodal Investment Networks (MINs) have been identified throughout the state of Virginia - each spanning a specified region in the state - comprising transportation projects from any of the four transportation modes. Figure 1 shows an excerpt from the VTrans2025 plan that describes the life cycle of transportation projects that will receive priority for federal and state funding. The effort aims to develop priority models for comparison with a performance-based multimodal analysis. The lower left portion of the figure depicts transportation project comparisons to be examined on a mode-by-mode basis. The center portion of the figure depicts

5 transportation system comparisons to be examined on a multimodal basis. By improving prioritization methodology in these two steps of the VTrans2025 flow chart, the effort aimed to efficiently allocated funds of the Commonwealth’s four transportation agencies.

Each State Project RankSystems Receives Bonus Points in -Quantitative its Respective Modal Priority -Qualitative Process -Political

Each Mode Implements Score each System using Devlop Implementation Plan Individual Priority Model Priority Model -Schedule -Federal & State Requirements -Lead Agency -Governing Board -Source of Funding -Funding Source(s) -Industry Measurements Develop Transportation Systems that have Regional & State Interests VPA

VDOT Legend

Review 6-Year Plans for Agency Actions DOAV Eligible System Projects

IMAT Actions VDRPT

Figure 1 - Multimodal Statewide Transportation Planning Process (VTrans2025, 2004, 2002). The figure above shows the iterative stages of a transportation project through the course of its potential implementation.

Building from these primary efforts of the VTrans2025 committee, the motivation of this thesis became an analysis of the multimodal transportation investments across the various modes of transportation within the state of Virginia. Specifically, the effort aims to prioritize and coordinate advantages associated with a multimodal implementation of transportation projects across the state of Virginia. An analysis here - comparing the eleven investment networks throughout the state - is desired to compare tradeoffs between alternative investment strategies. To perform such analyses, deriving a modally blind performance metric became critical, as each of the MINs varied in their number of modal-specific transportation projects. Coupling this objective with the need to create objective criteria for evaluation of the MINs, the VTrans2025 committee provided this effort with a list of performance measures and objectives for use in prioritizing transportation project investments reflecting VTrans2025’s vision for transportation in Virginia.

6 CHAPTER 2: NEED FOR RESEARCH

2.1 OVERVIEW

The purpose of this section is to identify the need for consistent, repeatable method of systems integration, cost effectiveness analysis, and prioritization of multimodal transportation investments. Understanding the importance of transportation agencies across the world to maximize use of available transportation funds, analysis of transportation investment alternatives and cost-effective methodologies for spending continue to be imperative. While previous attempts have been made to determine advantages associated with multimodal transportation investments, their methods have largely taken on theoretical and practical approaches; essentially lacking a technical approach for high-level decision makers to base their critical decisions. Realizing that alternatives to the multimodal implementation include single-mode and cross-modal strategies, this research aims to consider a single-mode alternative. Specifically, this section identifies the need for both: 1) a technical analysis of prioritizing multimodal investments, in addition to 2) a comparison of their implementations with highway-only investment strategies.

2.2 NEED FOR ANALYSIS OF MULTIMODAL INVESTMENTS

2.2.1 Theoretical Analyses

Previous tradeoff analyses of multimodal transportation investments have been found to focus largely on theoretical application and evaluation methods. From these previous efforts, advantages of multimodal transportation spending are obtained entirely through non-technical approaches. By identifying advantages such as project diversity and removal of modal bias in long-term transportation planning, none of the employed efforts are grounded in an objective-based analysis. From United States Commonwealths to European nations across the Atlantic, transportation research has lacked a technical approach for objective comparison of investment alternatives between the multiple modes of transportation. Though cost-benefit analysis has been a popular method for comparison of transportation projects, many of the future projections necessary from these transportation projects are not yet available - due the long-term nature of their proposed implementation. Thus, this research effort aims to develop a systems integration methodology for use in a cost effectiveness tradeoff analysis between transportation investment options.

2.2.2 Performance-Based Analysis

In order to identify optimal use of existing resources for transportation spending, many state transportation agencies aim to achieve such measures through use of

7 objective, performance-based criteria. To begin the process of reducing the $108 Billion projection in un-met needs, the Commonwealth of Virginia hopes to implement multimodal transportation investments incurring the lowest initial cost, yet adhering best to the performance objectives specified by the VTrans2025 technical committee. In order to assess which of the MINs are most appropriate for implementation, a unique analytical methodology is needed to compare each investment network against one another.

2.3 NEED FOR ANALYSES COMPARING ALTERNATIVE MODAL INVESTMENTS

2.3.1 Removing Modal Bias

In addition to implementing analytical methods for assessing performance of each MIN relative to a list of VTrans’s predefined performance measures, a more objective measure of performance is lacking for comparison of the MINs – previously implemented analytical methods rely on input from critical decision-makers of the VTrans2025 committee. While subjective input from high-level decision-makers is often welcomed for transportation planning, a more objective metric for assessment of the MINs will help to remove any subjectivity.

2.3.2 Cost Effectiveness Analysis

Aiming to minimize subjective input in the planning process and optimize the allocation of transportation funds, the research effort aims to produce an objective and repeatable analysis between a multimodal investment strategy and potential alternatives. Specifically, the $108 Billion deficiency in Virginia’s transportation funding over the next twenty years drive’s the VTrans2025 committee’s goal to assess the capital expenditures associated with these multimodal implementations, allowing the research effort to develop a unique cost savings method from alternative transportation strategies. Coupling the need for research – lack of an objective analysis comparing multimodal investments with alternative strategies - with the VTrans2025’s desire to understand financial tradeoffs of the multimodal approach being implemented by many Commonwealths today, a cost effectiveness analysis is performed to compare such options. States such as California and Minnesota have directed a great deal of effort in recent years to uncover savings from the multimodal approach to transportation spending, and it is the ultimate hope of the state of Virginia to follow suit in their pursuit to maximize the efficiency of monetary funds being spent on transportation over the next twenty years. Therefore, the systems integrated cost effectiveness analysis employed by this research will assist transportation decision-makers by developing a unique cost savings tradeoff between MIN implementations and a highway-only strategy spanning the equivalent region.

8 CHAPTER 3: GOAL AND OBJECTIVES

3.1 GOAL

The goal of this research effort is to implement systems integration to produce a cost effectiveness analysis for prioritizion of multimodal transportation investments - by comparing their capital expenditures to that of a highway-only (single-mode) planning strategy. The effort specifically focuses on development and application of a cost analysis methodology for comparing predefined Multimodal Investment Networks (MINs) with that of a highway-only implementation.

3.2 OBJECTIVES

3.2.1 Objective 1 - Modal Needs vs. Revenue Comparison

The first objective of this research effort was a comprehensive need versus revenues comparison of the four transportation agencies - essential to understand the Commonwealth’s financial foundation for future investment. To accomplish this task, efforts were focused on data collection from a number of agency sources retaining accurate budgeting information. From this research, various memos were provided to the VTrans2025 technical committee summarizing each transportation mode’s needs/revenues status – obtained from Virginia’s Statewide Multimodal Long-Range Transportation Plan Phase 3 Report and various transportation agency documentations.

3.2.2 Objective 2 – MIN Prioritization Methodology

Upon completion of the modal needs and revenues comparison, the second objective of this research effort was to complete work originally started on the MIN coordination and prioritization workbook (Peterson, 2004) – an analytical tool for performance-based evaluation of MINs. Having been provided with a list of initial performance objectives and measures for which each of the MINs is assessed with respect to, these objectives were continuously revised by the research effort resulting from VTrans2025 technical committee meetings and public input. Although this tool contained some of the critical components necessary for a performance-based analysis, re-engineering and manipulation was needed to ensure its functionality and accurate operation. With this performance-based evaluation of the proposed multimodal investment networks and the $108 Billion deficiency in transportation funding over the next twenty years, the research effort shifted attention to the creation of a MIN cost analysis tool to be used for an objective cost analysis. This cost analysis tool retains key information for a capital expenditure cost comparison of the MINs, in addition to alternative highway-only implementation strategies.

9 3.2.3 Objective 3 – MIN Capital Cost Estimation

As part of this MIN cost analysis tool, the third objective of this research effort is to determine a projected capital cost for each of the eleven MINs proposed across the state of Virginia. To do this, the objective focuses on coordinating and classifying the transportation projects from each MIN into their respective mode of transportation, while simultaneously associating an estimated capital expenditure to each. Locations of these capital costs vary, and are obtained from VTrans2025 (Phase I, II, and III) Reports to the General Assembly of Virginia, state transportation agency reports, and scheduled meetings. The estimated capital cost of a MIN is calculated as the sum of its respective transportation project costs.

3.2.4 Objective 4 – Multimodal and Highway-Only Cost Comparison

The fourth objective of the research effort is a systems integrated cost effectiveness analysis comparing each of the MIN implementations with a highway-only transportation investment - spanning the same area covered by the given MIN. Aiming to create an analysis of alternative strategies for transportation investment between candidate transportation spending options, this objective reflects a quantitative comparison of MIN capital expenditures with that of highway-only implementations. The comparison result is an estimation of the ‘cost savings’ attained from the difference between the MIN and highway-only capital costs - helping decision-makers in their quest to minimize future deficits in transportation budgeting.

10 CHAPTER 4: LITERATURE REVIEW

4.1 OVERVIEW

The purpose of this section is to identify efforts this research builds on, in addition to background sources contributing to the technical methodology developed. Working closely with Professor Lambert through the course of this research, his experience in the field of transportation engineering and analytical decision-making has proved critical to the effort’s continued progress. Specifically, the efforts of two teams led by Professor Lambert will be discussed, as their theoretical analyses of multimodal investments directly motivates the technical approach implemented through this work.

4.2 LONG-TERM TRANSPORTATION PLANNING

By building research efforts from these theoretical attempts to analyze multimodal investments, background research was necessary to understand the critical factors influencing transportation planning of multimodal investments. Specifically, numerous sources identify the need of a long-range transportation plan to encompass both the current status of a Commonwealth’s transportation system, in addition to tradeoff analysis for determining optimal transportation spending amongst all modes of transportation. The ISTEA legislation passed in 1991 was directly aimed at forcing the federal government, states, and metropolitan areas to develop solutions to transportation needs without a modal bias - by using the full range of multimodal and intermodal solutions available (Pedersen, 1999). In order to create a long-term transportation plan, a thorough feasibility analysis of potential investment options and alternative investment strategies must be considered. In past years, statewide efforts for transportation planning outside Virginia have fell short of expectations largely because their responses to transportation needs have taken the form of short-term fixes designed to deal with the immediate crisis-at-hand (Brown, 2002). Brown continues that a change in approach is needed, moving away from immediate transportation solutions to a focus grounded in solidifying a foundation for future transportation planning between all modal agencies.

4.3 THEORETICAL ANALYSES OF MULTIMODAL INVESTMENTS

The efforts of a project led by Lambert developed a method for comparing tradeoffs between multimodal investment networks (Copperman et al., 2004). The project provided a qualitative approach for comparing two or more multimodal investment networks through creation of “Multimodal Impact Statements.” These statements, one-page encapsulations of a proposed multimodal investment option,

11 provide qualitative insight into the functions of the MIN. These statements, specifically, outline: 1) the region spanned by the MIN, 2) identified projects to be carried out within the region, 3) a qualitative need and rationale section, in addition to 4) the multimodal investment’s implementation plan. While useful for a high-level, qualitative comparison between multimodal investments, these statements lacked a quantitative analysis for informed decision-making. As such, the efforts of this research focused on moving towards a technical and objective analysis of multimodal transportation investments. An additional effort spawning this research is that from work led by Lambert during 2002. The efforts of a research group produced a novel interface for comparison of transportation projects, specific to each of the transportation modes (Ba-Ali et al., 2003). Requiring transportation project costs and performance data for analysis using this interface, the tool is primarily aimed at uncovering dominance between various transportation projects. While performance data from the transportation projects evaluated through this research have yet to be determined, the implementation of project costs would represent a first step in putting this interface to use. By implementing costs into the interface, this research will directly aid users to determine which transportation projects are dominated by others relative to capital expenditure – until performance data for the proposed projects are available. Working closely within the transportation- engineering environment, a number of analytical tools for transportation project selection have been created to date through the efforts of Lambert. One such tool is aimed at aiding the comparison of multiple highway improvements (Frohwein and Lambert et al., 1999). Providing a comparative technical analysis between alternative investment options, this analytical tool’s objective analysis reflected a similar direction to that of this effort.

4.4 PERFORMANCE-BASED EVALUATION OF MULTIMODAL INVESTMENTS

While the coordination between state transportation agencies is a critical aspect of the long-term planning process, it is coupled with a need for systematic planning and for accountability in transportation decisions, whether that is strategic planning, quality initiatives, or performance-based planning (Halvorson, 2000). Understanding the need for performance-based planning of transportation investments (a concept implemented by many states in the US today), the MIN coordination and prioritization workbook develops this concept subject to criteria provided by the VTrans2025 Technical Committee. Although performance-based evaluation of multimodal investments using multi- criteria decision-making is valuable in its broad approach of evaluating transportation projects, the need to equally consider all modes of transportation is critical for an objective analysis of all alternatives - as the performance objectives identified were not always applicable to each transportation option. Furthermore, the continuously changing transportation needs for the residents of Virginia require constantly changing performance measures and objectives. As such, transportation agencies across the world have come to realize that performance-based prioritization of transportation project investments is a difficult task. Understanding the difficulty involved in such efforts, the major challenge encountered has been that of developing objective, non-mode specific

12 measures for which data are readily and regularly available (Pederson and Jeff, 2000). Though the advantage of multi-criteria analysis is that it does not need a common measuring unit (i.e. money), the disadvantage is that valuation in several dimensions, which are made comparable by scoring systems, are often not transparent (Giorgi and Pearman, 2002).

4.5 COST ANALYSES OF MULTIMODAL INVESTMENTS

Building from the performance-based evaluation proposed for tradeoffs between mutimodal investment options, the need to consider financial advantages associated with the MINs became the focus of attention. Outlining the importance of efficient transportation investment, the state of Florida’s long-range multimodal transportation plan emphasizes establishing causality between program investment and performance measures; noting that this becomes a critically important technical and political issue for future transportation investment (Cambridge Systematics, 1999). Furthermore, from the $108 Billion in un-funded transportation needs over the next twenty years, Virginia is one of the many Commonwealths facing the issue of a large transportation-spending deficit since the implementation of ISTEA and TEA-21. Pedersen describes that these two pieces of legislation, combined with the long periods for developing transportation projects, gave rise to a massive accumulation of unfunded state transportation needs; thus resulting in short-term planning processes to catch-up with previously identified needs and projects (Pedersen, 1999).

4.5.1 Methods for Identifying and Assessing Modal Tradeoffs

Resulting from this funding shortage for future transportation investment, statewide transportation planning efforts have focused heavily on cost-effective spending of transportation funds. The state of Iowa, for example, has been thinking in this direction for over ten years. Forkenbrock suggests that transportation cost savings are true benefits to society in that cost reductions act exactly the same as income increases: by making more resources available for other purposes (Forkenbrock et al., 1993). In Synthesis #243 completed by the National Cooperative Highway Research Program (NCHRP), a discussion outlines that capital programming for transportation projects responsive to policy needs is one of two key aspects necessary for implementation consideration, adding that “…the second key issue is whether funds are being spent wisely; are the specific types of projects in the program the most cost-effective way of solving problems or meeting identified needs…?” (NCHRP, 1997). To ensure efficient spending of state transportation funds, various technical approaches have been implemented in the past. Though, when assessing what constitutes efficient transportation spending, alternative strategies and investment options must be considered in order to derive a full set of investment options. Once these alternatives have been proposed, tradeoffs must be analyzed between implementations across any of the four modes. The National Cooperative Highway Research Program’s Synthesis #243 states re-iterates that the success of cost-effective spending is a direct result of the extent

13 to which DOTs explicitly consider program tradeoffs, and their approach to evaluating program-level tradeoffs (NCHRP, 1997). As such, analyses are needed to understand the modal tradeoffs amongst varying program implementations. From Synthesis #290 by the National Cooperative Highway Research Program, direction is given towards an analysis comparing a no-build (base case) scenario to one or more transportation investment scenarios (NCHRP, 2000) when considering alternative investment strategies. Additionally, Synthesis #238 directs statewide transportation agencies to consider undertaking cross-modal analyses on an objective basis. The Synthesis continues by suggesting the importance of modally blind performance measures and comparable data across all transportation modes (NCHRP, 2000).

4.5.2 Evaluation of Single-Mode Transportation Investments

While theoretical strategies have been implemented to analyze alternative investments in transportation, the research effort strove to create a technical analysis between multimodal transportation investments and a highway-only alternative. This methodology - carried out for each of the proposed MIN implementations – of comparing modal alternatives entails that the process starts with a broad set of goals and objectives, iteratively analyzing different modes or combinations of modes for meeting specified process objectives (NCHRP, 2000). Due to the limited availability of needs analyses from three of the four modes of transportation in Virginia (excluding highway), the selection of a proposed highway-only investment strategy was solely considered here. Understanding the critical nature of considering alternative modal investments, this research builds from propositions derived from many provided by the Transportation Research Board. Aiming to help the long-term plans of Virginia’s future in transportation, research was directed towards an economic analysis of the proposed investments throughout the Commonwealth. The TRB suggests that communities and states compare the economic impact of alternative transit investments, or an investment in transit compared to investing in another public works project or no investment at all. Continuing from this idea, the TRB outlines that “In these cases, the single methodology is applied to two or more investment scenarios, and the results are compared to identify which investment will result in the greatest positive economic impact” (TRB, 1998). Combining this modal comparison of investment options in transportation with a performance-based criteria evaluation, a highway alternative cost comparison was sought for evaluating alternative transportation investments. Another of the research’s foundations builds from analyses of the modal alternative producing the lowest financial expenditure for the Commonwealth. In the case of the research proposed, the greatest positive economic impact is that investment which best meets the performance objectives outlined by the VTrans2025 committee, while producing the lowest capital expenditure. As such, research efforts were employed to analyze similar efforts not only in the United States, but countries across the world. While a cost-benefit analysis here is ideal for selection of candidate alternatives, future MIN projections for resident travel time and volume were not readily available from the Commonwealth. As such, focus was aimed to consider common methodologies carried out in European nations abroad.

14 4.6 COST-EFFECTIVENESS OF TRANSPORTATION INVESTMENTS

From Giorgi and Pearman, a method was proposed to analyze transportation investment alternatives based on cost-effectiveness. The text states that as cost-benefit analyses began to be applied to broader fields, including the comparison of alternative portfolios of projects such as road policy choices, the increased complexity required that the problem set a constant level of benefits, while evaluating the problem in terms of finding the most effective (least-cost) option - sufficient to meet the desired level of benefits. The text builds by identifying that this cost-effective analysis has the advantage that benefits need not always be explicitly valued (Giorgi and Pearman, 2002). From this analytical approach, research efforts have focused on a cost-savings approach to determine the least-cost option associated with the potential implementation of the eleven MINs proposed by the Commonwealth of Virginia. Stemming from the mentioned theoretical attempts to evaluate and compare multimodal investments, a technical, quantitative approach remains necessary for informed policy decision-making. From the work of previous team efforts led by Lambert, in addition to the cost-effective methodology for transportation spending previously discussed, a framework was provided for extended technical analysis. Hoping to build on previous attempts at prioritizing and coordinating transportation projects relative to both performance and cost (Ferguson et al., 2003), the efforts of this research have been geared towards a novel approach to assess multimodal investments, while simultaneously evaluating alternative strategies for transportation spending.

15 CHAPTER 5: OUTLINE OF TECHNICAL APPROACH

5.1 OVERVIEW

The aim of this section is to describe the technical approach in developing a systems integrated cost effectiveness analysis comparing multimodal transportation investments with highway-only implementations. Large portions of the technical analyses are in conjunction with the creation of two electronic workbooks: 1) The MIN coordination and prioritization workbook, and 2) the MIN cost analysis workbook. While both of these analytical tools aid real-world decision-makers in comparing multimodal transportation systems against alternative transportation investment strategies, they differ with respect to their technical approach. The upcoming sections outline the detailed development of these analytical tools.

5.2 MIN PRIORITIZATION METHODOLOGY

From the desire to prioritize the eleven MINs with respect to a pre-defined list of performance measures and objectives, the research effort built off those previously implemented in the MIN prioritization workbook (Peterson, 2004). Having begun creation of a MIN coordination and prioritization analytical tool, the workbook required a great deal of re-engineering and refinement. As such, a large amount of time was dedicated to ensure the integrity of the workbook as it related to the changing needs and objectives of Virginia’s residents. After first viewing a copy of the workbook on the project’s website (www.virginia.edu/crmes/multimodal), the workbook had a number of the critical components necessary for a technical comparison between multimodal network alternatives. Further, the workbook was comprised of four worksheets, three of which contained critical components to the functionality of the tool; the fourth represented an empty introduction page yet to give any explanation of the tool’s features. Today, expansion and re-engineering efforts performed have given the tool a total of seven functional sheets, including the addition of a sensitivity feature previously unavailable. The new sensitivity feature helps decision-makers in determining the best performing of the multimodal propositions – with respect to six major performance criteria predefined by the VTrans2025 Technical Committee. This sensitivity feature has been developed in Microsoft Excel, and provides an interface for the user to dynamically view MIN scores and rankings produced from this performance tool; while simultaneously varying weights applied to each of the six major decision criteria. As research efforts focused on adding new features to the workbook, it was simultaneously critical for the workbook to adhere to a changing list of MIN Performance Objectives and Measures provided by the VTrans2025 Technical Committee. This set of objectives are an integral aspect in the lifecycle of this tool, as the scoring methodology employed is required to directly reflect the performance of each MIN relative to these measures. After an initial list of performance measures was originally developed and

16 provided to the research effort, continuous contact has allowed the list to be progressively altered to reflect the changing needs of Virginia’s transportation system. Consequently, the MIN Coordination and Prioritization workbook incorporates a user-based scoring methodology (score and rank) of the multimodal networks based on criteria defined by the VTrans2025 effort. The six major performance criteria defined by the committee for which a MIN is evaluated are: Economic Competitiveness (EC), Fiscal Responsibility (FR), Quality of Life (QL), Intermodalism and Mobility (IM), System Management (SM), and Safe and Secure Transportation (ST). Within each of the above-mentioned major performance criteria lay a subsequent group of performance objectives and measures. Using fundamental systems and analytical analysis grounded in theory of multi criteria decision aiding, a weighting and scoring methodology has been implemented - for the six major criteria and all performance objectives - to assess the performance of each MIN across the six criteria named above. Each MIN receives a score of +1, 0, or –1 relative to the performance measure within any of the six criteria; a higher cumulative score across all metrics indicates the MIN is best-performing relative to the performance objectives and measures considered.

5.2.1 MIN Scoring Methodology

The first component of the scoring algorithm requires a user-inputted weight, relative to one the major performance criterion being evaluated. Here, the user may apply any weight from 1-100% to the criterion being analyzed – though the sum of all the six major criteria weights should be 100%. This particular explanation will provide insight into the scoring methodology employed with respect to one of the six major performance criteria – Safety and Security. The Safety and Security major performance criterion (one of six) retains two performance factors within it: 1) Safety – 1.1, and 2) Security 1.2. Within the first of these performance factors (Safety) is a single performance measure, 1.1a. Contrarily, the Security performance factor retains two performance measures, 1.2a and 1.2b. As such, the user is then to input weights relative to the performance measures with the major performance criterion being assessed. It should be noted here that the sum of the weights applied across all three performance measures must total 100%, also. Thus, while the user may give the Safety and Security major performance criterion a weight of 30% as compared with the other five, the user may wish to equally weight both of the performance factors (1.1 and 1.2) – giving each a weight of 50%. Though, because performance factor 1.2 retains two performance measures (1.2a and 1.2b), the remaining 50% are split equally between the two. The user here can manipulate the weights of the three performance measures as they see fit – so long as their sum does not exceed 100%. Once weights have been applied to the six major performance criteria and their subsequent performance measures, they are used in conjunction with the previously discussed user-inputted scores (-1, 0, and +1) to obtain a MIN score for the given weighting schema. Equation 1 below gives insight into the performance algorithm used for a MIN performance score using two major criteria.

17 5.2.2 Performance-Based Scoring Algorithm

Major Criteria X1 = Major performance criterion weight #1 X2 = Major performance criterion weight #2

Performance Measures within Major Performance Criterion #1 Y11A = Weight to performance measure 1.1a Y12A = Weight to performance measure 1.2a Y12B = Weight to performance measure 1.2b

Performance Measures within Major Performance Criterion #2 Y21A = Weight to performance measure 2.1a Y21B = Weight to performance measure 2.1b Y22A = Weight to performance measure 2.2a Y22B = Weight to performance measure 2.2b Y22C = Weight to performance measure 2.2c Y22D = Weight to performance measure 2.2d

US = User score (provided for specific MIN relative to a given performance measure)

MIN score from major criteria # 1 = (X1* Y11A*US) + (X1* Y12A*US) + (X1* Y12B*US) MIN score from major criteria # 2 = (X2* Y21A*US) + (X2* Y21B*US) + (X2* Y22A*US) + (X2* Y22B*US)+ (X2* Y22C*US) + (X2* Y22D*US) MIN score from major criteria # N = [(XN* YN1A*US) + (XN* YN1B*US) +…+ (XN* YN1Z*US)] + [(XN* YN2A*US) + (XN* YN2B*US) +…+ (XN* YN2Z*US)]

where,

Z = number of performance measures within Nth major performance criterion

Thus, a given MIN Score relative to a single weighting scenario can be defined as:

MIN Score = 100 * (Weighted score from major performance criteria #1) + (Weighted score from major performance criteria #2) +…+ (Weighted score from major performance criteria #N)

MIN Score = 100 * ∑ Weighted MIN Scores across N major performance criteria (Eq. 1)

18 5.3 MIN COST ANALYSIS METHODOLOGY

The aim of this section is to outline the framework and development of the systems integration and cost effectiveness analysis of the MINs. Incorporating steps to derive an estimated MIN capital cost and a comparison with a highway-only implementation strategy, the effort requires data collection, integration, and objective analysis of the region encompassed by a given MIN. Following the technical analysis grounded in theory of multi criteria decision aiding for use in comparing MIN performance, a method is needed to compare the multimodal investments without use of a subjective scoring methodology. Noting that each of the proposed multimodal investments are comprised of multiple transportation projects spanning any of the transportation modes in Virginia, finding a performance metric common to each (modally blind) became critical. With the estimated deficit in transportation spending over the next twenty years, the most important metric for comparison of the multimodal investments is monetary expenditure (capital investment). With the gross figure of $108 Billion in un-met transportation needs by the year 2025, cost-effective and efficient spending are the most critical aspect for Virginia’s future transportation plans. Thus, technical analysis is needed to gauge the financial impact of the multimodal networks as compared with the others. Additionally, attention is needed to gauge alternative investment strategies – such as single-mode transportation investment. Consequently, research efforts are directed towards a system integration and cost effectiveness analysis of multimodal investments responsible for: 1) determining a projected capital investment cost for each of the proposed MINs, in addition to 2) comparing the capital cost of each multimodal transportation investment (MIN) with that of a highway-only implementation within the specified region. The tool is comprised of twelve separate sheets - one for each of the eleven defined MINs and an introduction page. Each of the sheets retain three individual steps aimed at providing technical insight into the two previously stated objectives of the tool.

5.3.1 Classification and Cost Estimation of MIN Objectives (Step 1)

With transportation projects from various modes comprising each MIN (MIN objectives), and each MIN spanning multiple jurisdictions within the Commonwealth of Virginia, the MIN cost savings workbook’s first responsibility is to coordinate and classify each transportation project (within a MIN) into its associated mode of transportation. In order to achieve this objective, various agency reports from each of the transportation modes were analyzed in depth, in addition to multiple phone conferences being carried out with officials from each of the transportation agency offices. After categorizing each of the MIN Objectives into one of the four allotted modes of transportation within the MIN objective classification table, the next step is obtaining a projected capital cost for each of the objectives within the MINs. Here, extensive data collection is critical in order to obtain projected capital costs for all of the MIN objectives. Many of the Commonwealth’s transportation agency reports are provided at VTrans2025 technical meetings, allowing for detailed data collection of projected MIN

19 Objective capital costs. VDOT, on the other hand, provided their twenty-year roadway recommendation improvements to the VTrans2025 committee in the form of: 1) a twenty-year interstate recommendations sheet, and 2) nine highway recommendations sheets - each for a different district in the Commonwealth. Once a roadway MIN objective is found within any of the provided recommendation sheets, the associated capital cost is then entered into the MIN objective classification table. Without these organized sheets for data collection from the other three modes of transportation, projected costs for non-roadway MIN objectives were researched on state transportation agency websites, online documentations, and official phone conversations. Those non-roadway improvements for which an associated capital cost is uncovered are then also implemented into the MIN objective classification table of Step 1 of the cost analysis. The final step in obtaining a projected capital cost for a given MIN entails summing the costs of all MIN objectives associated with the four transportation modes. From here, the equation for formulating the capital cost of a given MIN is derived as:

MIN Capital Cost = ∑ (Mode 1 MIN Objective Costs) + ∑ (Mode 2 MIN Objective Costs) + ∑ (Mode 3 MIN Objective Costs) + … ∑ (Mode X MIN Objective Costs)

 where X = # of transportation modes for given system (Eq. 2)

Understanding that this long-term transportation plan spans twenty years, it is understandable that some of the MIN Objectives do not have estimated costs to date. As the analysis continued, it became evident that all of the projected costs for transportation projects within the MINs did not include maintenance costs. Thus, the estimated costs projected for each of the MINs in the upcoming analysis reflect investment (capital) costs, not accounting for continued maintenance operations over the span of the MIN’s implementation. Upon incorporating projected capital costs into the MIN objective classification table, the research effort focuses on providing a clear, direct path for decision-makers to follow when attempting to uncover the original location of the projected capital costs. With the modes of transportation working rather exclusive from one another, determining the origin of such costs becomes an arduous task. As part of the MIN objective classification table, each of the transportation projects within a MIN are given a reference number directing the user to a table – MIN Notes for Step 1 - of citations below the MIN objective classification table. This table of references provides the user with a detailed description for each of the MIN objectives – stating the mode of transportation associated with the projected capital cost, the route number of the improvement (if highway or interstate), the district and jurisdictions spanned by that MIN objective, the agency report page reference from which the cost was found, in addition to an estimated mileage for the objective (also obtained from agency report, where applicable). In some cases, a MIN objective spans various districts and jurisdictions within the Commonwealth of Virginia. Thus, for many of the MIN objectives, a physical map is used to determine the region across which the MIN objective spans. From this physical analysis, a determination is made as to the districts

20 and jurisdictions constituting the region spanned by that particular MIN; this becomes an essential component in determining the alternative strategy for a highway-only implementation cost, as will be discussed in the next section. Another important element of the MINs is that a single MIN objective, in some cases, encompasses multiple transportation projects (e.g. Route 28 and Route 234 are listed as a single MIN objective in the NOVA Connections MIN). Thus, it is important to note that projected capital costs in the MIN objective classification table account for all improvements associated with a single MIN objective; also, each of the MIN Notes for Step 1 tables provide detailed locations for all improvements listed within the MIN.

5.3.2 Cost Estimation of Highway-Only Investment (Step 2)

In addition to this systematic approach of assigning a quantitative monetary figure to each of the MIN objectives (across all modes), the cost analysis is then aimed at comparing the cost of a multimodal implementation of transportation projects to a highway-only alternative strategy. In order to accurately associate a highway-only implementation cost to each of the MINs, analysis is first performed to determine the geographic area each of the MINs covers across the state. Using the MIN Notes for Step 1 and determining the areas spanned by each MIN across Commonwealth, a twenty-year roadway needs analysis of the Commonwealth’s roadway system - obtained from Mr. Chad Tucker at VDOT – is critical to Step 2 of the cost analysis. This needs analysis is the necessary link for a comparison between multimodal allocation of funds versus that of a highway-only transportation investment alternative. The twenty-year needs analysis provides over 3,000 roadway and interstate changes to be carried out in Virginia over the next twenty years; those both new and those requiring renovation or expansion. The needs analysis also details pertinent information such as: 1) the district within which the improvement is to be carried out, 2) the jurisdiction for the stated improvement, 3) the estimated mileage for each roadway improvement listed, 4) a projected capital cost figure for each roadway improvement, in addition to 5) specifying the beginning and ending street address for the given road improvement. Thus, while the VDOT twenty-year highway and interstate recommendations are used in the first-level cost analysis to project a capital cost figure for each MIN, the VDOT needs analysis described here is used to project an estimated cost figure for a highway-only transportation implementation - spanning the same area as that of the proposed MIN. Using this needs analysis, the first step here is to identify those proposed roadway renovations falling into each of those MIN geographic regions. Maps are used to determine which of the 3,032-roadway improvements (from the twenty-year VDOT highway and interstate needs analysis) fall within each of the proposed eleven MIN geographic regions. Once the roadway improvements from the needs assessment are associated to its respective MIN, estimated highway-only implementation costs are compared with the projected capital cost for that MIN. Equation 3 on the next page provides the formulation to obtain the highway-only implementation cost.

21 Highway-Only Cost = ∑ (needs assessment improvements falling within MIN region) = ∑ (needs assessment roadway improvements costs within district #1) + ∑ (needs assessment roadway improvements costs within district #2) + ∑ (needs assessment roadway improvements costs within district #3) +… ∑ (needs assessment roadway improvements within district # Y)

 where Y = number of districts within given MIN (Eq. 3)

As an example, consider two differing cases of the eleven MINs under study. The first to be considered is named the ‘Northern Virginia (NOVA) Connections’ MIN. The NOVA area, as a whole, is comprised of seven jurisdictions: 1) Arlington County, 2) Fairfax County, 3) Loudon County, 4) Prince William County, 5) City of Alexandria, 6) Town of Vienna, and 7) Town of Dumfries. For this particular MIN, the area it encompasses is the entire NOVA region. Thus, upon obtaining the over 3,000 roadway improvements from the VDOT twenty-year needs analysis, efforts were directed at identifying all the roadway improvements from this needs analysis which fell within the area covered by this MIN – in this case, the entire NOVA area. As such, Step 2 of the cost analysis separates each of the seven jurisdictions above, and identifies within each of them: 1) the jurisdiction number and name being assessed, 2) all roadways improvements falling within the region of that jurisdiction, 3) the total mileage of all improvements within the given jurisdiction, in addition to 4) the total capital cost of all improvements within the given jurisdiction. The sum of the roadways costs within these seven jurisdictions above produced the alternative strategy of a highway-only implementation cost. The second MIN example presented here takes on greater complexity, due to its unspecified region. The ‘I-95 Passenger/Goods Movement’ MIN, ranges from the top of Virginia all the way through the Commonwealth and into North Carolina. Comprised of both roadway and rail transportation projects, determining a highway-only implementation strategy required detailed use of accurate and up-to-date maps. Here, this MIN spanned across four districts within the Commonwealth, namely, the Fredericksburg, Hampton, NOVA, and Richmond districts. Though, when determining a roadway-only implementation alternative cost, it is not accurate to consider all roadway improvements within these districts, as the MIN does not encompass the entire region of each. As such, work was done to determine which jurisdictions within each of these districts were encompassed by the MIN. After determining these boundaries, roadway improvements were associated with the defined areas, and a similar methodology was performed as that in the NOVA Connections MIN described above to obtain a roadway- only alternative transportation strategy. Unlike the NOVA Connections MIN, this MIN required greater technical analysis to accurately reflect a highway-only implementation cost due to I-95’s presence from the Commonwealth’s northern tip continuing all the way to North Carolina.

22 5.3.3 Cost Savings of Multimodal Investment Option (Step 3)

The final step of the cost effective analysis entails a cost savings calculation between a given MIN and the proposed highway-only implementation strategy for that MIN. Specifically here, the capital investment required from a highway-only implementation is subtracted from that of the multimodal implementation. The resulting ‘cost savings’ figure is used to understand whether the multimodal integration of transportation projects does in fact save money as compared with a strategy focusing on highway renovation and expansion. This cost comparison figure gives decision-makers insight into those MINs which are better off as multimodal implementations, as opposed to those which may be better suited using a highway-focused alternative investment.

MIN Cost Savings = (Capital cost of Highway-Only implementation) – (Capital cost of MIN implementation)

(Eq. 4)

23 CHAPTER 6: WHAT HAS BEEN DONE TO DATE

6.1 OVERVIEW

The purpose of this section is to provide an outline of the research efforts that have been completed to date. This section will provide a summary of the tasks worked on thus far, in addition to screen captures of the analytical tools developed through this research effort.

6.2 PERFORMANCE-BASED PRIORITIZATION OF MINS

Research efforts to date have completed a couple of the tasks aimed for completion. The MIN Coordination and Prioritization workbook, previously started by Peterson (2004) and two Capstone teams led by Professor Lambert (2002, 2003), has been largely re-engineered and re-organized. The entire weighting schema sheet has been reconfigured for ease of use and usability. Further, a new sensitivity feature has been added to the workbook, aiding decision makers in their quest to determine the best performing of the multimodal propositions – with respect to the VTrans2025 committee’s predefined list of six major performance criteria. This sensitivity feature allows users to dynamically view MIN scores and rankings produced from the tool as they vary weights applied to each of the six major decision criteria - using a Microsoft Excel GUI interface. Furthermore, this workbook has been re-designed to reflect a polished, complete software tool to be used for analytical purposes. With respect to the research effort carried out, work on the MIN coordination and prioritization workbook has been completed.

6.3 SYSTEMS INTEGRATED COST EFFECTIVE ANALYSIS

Unlike the MIN coordination and prioritization workbook, work remains to be completed on the MIN cost analysis workbook. With eleven MINs proposed for implementation by the VTrans2025 committee, immediate efforts have been focused on determining projected capital costs for six of these. To date, six of the MINs have approximately 75% of their Step 1 cost analysis component completed – determining estimated capital cost expenditure. The screenshots below provide an example of Step 1 from a given MIN cost analysis – estimating a MIN capital cost expenditure and notes for tracking the referenced capital costs.

24 Figure 2 – The Step 1 MIN objective classification table above displays the coordination and classification of MIN objectives and their associated capital cost. The final column to the right associates a “Note #” for each MIN objective, allowing users to track each objective’s cost from the MIN Notes for Step 1 table to be shown.

25 Figure 3 – The MIN Notes for Step 1 table provides users of this cost analysis the ability to track any of the projected capital costs retained within a specific MIN.

In addition to the Step 1 component of the cost analysis, five of these seven MINs have Step 2 of their cost analysis components completed – determining a highway-only implementation cost for the equivalent region as that spanned by the given MIN. The following two screenshots show the organization of information within Step 2 of a given MIN from the MIN cost analysis workbook.

26 Figure 4 - Step 2a of the MIN cost analysis here identifies a summary of the districts spanned by the MIN, the number of miles accumulated from needs assessment roadway improvements suggested within each district, and the cost per district from the associated roadway improvements.

27 Figure 5 - This table from Step 2 of the MIN cost analysis provides the user with the districts and jurisdictions spanned by the MIN, the route #'s associated with roadway improvements falling within those regions, in addition to the total mileage and accumulated capital cost from each jurisdiction.

For those MINs with their Step 1 and Step 2 cost analyses completed, a cost savings figure is calculated (Step 3), representing the difference between the multimodal transportation investment as compared with a highway-only alternative. From these derived results, this research effort provides decision-makers at the state level the ability to decipher which of the eleven MINs produces the lowest initial investment, in addition to determining which of the MINs may incur a lower financial cost through implementation of a highway-focused long-term transportation plan. The screenshot below shows the cost savings calculation obtained from the difference between the multimodal investment strategy as compared with the highway-only alternative option.

28 Figure 6 - This figure provides a summary of the MIN cost savings calculation. The multimodal implementation capital cost is subtracted from the highway-only implementation cost to obtain an estimated savings figure.

29 CHAPTER 7: WHAT REMAINS TO BE DONE

7.1 OVERVIEW

The purpose of this section is to identify aims this research is pursuing, in addition to suggesting future efforts for continued research. Representing one of many potential investment strategies for transportation, efforts to expand on this research are always being considered. From this continued evaluation of various investment strategies, efforts can be focused to improve transportation planning and cost-effective spending throughout the Commonwealth.

7.2 MIN COST EFFECTIVENESS ANALYSIS

While the MIN coordination and prioritization workbook has been completed through this research, work remains to be completed on the MIN cost analysis workbook. With six of the eleven MINs partially having their cost analyses completed, efforts are now focused on producing quality analysis on the remaining MINs. Knowing that the VTrans2025 effort spans a range of twenty years into the future, difficulties arise when attempting to produce a cost effectiveness analysis for transportation projects whose plans have yet to be finalized. Continued efforts are being made to consult with the four transportation agencies regarding both new, and updated project costs. As such, the research effort continues its effort to gather and assemble transportation alternative costs to that of a multimodal integration. Furthermore, a number of the transportation projects within the eleven MINs remain ambiguously defined, leading to occasional confusion when attempting to categorize the given them into their correct mode of transportation. As such, the research effort requires a great deal of collaboration with the VTrans2025 committee to ensure that all transportation project classifications reflect

7.3 COST ANALYSIS INCORPORATION INTO COMPUTER TOOL

An additional objective for future research builds from work begun by a team led by Lambert, whom created on an extended comparison tool for evaluation of highway investments (Ferguson, 2003). Specifically, the efforts of this group produced a novel interface for comparison of modal projects. Requiring the use of transportation project costs for analysis of this interface (in addition to performance data), the tool is aimed at uncovering dominance between various transportation projects. Thus, the efforts of this research will directly aid users to determine which transportation projects are dominated by others. The screenshot below displays the visual methodology for this comparison. In the screenshot, each of the four graphs represents a different mode of transportation. For each of the modes, performance metrics have been identified for evaluation of those

30 transportation projects, specific to that mode of transportation. Along each graph’s diagonal are the six major performance criteria – identified by the VTrans2025 committee - for which the transportation projects are to be evaluated. At the intersection of these performance criteria, for which a decision-maker may choose two, circles representing transportation projects are found. The location of the circle within this intersection indicates its performance relative to the performance metrics identified, and its cost represented by the magnitude of the circle’s size. From this research’s cost effectiveness analysis of the multimodal investment networks, transportation project costs can be implemented into this interface. While mode-specific metrics for performance evaluation have been determined, integrating project costs into this interface will allow users to determine those mode-specific transportation projects that are dominated by others. This will directly aid decision- makers in their aim to efficiently spend transportation funds on those best-performing transportation projects. Aviation )

Ports k e e >300000 W

r s e d > 300 250000 P n ( a s e u n

250 i o 200000 L h t -

n n

i 200 O 150000

s s n r o a i

t 150

C 100000

a t r e h g p 100 i 50000 O e

r l F a

l u 50 a

n 0 t n o

A 10 100 1000

T > 0 10 100 > 1000 10000 Track Miles Population Served within 20 Miles Radius (in thousands)

Rail and Public Transportation Roads

Figure 7 – Each graph, one for each mode of transportation, retains six criteria along its diagonal (for each of the six major performance criteria identified by the Vtrasn2025 committee). Each of the circles represents a different transportation project, where the circle’s size indicates its relative cost (larger circle entails a higher costing transportation project). Mode-specific metrics have been identified for each of the graphs, and the location of each circle within an intersection of two criteria indicates its performance relative to other modal projects.

31 7.4 COST-BENEFIT ANALYSIS

In addition to building on theoretical foundations for a cost analysis of multimodal systems, the research effort is continuously aiming to brainstorm potential research improvements. In particular, when considering capital costs figures from transportation projects, one must also consider the benefits associated with such investments. Thus, one of the most obvious areas for continued research would be a cost- benefit study of the eleven proposed MINs. Understanding that transportation projects will further help connect the residents across Virginia, such may help to reduce unemployment and provide residents across the state with wide access to public transportation services.

7.5 COST EFFECTIVENESS ANALYSIS APPLICATION TO LARGE SCALE SYSTEMS

Building from this cost-benefit comparison of multimodal and single-mode transportation investments, another idea for progress entails the expansion of this cost effectiveness analysis to large-scale systems. Specifically, the research effort aims to explore the opportunity to adapt the developed methodology to systems on National and International scales. While the employed method spans across a single Commonwealth, efforts will evaluate its application to the Local, Regional, National, and International levels. Comparing and contrasting the MINs using the integrated method of a multimodal cost analysis and the priority setting employed through the prioritization workbook gives insight into financial advantages associated with specific investment networks. With the research effort’s cost analysis and performance based priority-setting schema, a foundation for continued research continues to be built. One of the critical elements when considering any investment is that of identifying potential contingencies associated with all investment options. In this case, identifying contingencies amongst the identified investment networks remains a continued focus of the research effort. Looking to quantitatively express potential advantages associated with these potential investment strategies, the cost-benefit analysis described above would be well suited for such a task. Using an objective, quantitative analysis, a benefit-cost ratio for each of the investment networks would provide a guide for decision makers when deciding which investment option will result in the highest economic return with respect to its cost. Combining the efforts of this research with the aim to create a cost-benefit assessment for identification of investment option contingencies, steps can be taken to identify and test real systems within the state. Through such experimentations, indications of the methodology’s applicability can be further evaluated and altered.

32 CHAPTER 8: EXPECTED RESEARCH CONTRIBUTIONS

8.1 OVERVIEW

The purpose of this section is to identify contributions this research has provided, in addition to its impacts and their resulting significance. Having developed a methodology for comparison of multiple transportation investment strategies, this section suggests applications for its future use. Further, the particular significance from this research’s cost effectiveness analysis will be considered, including its applicability and usefulness to long-term transportation planning in Virginia.

8.2 SUMMARY OF SCHOLARLY CONTRIBUTIONS

8.2.1 Performance-Based MIN Prioritization

Using a pre-defined list of performance criteria for MIN evaluation, a methodology was developed for users to score MINs relative to specific metrics of performance. The developed workbook allows users to manipulate weights assigned to six major performance measures. By producing the user with a set of MIN scores and rankings, the methodology implemented allows for the prioritization between competing multimodal transportation investments.

8.2.2 Systems Integration and Coordination of Multimodal Investments

The contributions of this research build on multiple theoretical analyses of multimodal transportation investments. Building from these previous theoretical applications evaluating multimodal investments, this research represents an attempt to enhance coordination and communication between the transportation agencies within Virginia. Specifically, coordination of transportation projects within specified multimodal investment networks was critical to the technical analyses developed through the research effort.

8.2.3 Cost-Effectiveness Analysis Methodology

The research employed builds from the previously referenced cost-effective strategy for transportation planning. This research highlights the first attempt to incorporate a systems integrated cost-effectiveness strategy comparing predefined multimodal network investments with a highway only strategy. As the first person to develop a method for assessing a single-mode implementation option for transportation spending, the goal of the effort is aimed to produce quality analysis for long-term transportation planning, in addition to future work to build on.

33 8.3 IMPACTS AND SIGNIFICANCE OF RESEARCH

The impact and significance of this research effort is systems integrated cost effectiveness analysis, aiding Virginia’s transportation decision makers to compare multimodal transportation investments with alternative options. Specifically, the analysis here focuses on a bottom-up approach in which determining a cost for each MIN is based largely on the efforts to successfully obtain a cost for each MIN objective. The cost effectiveness analysis developed as part of the research effort allows the Commonwealth’s transportation decision-makers to: 1) determine which of the multimodal investments is the most cost-effective with respect to initial investment, and 2) determine which of the multimodal investments produces the largest capital investment savings by way of comparing its projected cost with that of a highway-only implementation. Step 1 of the cost analysis, producing an estimated capital cost for each of the eleven MINs, is useful for comparing multimodal transportation alternatives against one another. Stemming from the $108 Billion in un-met transportation needs over the next twenty years stated by the VTrans2025 effort, the continued focus for improving Virginia’s statewide transportation system for the future directly correlates to efficient, cost-effective transportation spending. As such, the research effort here places large emphasis on providing decision-makers the ability to discern which of the eleven MINs should retain the highest priority with respect to its eventual implementation. While maintenance costs will critically impact which of the eleven MINs eventually receive funding for implementation, such was not provided by any of the transportation agencies regarding their transportation projects; as such, maintenance costs have not been included as a part of this cost analysis. From Step 1 of this cost analysis, decision-makers will have the ability to determine which of the eleven MINs incur the lowest initial investment. Understanding the Commonwealth’s lack of funding for transportation improvement over the next twenty years, implementing the most cost-efficient MIN will be a key step in setting the trend over the next decade for smart, purposeful transportation spending in Virginia. Contrarily, Step 2 of the implemented cost analysis provides decision-makers with an idea of how the multimodal transportation investment compares with transportation improvements focusing solely on highway spending – with respect to capital investment. With the large sum of unmet transportation funding over the next twenty years, Step 2 of the cost analysis allows Virginia’s transportation decision-makers to compare an alternative to potential multimodal investments based on capital expenditure. Although the multimodal investments provide a more diversified group of transportation project implementations, their capital costs may be such that expenditures will not be financially beneficial to the Commonwealth’s long-term plans. On December 8, 2004, many components of this research effort were presented to the VTrans2025 committee by the author in Richmond, Virginia at the Commonwealth’s Department of Aviation Headquarters.

34 CHAPTER 9: RESEARCH MILESTONES

Table 1: The following table outlines completed research developments to date, in addition to those being pursued through continued work. Milestone Start Date End Date

Modal Needs/Revenue Memo June 10, 2004 July 15, 2004 MIN Prioritization Workbook July 15, 2004 August 30, 2004 Re-Engineering MIN Prioritization Workbook Sensitivity September 1, 2004 October 1, 2004

MIN Cost Analysis Data Collection October 3, 2004 October 28, 2004

MIN Cost Analysis Step 1 October 29, 2004 January 19, 2004

MIN Cost Analysis Step 2 November 16, 2004 January 19, 2004 Draft copy: VTrans2025 Multimodal December 17 - Final copy: TBD Project Report January 15

Thesis Proposal December 24 - January 30

The table above describes the schedule of tasks outlined by the research effort. Start and end dates have been specified for each of the research objectives. Creating a timeframe for each of the tasks has allowed the research effort to continue progressing in an organized fashion while adhering to specific deadlines.

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