design and exercise of a flexible transportation …web.mit.edu/mtransgroup/reports/reports pdf...

Joshua McConnell of 31 February 16, 2005

DESIGN AND EXERCISE OF A FLEXIBLE TRANSPORTATION SYSTEM USING A LIFE-CYCLE FLEXIBILITY FRAMEWORK

EXECUTIVE SUMMARY Designing flexibility into a system is one method for managing uncertainty. However, designing flexibility into the physical system is but one of several tasks that must be accomplished if the designed flexibility is to be feasible. While designing physical flexible systems is not trivial, other difficult tasks are ensuring that relevant institutions and organizations can, one, implement flexible solutions over time and, two, repeatedly and creditably evaluate the environment over time to determine when conditions are appropriate to implement the flexibility designed into the physical system. Another task that must be accomplished is that system designers, analysts, decision makers and owners must have the proper incentives, training and norms to recognize the need for flexibility and to design flexible physical systems and further, posses the skill set to implement, sustain and support flexible systems from within institutions. How to create “cradle to grave”, or life-cycle flexibility as solutions to complex design problems is the research topic addressed in this proposal. Life-cycle flexibility can be divided into six main phases, listed below in chronological order (though iteration will be necessary):

1. Creating or modifying enterprises and institutions so that they can produce flexible solutions, 2. Designing flexibility into the technical architecture, and creating enterprise and institutional

architectures that enable and support the flexibility in the technical architecture, 3. Implementing the flexible system – taking the system from a “paper” design and

implementing it in the “real world”, 4. Evaluating how the identified areas of uncertainty (“known unknowns”) are unfolding to

determine the need for “triggering” the flexibility that has been included in the system architecture,

5. “Triggering” the flexibility in the architecture – overcoming stakeholder and institutional resistance to create the appropriate system changes, and

6. Continuously monitor ing the environment so that uncertainties not previous identified (“unknown unknowns”) are actively identified and evaluated and uncertainties in outcomes and probabilities (“ignorance”) are reduced, allowing for the system to adequately prepare for a changing environment, before crises are created or allowing new flexibility to be built into the system.

The research proposes the development of a life-cycle flexibility framework; this framework will be applied to a “real world” problem and finally that this flexible solution will be evaluated in terms of its effectiveness and efficiency. The “real world” challenge that motivated this research is the ongoing major investments in transportation systems for Malaysia. The fast pace of development in Malaysia and the social, demographic, economic and political changes that this development has created is a major source of uncertainty complicating the design of the transportation system to meet the uncertain needs of the future. To keep pace with these changes, the Malaysian government has embraced the use of Intelligent Transportation Systems (ITS). ITS technologies make use of a blend of information and communication technologies, computers, software, and modeling tools, allowing the transportation system to be managed in new ways; usually at costs significantly lower than traditional infrastructure investments. Also, ITS enables the collection, evaluation and sharing of new information among organizations and institutions.


This research proposes that ITS capabilities can be used to design a flexible transportation system. The concept of “ITS as a real option” is proposed as an innovative way to accomplish several objectives; these objectives include:

• Creating a flexible transportation system architecture capable of dealing with “known unknowns”,

• Aiding in the ability of institutions to exercise designed flexibility, • Creating a methodology to comprehensively design flexibility into systems over their

entire life-cycle. ITS as a real option is based on the general concept of real options, which are in turn based on the notion of financial options. Real options provide flexibility to physical systems and institutions by providing the right, but not the obligation, to pursue an option at some time in the future for a predetermined price. ITS as a real option is a subset of real options that focuses on the use of ITS capabilities to provide flexibility in the transportation system. Different tools, such as options pricing, decisions trees or simulations can be used to value the flexibility provided by the ITS options. Quantitative ly valuing flexibility is important, as there is typically a cost associated with providing the flexibility to the technical system, which raises the question of whether the proposed flexibility to be imbedded in the architecture is a good investment. To help ensure that real options designed into the system can actually be exercised, political options are also included in the system. Political options are also based on the concept of real options and are designed to help ensure that real options created to embed the technical system with flexibility can be exercised. This research proposal will further describe the concepts, inter-relationships, benefits and costs, analysis/evaluation procedures of:

• Life-cycle flexibility • ITS as a real option • Political Options

The research questions that are addressed in this proposal are:

1. How can ITS capabilities be used to create flexibility in transportation systems? 2. What are the characteristics of options such that they can be exercised? 3. What type of framework should be developed as an aid in guiding actions related to

enabling, creating and operating flexible systems? To help illustrate these concepts, a set case study is included in the appendix. I. TRANSPORTATION SYSTEM OVERVIEW Transportation systems form the backbone of modern societies – providing the means to move people and goods around cities and across countries. With issues such as increasing traveler demand, congestion, safety, environment impact and suburbanization, among others, the technical challenges involved in designing an effective, efficient and sustainable transportation system have never been more difficult. The challenges involved with a transportation system go beyond the technical. Financing the large capital investment needed for infrastructure like railroads, airports, and roadways poses a challenge even for the richest of nations. Political issues surrounding the placement of large, intrusive infrastructure in one neighborhood versus another or choosing one mode over another creates challenges in building coalitions of political support for any transportation solution. Social and cultural challenges associated with changing demographics, land use changes and economic prosperity create changing norms and expectations for transportation system usage and equity issues between rich and poor. Taken together, these technical, economic, political, institutional, social, and cultural challenges create a complex system. Designing, implementing and sustaining solutions to the various problems that arise in such a complex system is not a trivial task.


II. SYSTEM COMPLEXITY It is claimed that, one, a transportation system is a complex system and, two, that complex systems exhibit problems that make designing, implementing and operating solutions difficult. Transportation Systems as Complex Systems Several different definitions of what makes a system complex and different types of complexity have been proposed, summarized by Sussman [Sussman 2000]. A key characteristic relevant to this research is the difficulty in predicting system behavior, which leads to uncertainty. Systems that exhibit these complex, large scale, integrated, open attributes are called CLIOS (Complex, Large-Scale, Integrated, Open Systems), as defined in Dodder, Sussman, McConnell and Mostashari [Dodder 2004]. A CLIOS can be conceptualized as being composed of a physical system and institutional or policy “sphere” (composed of organizations both formal and informal) as graphically represented below.

Figure 1. CLIOS Representation of Nested Complexity: Physical system “nested” within a policy system (policy sphere).

Transportation systems can be classified as complex systems because of characteristics like the ones listed below.

• Transportation systems are composed of many different subsystems that interact in ways not easy to predict,

• Transportation systems vary in both geographic and time scales, such as differences in operating and building decision time scales.

• Human decisions affect the system and the system’s operation, both in terms of explicit decisions (i.e. urban planners) and implicit decisions (i.e. travelers).

Difficulty in Creating Feasible Solutions in Complex Systems Problems that arise in complex systems are often difficult to effectively fix for a number of reasons. First, complex systems are composed of many different technical and institutional subsystems which interact in a difficult to understand manner, which makes prediction difficult. Second, the decisions made to change the system impact several areas (measured in money, time, political capital, environmental effects, social impacts), making trade-offs between areas necessary but difficult. Third, changes made to the system are often not only directed at current issues but also anticipated or desired future issues, which is difficult due to uncertainty. Dealing with uncertainty when designing, implementing and sustaining solutions to problems in complex systems is the major focus of this research.


III. UNCERTAINTY IN COMPLEX SYSTEMS The management of uncertainty is of particular importance in a CLIOS. This is because a CLIOS, more so than traditional engineering on a smaller scale, impacts more aspects of society and is longer lasting. This means that there are more sources from which uncertainty can arise and that the uncertainty can grow larger (due to long time scales and multiple subsystems) [de Neufville 2004]. Uncertainty appears in many forms, such as technical uncertainty, economic uncertainty, scheduling uncertainty and political uncertainty. These areas have been recognized and tools have been developed to deal with each. For example, factors of safety are included in the technical design to accommodate technical uncertainty, management reserves are created to address scheduling uncertainty and work in major government programs is spread over many congressional districts to reduce political uncertainty. Unfortunately, while these actions can be effective in reducing the uncertainty that is targeted, the system wide uncertainty may not be significantly reduced. For example, including factors of safety may significantly increase economic costs which may increase the uncertainty of political support in government programs. Or spreading the design and manufacture over several political districts may decrease political uncertainty but can make the management of the system more complex and may increase scheduling uncertainty. These “siloed” fixes for uncertainty can act to push the uncertainty from one part of the system to another, rather than lower system wide uncertainty. Solutions are needed that reduce uncertainties across multiple dimensions. Traditionally, engineers have focused on creating single “point designs”, which optimize around a single estimate of the future, as opposed to fully taking uncertainty into account. When uncertainty is addressed, it is usually limited to a narrow band of technical or economic uncertainty. It is believed possible to explicitly include ways to address multiple types of uncertainty within the technical, enterprise and institutional architectures. However, this will necessitate a trade -off between designing a flexible or single point future. Another way of examining uncertainty is through the classification presented in Table 1.

Table 1. Types of uncertainty (Based on Hastings 2003 and Stirling 1999).

Types of Uncertainty1 Suggested Tasks Given Degree of Uncertainty Known outcomes and probabilities "known knowns" or risks

Backcasting for near term alternatives to reduce expected costs or accentuate expected benefits , PRA

Unforeseen changes and effects “unknown knowns” or ambiguity

Additional study of system, consulting with expert options, increase scope of expert domain knowledge, sensitivity analysis

Recognized sources of uncertainty "known unknowns"

Reducing expected uncertainty at future decision points; and using conventional sensitivity and scenario analysis

1 To help illustrate these types, some examples and discussion are provided. “Known unknowns” in a transportation system are parameters such as future congestion levels. Congestion is recognized as being important, but the future level beyond a probability density function is still unknown. A “known known” example could be the effects of electromagnetic radiation from power lines. Consequences and probability of exposure can both be estimated, allowing for a firm basis of estimating risks. An “unknown known” would be a situation similar to the previous example, but one that is overlooked. The vibration modes that affected the Tacoma Bridge would be an example of this; the vibration mode was an “unknown known”, but it has subsequently moved into being a “known known”. “Unknown unknowns” are sources of uncertainty that have not been identified and may not be completely reducible. The World Trade Center attack or the rise of collectivos and subsequent collapse of the bus system in Mexico City are both examples of this type of uncertainty. The collectivo example is further elaborated on later in the proposal. Ignorance is the lack of any basis for making estimates for probabilities of occurrence or consequences. An example would be the likelihood or effects of widespread release of genetically modified organisms entering into the environment.


Uncertain outcomes and little or no basis for probabilities - Ignorance

Additional research, designing institutions and policies with capacity for adaptation and learning, precaution

Unknown sources of uncertainty "unknown unknowns"

Designing institutions and policies with capacity for scanning, adaptation and learning

This research primarily addresses “known unknowns” and “unknown unknowns”. IV. STRATEGIES FOR DEALING WITH UNCERTAINTY There are three basic strategies for dealing with uncertainty in complex systems [de Neufville 2004]. These strategies include:

1. Reducing uncertainty in the system – While all aspects of uncertainty can not be eliminated or even reduced, it is possible to reduce some uncertainty. For example, through demand management techniques, uncertainty related to market or social pressures can be reduced.

2. Increasing system robustness – Increasing system robustness over a range of possible futures is the traditional means of addressing uncertainty. For example, design factors of safeties are included in civil structures to account for uncertainty in loading and operating conditions.

3. Including flexibility into the system – Creating a system that can actively transform or better facilitate a future transformation, so as to better anticipate or respond to changing environmental and operating demands is a third way of addressing uncertainty. In this definition flexibility is different from robustness in that flexible systems actively transform to meet changing needs, while robust systems can passively perform under a variety of changing conditions. Flexibility is also different from adaptability in that flexible systems need to be modified from outside the system, while adaptable systems self modify. 2

This research primarily studies flexibility as a means of dealing with system uncertainty, though some technologies used in designing flexible systems may have additional benefits, such as aiding in the reduction of uncertainty. V. CHALLENGES OF FLEXIBLE SYSTEMS The following three challenges have been identified as being associated with flexible systems and are addressed in this research. Difficulty in “Triggering” Flexibility Triggering flexibility will require a change in the system from the status quo to a new state. Often, change in the system will disenfranchise entrenched interests and create resistance towards the change; which in this case is the exercise (or “triggering”) of the system flexibility. Also, stakeholders closely associated with the system, such as program managers, will prefer stability over changes brought about by implementing flexibility. 3 How to design Political Options into the system to overcome this resistance to triggering an option is addressed in the research.

2 Where the system boundaries are defined makes the difference between a flexible and adaptable system questionable. If a transportation system is defined as including the transportation institutions that plan, design construct, operate, manage and maintain the system, then it is unclear how this could be anything but an adaptable system, as almost all stakeholders that can change the system are considered part of the system. However, if the terms flexible and adaptable are applied only to the physical system, then these terms can have the added significance of implicitly looking at the need for policy intervention. IE, flexible systems will need outside intervention (policy intervention) to make changes, while adaptable systems will be able to change on their own without needing additional intervention from policy makers. 3 The challenges of implementation are so great that project cancellation, litigation and social value shifts resulting in substantial changes to the project are the top risks that concern transportation program managers


Evaluating the Environment Evaluating the environment presents two challenges. The first is assessing current conditions to determine when to trigger flexibility. This is assessing the current state of “known unknowns” class of uncertainties. The second challenge is evaluating the environment so that new trends not previously anticipated are identified and can be responded to before they develop into a crisis. This is identifying and assessing the “unknown unknowns”. Enterprises normally do not address the unknown unknowns and will likely need a different architecture if they are to do so in the future.4 Enabling Flexibility at Enterprise and Institutional Levels5 The last major challenge addressed here is the ability of engineers, urban planners, decision makers, managers, owners and politicians to be able to conceive, design, implement, manage, and support flexible systems. The concept of flexible systems is fundamentally different than what is the current norm for design engineers [de Neufville 2004], and similarly for other professionals, politicians and managers involved with the creation and sustainment of a transportation system. The life-cycle flexibility framework provides a methodology for systematically identifying and addressing all of the above concerns, though the focus of this research primarily addresses just the design and evaluation of flexible systems. VI. RESEARCH MOTIVATION The specific need behind this research comes from the issues facing the Malaysian government, which is investing in a transportation system that will serve the Malaysian people into the future. The Malaysian government has several long-term and ambitious goals that they are committed to meeting, such as achieving developed nation status by 2020 and transitioning from a manufacturing economy to an information economy [Mahathir 1991]. To both support these goals and alleviate current problems, such as extreme urban congestion and poor air quality, the Malaysian government has decided to improve the transportation system – one of many projects being concurrently pursued. To help deal with the uncertainty in the Malaysian transportation system, flexibility can be built into technical transportation system and the institutions affiliated with it. This drives a need for a design and analysis framework that can aid in designing and implementing flexibility into the overall system. VII. RESEARCH QUESTIONS

[Brand 2000, Mehndiratta 2000]. To minimize these risks, decision makers and managers are more likely to want to lock a project and project constituency in place and then complete the project as quickly as possible. 4 How an institution responds to the environment will be shaped and constrained by both formal (political rules, contracts, economic rules, etc.) and informal (customs, norms, perceptions, etc.) constraints on the actions that it can take [North 2002]. For example, in government agencies like the U.S Department of Transportation, there are legal constraints (formal constraint) that prohibit the organization from expanding its legislated mandate and cultural constraints (informal constraints) that prohibit the USDOT from engaging in activities such transportation system operations. These formal and informal constraints act to prescribe the type of information that these institutions collect and the types of actions that are seen as feasible. This poses a problem when there is trouble with the system that had not been anticipated (unknown unknowns) and which falls outside the current mandate of current institutions. Creating institutions that can evaluate new types of information and adapt to the changing environment [Zuckerman 2001] in a timely manner is essential if flexible transportation systems are to be flexible enough that they can respond or anticipate more than just the “known unknowns”. 5 Enterprises are typically thought of as organizations or groups of organizations that are focused on achieving a particular venture. In contrast, institutions are thought of as broader based organizations and rules that form the underlying building blocks of societies, governments, etc. As an example, the Department of Defense could be considered an institution, while the interaction and creation of organizations for a specific purpose, such as designing and building the Joint Strike Fighter, would be an enterprise.


The following three questions help define the emphasis of this research.

1. How can ITS capabilities be used to create flexibility in transportation systems? ITS technologies enable transportation systems to be operated and managed in new ways and at a substantially reduced cost compared to traditional infrastructure investments. Additionally, ITS technologies enable information collection that can be used to make better future decisions. 2. How do the above capabilities need to be designed so that they can be exercised? Aligning the ITS technology and architecture choices with both the technical needs posed by the transportation system and the needs posed by involved stakeholders will also be necessary for the ITS enabled flexibility to be effective. As triggering options that make use of ITS capabilities may mean a substantial change from the status quo of traditional infrastructure investments, operational norms and roles of existing transportation stakeholders, these flexible designs will likely need to be modified. Instead of just being designed to deal with uncertainties affecting the technical system, the inclusion of political options creates incentives or stakeholder coalitions that desire the exercise of the designed options. 3. What type of framework should be developed as an aid in guiding actions related to enabling,

creating and operating flexible systems? The act of creating a flexible system must encompass more than just the act of designing flexibility into the technical system. Additional activities could include; creating an enterprise and institutional environment conducive to including flexibility in designs, valuing flexibility, knowing when to “trigger” flexibility, understanding how to implement flexibility, and possessing the ability to evaluate the environment to respond to emerging trends that were not previously antic ipated. Creating a framework that can help guide designers and decision makers through all the facets of flexible systems is necessary. VIII. LIFE-CYCLE FLEXIBILITY DESCRIPTION I hypothesize that current methods of designing and evaluating flexibility in physical system are not adequate to ensure that the deployed architecture is effective, efficient, implementable and capable of coping with unknown unknowns and ignorance. A new framework that deals with all of these issues, called life-cycle flexibility, is proposed. Current tools and practices for designing flexibility into a system are inadequate for a number of reasons, as follows:

i. Quantitative analysis tools do not take into account difficult to quantify parameters such as distribution of costs and benefits among stakeholders or political realities that may make it difficult to implement solutions,

ii. Qualitative tools are not sufficient by themselves to fully develop technical systems or determine the effects that technical, social, economic and political systems have on one another,

iii. In practice, professionals charged with either system design or implementation are not trained in or aware of methods to create and implement flexible systems, and

iv. The ability of institutions to create support for or implement flexible systems in practice is questionable.

The life-cycle flexibility framework is envisioned as being “overlain” on top of existing processes for architecting and policy making, as opposed to replacing existing activities, as shown below in


Figure 2. Specific phases called for by the life-cycle flexibility framework are presented in a diagram that integrates the existing CLIOS framework with the life-cycle flexibility framework in Figure 3. Additional discussion on each of the steps in the life-cycle flexibility framework is discussed in Table 2.

Figure 2. Life-cycle flexibility framework overlaid on top of existing frameworks for technical design and policy.

Figure 3. Integration of CLIOS and Life-cycle flexibility frameworks.


Table 2. Description of Life-cycle flexibility activities.

Phases Description Time Line

Enterprise Readiness for Flexibility

• Support for addressing uncertainty in owner enterprises • Ability to architect flexible systems in design enterprise • Incentivize addressing uncertainty in institutions

Occurs during pre-design / policy making process

System, Enterprise and Institutional Architecture

• Address uncertainty in an integrated manner using technology, management, organization and policy tools

• Flexibility at system, enterprise and institutional levels • System architecture can enable implementation

Occurs during design / policy making process

Architecture Implementation

• System, Enterprise and Institutional architecture to support implementation of system at T=0 (system deployment)

Occurs during system deployment

Flexibility Assessment

• Assess environment to determine when to implement flexibility

Occurs periodically or continuously after deployment

Flexibility Implementation

• Environment capable of imp lementing flexibility at T>0 • Feasibility of implementing system flexibility

Occurs periodically after deployment

Environmental Assessment

• Assess and evaluate changing environment for “unknown unknowns” to allow for flexibility at institutional level

Occurs periodically or continuously after deployment – observation of long term trends

From this discussion, the following definition of life-cycle flexibility is proposed:

Life-cycle flexibility is a framework for holistically addressing multiple aspects of uncertainty with the goal of creating a flexible system capable of coping with both known and unknown uncertainties. Life-cycle flexibility explicitly addresses uncertainty at all phases in the life-cycle, including; enabling flexibility at the enterprise and institutional levels, designing and evaluating flexibility in technical architectures, evaluating the environment, and implementing the flexibility.

IX. REAL OPTIONS AS A TOOL FOR ACHIEVING FLEXIBILITY Real options are one potential tool that can be used to help create and evaluate flexibility in technical architectures. The concept of real options is based on financial options. Real options give decision makers specific alternative courses of actions that can be pursued in the future for the physical system, depending on changing needs. Financial options give the option owner the right, but not the obligation, to take some action now or in the future at a predetermined cost. Real options are similar in concept, but in reality, additional complexities may exist, such as the ability to actually exercise the option or uncertainty surrounding exercise costs. Real options can be used to help cope with “known unknowns”. Tools for Quantitative Analysis of Flexibility Using a quantitative method to determine the benefits of flexibility is important, as a flexible system design is often more expensive than a point design and the question of whether the flexibility is worth the cost needs to be addressed. Several methods exist that are useful in quantitatively assessing the value of flexibility in a system, including options pricing, decision trees and simulations. Each methodology has strengths and benefits and the appropriateness of which to use is primarily based on the type of system encountered and the information available.


Options pricing methods offer the benefit of finding the exact value of flexibility in systems, removing uncertainties regarding parameters such as user determined discount rates and expected future probabilities. However, in order to calculate an exact value using arbitrage free pricing of replicating portfolios, a complete market must exist for an underlying asset that determines the value of the option. Often in real world systems such a market does not exist and finding appropriate replicating portfolios is either difficult or impossible, making the application of options pricing methods infeasible. Decision trees use expected outcomes and probabilities to find flexibility values [Raiffa 1968] and offer the advantage of being conceptually easy to understand and allow calculations when little historic information is available. However, several technical issues plague decision trees, such as the appropriate selection of risk adjusted discount rates and the changing risk profile of a project over time. Additionally, applying decision trees to real projects becomes difficult as the sheer number of choices available makes the decision tree very “bushy” very quickly. Simulations offer the advantage of being able to take into account many different parameters associated with the system, such as multiple sources of uncertainty. Through the use of Monte Carlo analysis, many different scenarios can be analyzed that create an understanding of how the flexible system will respond to uncertainties. However, simulations are the least theoretical method for calculating values associated with uncertainties, and values calculated can be sensitive to user defined functions, such as decision rules. In this research, option pricing, decision trees and simulations have all been considered as tools for quantifying flexibility values. Simulations have been chosen as the method that will be used, as this allows for the consideration of a greater number of parameters and the demonstration that options pricing tools are not appropriate for the types of ITS capabilities being investigated. X. ITS AS A REAL OPTION AND SIMULTANEOUS INFRASTRUCTURE AND OPERATIONS PLANNING ITS capabilities create new opportunities in transportation systems, that when coupled with their relatively low cost, will permit transportation systems and enterprises to be architected in radically different ways in the future. First, ITS technologies make use of a blend of information and communication technologies, computers, software, and modeling tools. These technologies allow the transportation system operations to be actively managed where system operations change based on current needs. To take advantage of this type of operational flexibility, considering operations at the same time that infrastructure investments are being planned for is necessary. Second, the use of these various technologies enables the collection, evaluation and sharing of unprecedented amounts of information concerning the transportation system usage and status. Third, ITS capabilities often entail significantly lower capital outlays than comparable improvements in traditional infrastructure. Examples of using ITS as a real option is presented in Table 3.

Table 3. Examples of ITS real options.

Option Types ITS Example Postpone/ Defer/

Wait The use of ITS capabilities to defer infrastructure investments until additional information is gathered on future transportation system conditions [Leviäkangas, 1999, 2002].

Abandon End of service for most types of ITS capabilities is possible and is easier to accomplish than with fixed infrastructure. For example, ending service to customers is simple, compared with removing infrastructure.

Expand / Contract

Variable Message Signs can be used to expand the types of information available to travelers or Electronic Toll Collection technologies can have their use expanded upon, first as dedicated ETC and then to help monitor congestion. Similarly, ITS capabilities can then be contracted after an expansion.

Growth ITS infrastructure, such as fiber optic cable or embedded roadside sensors, can be invested


in during routine construction, before there is an identified need for full ITS capabilities. This can result in new capabilities being added at a later date. The addition of non-transportation capabilities is discussed in more detail later in the paper.

Switch ITS capabilities, such as cameras, can be switched between functions. In a normal state they can be used to observe traffic flows and identify traffic accidents, though their functionality could be switched to incorporate the cameras into a security system in the event of terrorist threats, similar to the use of cameras deployed in London.

Compound ITS capabilities that enhance user operations can be deployed sequentially – GPS onto trucks first, then tracking equipment, then two way communications, then centralize scheduling capabilities, etc.

While ITS as a real option can introduce flexibility into the physical system, the ITS capabilities that are included in the architecture can also be structured to enable other opportunities as well. Specifically, ITS capabilities can be structured to:

• Create opportunities to enable non-transportation applications 6, • Create links to stakeholders not normally associated with transportation systems7, • Reduce barriers and enable future transportation related policy choices.

It is believed that ITS as a real option can be structured such that it not only provides flexibility to the physical transportation system, but can do so in an implementable manner and can provide a means for institutions to better evaluate and adapt to the changing environment. XI. POLITICAL OPTIONS Three general strategies have been identified for overcoming implementation barriers that may exist when an attempt to trigger an option is made.

1. Policy fixes – Solutions include incentives (direct transfers, tax breaks, favorable regulations and standards, etc.) to stakeholders that may otherwise resist option exercise.

2. Change in stakeholders – Stakeholders that may resist option exercise are removed or marginalized. For example, government could take over some responsibilities and reduce role of private industry in system.

3. Change in technologies or system architecture – The technical architecture or technologies in the system can be changed. It is believed that the choice of technologies and architectures will affect stakeholder resistance. Technology and architecture choices can both pacify resistance and bring in new stakeholders that will shift the power balance in stakeholder coalitions.

While all methods are viable for overcoming political resistance to option exercise, the third method is the only one examined in this research.

6 Certain ITS technologies have the potential for being “dual-usage” in both transportation and non-transportation applications. The potential to deploy dual use ITS technologies into transportation systems opens up the possibility of forming public-private partnerships with non-transportation stakeholders and tapping into new sources of revenue [Ankner 2003]. One possibility for these types of public-private partnerships would have early government deployment of dual use ITS systems which would then be expandable into non-transportation uses. This is an example of an expansion or growth option. 7 A key aspect of deploying dual usage ITS capabilities are their ability to create the incentive for non-transportation stakeholders to become interested in the deployment and expansion of the ITS technology. By forming public-private partnerships, new coalitions can be formed that will have vested interests tied to the deployment and expansion of dual-use ITS capabilities. By judiciously selecting the ITS architecture and technology, a coalition that can exert pressure for the deployment of the initial ITS architecture and the use of ITS options can be created, potentially overcoming resistance from entrenched stakeholders that are opposed to the deployment of ITS capabilities.


As presented previously in Figure 3, political options likely entail a technical redesign or addition to the real options previously included to address technical uncertainties. Here the technical redesign is meant to address political uncertainty. Like real options designed to address technical uncertainty, political options need to be evaluated for cost effectiveness also, though this is more difficult as benefits are not necessarily measured in cash flow or economic value but in political support. A two step evaluation procedure, consisting of qualitative and quantitative stages, is proposed. The qualitative first step consists of performing a stakeholder analysis to help identify stakeholders and their positions, both before and after the inclusion of political options. The second stage quantitative analysis supplements the qualitative stakeholder analysis by analyzing the cash flows magnitudes and the timing of the cash flows to various stakeholders. Changing the technical design and architecture to change the magnitude and timing of cash flows is meant to change the relative stake that each stakeholder has in exercising the real options. Together, the qualitative and quantitative analysis is meant to assess whether political support for option exercise can be obtained. XII. RESEARCH DESIGN To help answer the research questions presented above, the research will be conducted in five major parts, which are listed below. Each part is elaborated on below. It is noted here that this research design is in its preliminary stages and may be modified.

1. Background literature search and general research in the areas of: § quantitative analysis techniques for designing and evaluating flexibility, § qualitative frameworks for understanding and designing the implications and needs of

flexible systems, § available ITS technologies and common practices, § CLIOS Analysis framework, § current practices used in making project decisions, and § current state/future direction of overall transportation related system in Kuala Lumpur,

Malaysia, 2. Definition and Development of Major Concepts; Life-Cycle Flexibility, ITS as a Real

Option and Political Options, 3. Application of the life-cycle flexibility framework to transportation issues in Malaysia, 4. Evaluation of life-cycle flexibility, and 5. Integration of the life-cycle framework into pre-existing CLIOS Analysis framework.

It should be noted here that while the above steps are presented in a linear manner, it is anticipated that iteration between steps will be necessary. Elaborating on each of the above major parts; 1. Background Literature Search The background literature search has five major purposes, as follows:

i) Identify appropriate qualitative and quantitative tools that may be of relevance to this research. ii) Become familiar with current practices in the areas relevant to the research, namely options pricing, decision tree analysis and simulations, various qualitative frameworks such as organizational theory and political economy, and techniques and tools currently being used to make and implement decisions. iii) Identify strengths and weaknesses in each of the above areas. This will help in guiding the creation of the life-cycle flexibility framework by pairing the strengths of the qualitative frameworks to address the weaknesses in quantitative analysis tools, with the corollary also holding. iv) Gain domain background knowledge on the state of affairs in Kuala Lumpur and Malaysia . v) Gain domain knowledge on ITS technologies and history of ITS usage.


2. Definition and Development of Major Concepts; Life-Cycle Flexibility, ITS as a Real Option, and Political Options These research concepts will be explored in more depth. More detailed work on all parts of each concept will be pursued. However, specific attention will be focused on a more narrow aspect of each of the concepts, as opposed to trying to fully develop each. The inter-dependencies of these concepts will also be focused on. 3. Application of Life-Cycle Flexibility, ITS as a Real Option, and Political Options to Transportation Problems in Malaysia It is believed that these concepts must be applied to a “real world” problem to determine if they are useful. One or more current issues related to developing the Malaysian transportation system will become the focus for applying these three concepts. The problems of interest are yet to be determined, but the scope of the problems may range from the small8 to the large9. Life-cycle flexibility, ITS as a real option, and Political Options will be used to help produce a flexible solution to an appropriate problem associated with the transportation system in Kuala Lumpur. It is anticipated that during the application of these concepts, additional development work will be required. Iteration between parts is expected. 4. Evaluation of Life-Cycle Flexibility, ITS as a Real Option, and Political Options To determine if these concepts can be used to help create flexible solutions that are effective and efficient, some set of metrics and a methodology must be found or created for evaluative purposes. Modifications to each concept may be necessary to improve the flexibility of solutions created with their use in either effectiveness or efficiency. 5. Integration of Life-Cycle Flexibility and the CLIOS Analysis Framework To complete the steps outlined in the CLIOS Analysis, a variety of tools may need to be employed – one of which is a tool to help develop and evaluate options necessary for system design and implementation. Life-cycle flexibility is anticipated as being one such approach that could be employed during the CLIOS Process. Steps 7 through 910 in the CLIOS Process are currently believed to be the ideal location where life-cycle flexibility would likely be used. However, life-cycle flexibility may potentially be of use in aiding in the completion of steps 5 through 12.

8 For example, when expanding the rail connection between the Malaysian International Airport and the Singapore International Airport, should a second rail line be added or should the existing rail line be converted to support high-speed rail? 9 For example, what forms of transportation infrastructure should the Malaysian government invest in – rail, buses, road infrastructure or a combination – and how should they implement these decisions so that they can be adapted in the future to help ensure continued effectiveness. 10 Step 7 – Identify Options for System Performance Improvements Step 8 – Flag Important Areas of Uncertainty Step 9 – Evaluate Options and Select those that Perform “Best” Across Uncertainties


Figure 4. Steps in CLIOS Analysis (from Dodder 2004).

XIII. EXPECTED OUTCOMES OF RESEARCH This research is expected to offer unique and new contributions in several areas. First is a new real options application area. The use of ITS capabilities as real options is an area with little previous research attention. Second, the integration of quantitative evaluation of technical real options along with the more qualitative political options is expected to produce insight that will help produce flexible systems that are more likely to be implemented. Third, looking at the entire life cycle of flexibility as opposed to just the design or evaluation of flexibility is expected to yie ld insights into improved ideas for the design of flexibility. • Domain Application

– New way of thinking about ITS capabilities – as real option to create flexible system • Quantitative Outcomes

– Application of quantitative valuation techniques to ITS as a real option • Qualitative Outcomes

– New way of thinking that explicitly links architecture choices and decisions to creating exercise opportunities

• Methodological Outcomes – New methodology/framework for architecting systems


XIV. REFERENCES Allen, Tom et. Al. (2001) ESD Terms and Definitions, Version 12 . ESD Working Paper Series, ESD-WP-2002-01. Ankner, William. (2003) Financing Intermodal Transportation, Reconnecting America Report. Ashford, Nicholas. (2000) An Innovation-Based Strategy for a Sustainable Environment, Innovation-Oriented Environmental Regulation: Theoretical Approach and Empirical Analysis, Springer Verlag. Brand, Daniel, Shomik Mehndiratta, Thomas Parody. (2000) Options Approach to Risk Analysis in Transportation Planning, Transportation Research Record, Journal of the Transportation Research Board 1706. Borison, Adam. (2003) Real Options: Where are the Emperor’s Clothes?, Presented at the Real Options Conference in Washington DC, July 2003. Christensen, Clayton. (2000) The Innovator’s Dilemma, Harper Business. de Neufville, Richard. (1990) Applied System Analysis: Engineering Planning and Technology Management, McGraw-Hill Publishing. de Neufville, Richard. (2004) Uncertainty Management for Engineering Systems Planning and Design, Monograph draft for Engineering Systems Division. Dodder, Rebecca, Joshua McConnell, Ali Mostashari and Joseph Sussman. (2005) The Concept of the CLIOS Process: Integrating the Study of Physical and Institutional Systems Using Mexico City as an Example, working paper. Hastings, Daniel, et. Al. (2003) Assessing the Implications of Emerging Technologies, National Science Foundation IGERT Proposal. Hastings, Daniel and Hugh McManus. (2004) A Framework for Understanding Uncertainty and its Mitigation and Exploitation in Complex Systems, MIT Engineering Systems Symposium, Cambridge, MA, April 2004. von Hipple, Eric, Stefan Thomke and Mary Sonnack. (1999) Creating Breakthroughs at 3M, Harvard Business Review, Jan-Feb 1999. Kingdon, John. (2003) Agendas, Alternatives and Public Policies, Longman. Lessig, Lawrence. (2003) Code and Other Laws of Cyberspace, Basic Books. Leviäkangas, Pekka, Jukka Lähesmaa, (1999) Profitability Comparison between ITS Investments and Traditional Investments in Infrastructure, Finish Ministry of Transport and Communications Report. Leviäkangas, Pekka, Jukka Lähesmaa, (2002) Profitability Evaluation of Intelligent Transport System Investments, Journal of Transportation Engineering, May/June 2002.


Litman, Todd, (2004) London Congestion Pricing: Implications for Other Cities, Victoria Transport Policy Institute. Mahathir, Mohamad. (1991) Malaysia: The Way Forward, Working paper presented by Malaysian Prime Minister to Malaysia Business Council, Feb. 28, 1991. Found on: http://www.epu.jpm.my/Bi/speech/vision2020i.html McConnell, Joshua. (2004a) Overview of Mathematics Behind Real Options. Working Paper. McConnell, Joshua. (2004b) Issues in Applying ITS Real Options. Working Paper. Mehndiratta, Shomik, Daniel Brand, Thomas Parody. (2000) How Transportation Planners and Decision Makers Address Risk and Uncertainty , Transportation Research Record, Journal of the Transportation Research Board 1706. Mun, Johnathan. (2002) Real Options Analysis: Tools and Techniques for Valuing Strategic Investments and Decisions, Wiley Finance. Olsen, Mancur (1982). Rise and Decline of Nations, Yale University Press. North, Douglas. (2002) Institutions, Institutional Change and Economic Performance, Cambridge University Press. Raiffa, Howard. (1968) Decision Analysis: Introductory Lectures on Choices Under Uncertainty, Addition-Wesley Publishing. Ramirez, Natalia. (1997) Valuing Flexibility in Infrastructure Development Plans: The Bogota Water Supply Expansion Plan, Masters Thesis, MIT, Cambridge, MA. Stigler, George. (1971) The Theory of Economic Reform, Bell Journal of Economics and Management Science, Spring 1971. Stirling, Andrew. (1999) On Science and Precaution in the Management of Technological Risk Volume I, European Science and Technology Observatory Project Report, EUR 19056 EN. Stone, Deborah. (2002) Policy Paradox: The Art of Political Decision Making, W.W. Norton & Company. Sussman, Joseph. (2000) Ideas on Complexity in Systems: Twenty Views. Engineering Systems Division Working Paper, ESD-WP-2000-02, February 2000. Triantis, Alex and Adam Borison. (2001) Real Options: State of the Practice. Journal of Applied Corporate Finance, v 14 n 2, Summer 2001. Zuckerman, Brian. (2001) Long Term Trends, Adaptation, and Evaluation in US Regulatory Policy, MIT PhD Dissertation.


APPENDIX - CASE STUDIES To help illustrate the concepts presented previously, a case study has been developed. The case study is idealized, but based on the current situation in Kuala Lumpur, Malaysia. The scope of the case study is limited to discussing creating technical flexibility through real options and political options. As seen in Figure 3 above, life-cycle flexibility also requires consideration of short and long term evaluation activities and enterprise readiness activities. These will be addressed in future research. Note, while the case study is based on the situation in KL, the ideas and lessons learned are applicable to other cities as well. A1. INFRASTRUCTURE INVESTMENT VS . ITS AS A REAL OPTION One of the most apparent uses that ITS as a real option can have is as an alternative to infrastructure investments. Typically, transportation organizations respond to congestion with new investments in infrastructure. As large infrastructure projects can be extremely costly in terms of capital funding11, the use of ITS capabilities, which can be an order of magnitude less expensive, to replace or delay an investment in traditional infrastructure can seem attractive. The case study addresses several objectives. First, is demonstrating how ITS as a real option can be used to address future uncertainty regarding the need for traditional infrastructure, by delaying a decision until more favorable conditions appear or additional information is gathered to reduce uncertainty. Second, this case study provides a means to show one way of quantitatively valuing the flexibility that is added to the system with the ITS capabilities. Third, a brief analysis of the quantitative results is presented to highlight some of the potential system and policy implications that flexibility will create. Fourth, the case study is re-examined to determine the feasibility of actually implementing the flexibility that is designed into the system. Description of the System, Alternatives and Decisions The system under consideration is the road network around the Kuala Lumpur metropolitan region, as shown in Figure A1. Future projections regarding the level of passenger and freight traffic levels are uncertain. Large increases in traffic will make the road network congested, reducing the economic value to users. Traditional, non-flexible approaches to this problem have either been to invest in traditional infrastructure (new roads, expanded capacity of existing roads) expansion projects or to do nothing and maintain the status quo until some future date. Traditional infrastructure investments are likely to be of a large scale and require large capital funding levels, which will be wasted if projected traffic levels do not materialize. Maintaining the status quo threatens to swamp the current infrastructure, reducing economic value to users, especially if heavier than projected traffic increases occur. Additionally, postponing investments can make decision makers in government seem unresponsive to current conditions. In addition to economic concerns, other drivers to this decision, such as environmental concerns from vehicle emissions, also exist. If the state of congestion is high, the infrastructure would be a good investment and the economic and environmental returns stemming from increased traffic flows, mobility, and time savings in the area would more than offset the capital expense of the project. However, if the congestion turns out to be lower, the additional infrastructure capacity will bring no additional economic or environmental benefits and the project would be considered a net loss.12 In the simulation, expansion of traditional 11 Large infrastructure can also be very costly in areas besides capital funding, such as; time, environmental impact, social impact, political capital, etc. 12 This is always debatable and dependant on how benefits are measured. If measuring only benefits to the community as a direct result from the transportation system, then there would be no net benefits if the future congestion was low. However, if other benefits are counted, such as payments to construction workers and the increased circulation of money in the local economy, it becomes unclear if the project if worthwhile or not. For simplicity, this paper only considers benefits resulting directly to or from the transportation system.


infrastructure costs would be the result of adding additional capability to the network. A doubling of capacity is created in the simulation when the infrastructure expansion alternative is chosen. Benefits from the infrastructure expansion come from a reduction in congestion (increased mobility), an increase in the total number of vehicles that the transportation system can accommodate and an improvement in environmental conditions brought about from a reduction of congestion.

Figure A1. Map of Kuala Lumpur metropolitan region. Additional alternatives to maintaining the status quo or investing in traditional infrastructure also exist, namely in the form of ITS capabilities. For this case study three types of ITS solutions are considered: congestion pricing systems, a network of dedicated HOT lanes (assumed to service passenger traffic only) and a network of dedicated freight lanes (assumed to service freight traffic only). Currently, a significant fraction of the surface road network around and leading into the KL city center are privately owned toll roads, as shown in Figure A2. These toll roads employ a variety of ITS technologies and architectures (most commonly utilizing smart cards) that are employed to facilitate toll payments and in some cases provide traveler information and traffic management capabilities. One of the alternatives under consideration for the case study would be to employ a percentage of the current toll road infrastructure as HOT lanes. Instead of charging a fixed price irrespective of traffic levels, a portion of these roads would have real time pricing based on current traffic conditions, with the purpose of maintaining traffic flows. Additional HOT lane capabilities can be deployed on the most congested public roads. In many cases the ITS technologies on the toll roads would need to be upgraded or operated in a different manner to support the HOT lanes. Some ITS infrastructure on public highways would likely need to be deployed. Costs for deploying HOT lanes in the simulation account for upgrading existing ITS capabilities to accommodate real time pricing. Benefits from HOT lanes come from an increase in mobility for a subset of passenger traffic.

Port Klang

Cyberjaya and Putrajaya

KLIA


Freight lanes work in a similar manner to HOT lanes, but for this simulation the operational difference is that HOT lanes service passenger traffic only, while freight lanes are reserved for freight traffic exclusively. ITS technologies are assumed to be identical between HOT lanes and freight lanes. Differences between the two center on the accompanying infrastructure. Freight lanes are assumed to have modified lanes that would include road surfaces that have been fortified to better accommodate the larger loads created from freight vehicles. It is assumed that freight lanes would primarily operate between KL city center and Port Klang, where the majority of freight traffic originates in the KL metropolitan region. Costs for deploying freight lanes come from upgrading existing ITS capabilities and improving selected road surfaces between KL city center and Port Klang. Benefits from an increase in mobility for a subset of freight traffic.

Figure A2. Kuala Lumpur Metropolitan Region surface roads; toll roads called out with arrows.

The congestion pricing system envisioned for KL would be a cordon system deployed around KL city center. Geographically, the KL city center border is similar to the boxed in portion of the KL metropolitan region shown in Figure A1. The congestion pricing system would create a fixed costs assessed on motorists, both passenger and freight, for crossing the cordon and traveling in KL city center, in a similar manner to the congestion pricing system recently deployed in London. However, while the London congestion pricing system uses camera based ITS technologies, the congestion pricing system envisioned for KL would utilize existing ITS smart card technologies, where applicable, on the extensive private toll road network. The congestion pricing system would act to increase prices assessed on motorists and with the increased price suppress demand for travel. The lower demand would create a reduction in congestion which would result in increased mobility, providing benefits in economic value and environmental costs. Economic costs from deploying a congestion pricing system in the simulation come from standardizing and interfacing existing ITS capabilities and adding new capabilitie s were needed, to complete the cordon. Benefits from a congestion pricing system stem from a reduction in traffic levels, which results in improved


mobility. Improved mobility gives economic benefits and environmental benefits (fewer vehicles idling). A summary of the costs and benefits associated with each alternative is given below.

Table A1. Summary of costs and NPV for each alternative.

A discount rate of 5% is used for all alternatives.

Alternative NPV Costs Status Quo -$15M $0 Infrastructure Expansion $14M $190M Congestion Pricing System $57M $19M HOT Lane $44M $5M Freight Lane $42M $10M

Traditional economic analysis shows that the congestion pricing system would be the most beneficial alternative in year 0, as it has the highest NPV. However, as seen from figures A3 – A5, this is not always true, depending on annual traffic growth and deployment year. All of the alternatives presented could be implemented in year 0 by decision makers. However, in traditional, non-flexible planning, the design of any system chosen would likely be made for the long term. If projections turn out different than expected, losses can be expected. Additionally, each alternative is better for different levels of traffic. HOT and freight lanes are good for low levels of congestion, congestion pricing systems are good for moderate levels of congestion and infrastructure expansions are good for high levels of congestion. The appropriateness of when to deploy a certain type of infrastructure is based on the level of traffic, which varies over time. For example, infrastructure expansion may be necessary to alleviate high congestion, but the level of congestion that makes infrastructure expansion worthwhile will not occur for years. But if no actions are taken in year 0, as infrastructure investments are postponed until traffic levels drastically increase, the moderate levels of congestion currently being experienced will result in decreases in value to users. Similarly, investments today in ITS may alleviate current conditions, but prove inadequate for future congestion levels. These trends are shown graphically in Figures A3 – A5, below. The range of possible net present values calculated for each alternative if deployed in year 0 for a span of 20 years is shown below. The NPV is shown as a function of variable traffic growth rates and years of deployment.


Figure A3. Infrastructure expansion NPV as a function of annual traffic growth rates and deployment year.

Figure A4. Congestion pricing system NPV as a function of annual traffic growth rates and deployment year.

Year 0 – Variable annual growth rate

5% annual growth – variable deployment year


Figure A5. HOT or freight lane NPV as a function of annual traffic growth rates and deployment

year. To help explain the data presented in Figure A3 – A5, a brief explanation of a portion of the data presented in Figure A4 is presented. The interpretation of these charts is similar for Figures A3 – A5. Figure A4 displays the expected NPV for a congestion pricing system as a function of both annual traffic growth rates and congestion pricing deployment year. Two lines are shown in the figure; one, the effect on NPV when a congestion pricing system is deployed in year 0 and the annual growth rate is unknown and two, the change in NPV by delaying deployment of the congestion pricing system when the annual growth rate is 5%. Each line is presented as a “2D” graph for easier inspection in Figures A6 and A8.

-60

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V (

in m

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)

Figure A6. Deployment of congestion pricing system in year zero. Annual growth rate is uncertain.


Figure A6 shows the effect that an unknown growth rate has on the NPV of a congestion pricing system when deployed in year 0. For the congestion pricing system modeled in the simulation, a maximum NPV is obtained from a year 0 deployment when the annual traffic growth rate is 5%. When the traffic grows at a faster or slow rate, the NPV decreases. The average NPV presented in Table A1, $57M, is the weighted average of the values presented in Figure A6, where the weighting is based on the frequency of annual growth rates.

$90,000

$95,000

$100,000

$105,000

$110,000

$115,000

$120,000

$125,000

$130,000

$135,000

0 5 10 15 20

year

NP

VDeterministic1% STD5% STD10% STD

Figure A7. Effect of volatility on NPV for the congestion pricing system. Note, for clarity, the data used in this graph is from a different version of the simulation as that used to create the other graphs. As a result, the numerical values are different and should not be compared.

Figure A7 shows the effect that volatility has on NPV. The take away from Figure A7 is that increasing volatility of the uncertainty surrounding the annual traffic growth rates decreases expected NPV for the congestion pricing system. The top line is the NPV with no uncertainty and the lower lines assume increasing uncertainty. The uncertainty here was modeled as a normal distribution with standard deviations of 1, 5 and 10% displayed. The standard deviation used in the simulation is 5%.

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Figure A8. NPV of congestion pricing system hen deployed in years 0 – 20. Annual growth rate is 5%.


Figure A8 shows the effect of delaying the deployment of a congestion pricing system, when the annual traffic growth rate is 5%. For this growth rate, the maximum NPV is found with a year 0 deployment, with NPV steadily decreasing over time. For example, deploying a congestion pricing system in year 20 with a 5% growth rate is less beneficial because of the large amount of traffic that has developed over the preceding 20 years that will likely overwhelm the congestion pricing system. A similar set of 2D graphs could be created for any cross sections in the 3D graphs presented in Figures A3 – A5. In general, the cross section that presents the annual growth rate as a variable shows the effect of uncertainty on the NPV and the cross section that presents a variable deployment year shows the effect on NPV of delaying the deployment of any particular alternative. Taken together, Figures A6 and A8 help determine the appropriate alternative that should be deployed in what year for a given annual growth rate. Addressing Uncertainty with ITS as a Real Option While it appears the congestion pricing system point design above is the best investment, the uncertain future state of congestion and the potential to deploy in later years makes commitment to this alternative questionable. Ideally, it would be beneficial to design an alternative that kept the upside of providing economic and environmental benefits if congestion continues to increase as expected, while limiting the downside of either wasting capital funds on an unnecessary project or preserving the ability to expand in the future. Limiting downside exposure while keeping the right to benefit from upside movements characterizes an option. Reconsidering the ITS alternatives, these ITS capabilities can be redesigned as real options because ITS enables the following;

1. Investment in ITS capabilities like HOT or freight lanes is expandable in the future. Properly designed, these alternatives could be expanded into a full congestion pricing system at a later date, if conditions warrant. This would be accomplished by augmenting the multiple HOT or freight lanes in several ways. First, the HOT/freight lanes would need to be integrated into a connected network, as opposed to a series of partially connected links. The network would need to create the ability to deploy a cordon around the KL city center, to ensure that traffic does not bypass the congestion pricing system. Second, the ITS capabilities on all HOT/freight lanes would need to be expanded and standardized. Currently, each private firm has deployed its own ITS technologies on their own toll road. Finally, a centralized management center is likely to be needed to coordinate and operate the entire congestion pricing system and act as a single point interface with decision makers. Costs for expanding from HOT/freight lanes come from the deployment of additional ITS capabilities, standardization of existing ITS capabilities and addition of new operational capabilities, both infrastructure and personnel.

2. Investment in the HOT or freight lane ITS infrastructure allows flexibility in operations. Aside from the real time pricing flexibility normally attributed to these systems, the flexibility to switch between modes is possible. For example, if freight lanes are initially desired in year 0, but passenger traffic increases more than expected, the freight lanes could be switched for use as HOT lanes. For the sections of the network where freight lanes would realistically be built, economic costs to switch would be modest as the ITS infrastructure would be identical. The switching option would likely only be available where high freight traffic is experienced, which is between Port Klang and the KL city center.

3. Investment in HOT lanes, freight lanes or the congestion pricing is likely to entail lower capital costs than full infrastructure expansions. Any of these investments could act to delay infrastructure investments while still meeting current traffic needs. If congestion reaches a point in the future that ITS capabilities can not alone handle traffic levels, traditional infrastructure would then be added. The addition of traditional infrastructure


may or may not change the manner in which the ITS capabilities are operated. For example, the price set on a congestion pricing system may change if additional capacity is added. The costs of delaying traditional infrastructure expansion after the addition of ITS capabilities is the need to add additional ITS capabilities to cover the expansion. This means that if an additional lane is added to an existing roadway, ITS equipment will be needed to offer coverage for the additional lane. It is assumed that ITS deployed in preceding years will not need to be replaced.

4. The ability to delay can be enhanced with improved information gathering capabilities on traffic flows and system usage than would otherwise be possible without properly designed ITS investments. ITS capabilities can be used to gather real time information concerning the operation of the transportation system, identifying overall traffic conditions and critical points in the system.

The range of possible actions is presented below in Figure A9.

Figure A9. Options available through ITS as a real option and SIOP. Figure A9 shows the potential options that are available to decision makers. If low congestion is projected and encountered, HOT or freight lanes could be enacted at time 0. As time progressed, the option to expand into a full congestion pricing system would be available. If a moderate level of traffic growth was forecasted and encountered, the initial deployment of HOT or freight lanes may be exercised either into a full congestion pricing system or infrastructure expansion that had been delayed may be enacted. High levels of congestion may reduce the feasibility of ITS to improve conditions and increase the need for immediate infrastructure expansion. Additional ITS capabilities could be deployed in later years if needed. Calculating the Value of Flexibility A variety of analysis tools are available for analyzing the flexibility presented by the inclusion of the ITS based real options, including options pricing analysis, decision tree analysis and simulations. The use of simulations in this case study to determine flexibility value was made because of the shortcomings found in options pricing and decision tree analysis. Options pricing analysis is based on financial options pricing methodology. As such, options pricing theory allows the exact valuation of options, but only under certain conditions. To use this tool, an underlying asset for the option must exist in a complete market. Additionally, a complete market allows a replicating portfolio (a portfolio of assets that have a similar return profile to the option in question and has already been priced in the marketplace) to be identified. By comparing the option returns to the replicating portfolio returns through arbitrage pricing, the option value can be obtained. In the case of the transportation system of interest, options pricing is not appropriate for several reasons. First, the identification of a market traded underlying asset is difficult, though the economic value derived from the use of transportation system could potentially serve as the underlying asset.


Second, option pricing methods work well when there are only one or two sources of uncertainty being considered, such as uncertainty in the future value of the underlying asset. Third, most decisions made concerning technical design choices are made by engineers who are not familiar with options pricing analysis. Decision tree analysis uses expected values and estimates of probabilities of occurrence for future events. Decision trees suffer their own problems. Unlike options pricing, decision trees have fundamental problems in their determination of value. Choice of risk adjusted discount rates is difficult and must be changed at different locations in the decision tree to adequately represent the changing nature of risk across different branches, stemming from different choices. The use of decision trees for longer term large scale systems that will exhibit many future choices is also problematic from a computational standpoint. The use of simulations was chosen in response to the shortcomings of other methods and as a path for future expansion of analysis. The large number of choices stemming from the system size, number of alternatives available and length of time of interest when coupled with the large number of parameters that are of interest made simulations a reasonable choice for analysis. While simulations suffer their own shortcomings, like the inability to obtain an exact valuation of flexibility, this is seen as less of an issue in real world situations, as a relative valuation between options and between options and non-flexible systems is of primary importance. Using a Monte Carlo technique to determine the NPV of the system with the previously described options under uncertainty from future growth in passenger and freight traffic and comparing the analysis to choosing alternatives in year 0 for 20 years, the following results were produced. As seen in Figure A10, the average expected value for the flexible system is greater than the average value of any of the non-flexible design options. Additionally, the low tail values seen in deploying the infrastructure are eliminated in the flexible system. Also, the distribution of the flexible system is weighted towards the higher end, when compared with the other ITS investments that have a heavier frequency of achieving lower NPVs in the possible range. While these benefits make the flexible system a superior choice over any of the non-flexible designs, this also comes at a cost. The very top ends of the NPV tails seen in the ITS alternatives are eliminated, partially due to the additional costs imposed on the system to create flexibility in the system and partially due to choice in decision rules.


Figure A10. Histograms of system with flexibility (top) compared with each of the non-flexible system alternatives. Averages are shown by the dotted line.

Analysis of Flexibility Value The value of ITS as a real option stems from three sources. The net benefits derived from these three sources is presented below.

Eq. 1

iesopportunitdecisionnewgatheringnformationifrombenefits

sinvestmentmajorpostponingfrombenefitsvalueoptionrealaasITS

++=

.

In this case study, benefits from postponing the major investment in infrastructure can come from two sources; the first source is due to discounting cash flows13 and the second source is from the timing of the investment. Future capital expenditures can either be higher or lower in present day dollars, depending on the economy and the type of system under consideration, causing this influence to have either a positive or negative impact. Postponing the infrastructure until traffic levels warrant its investment can also increase the value from infrastructure. More generally, matching the appropriate type of infrastructure investment (ITS or traditional) and operational mode to current conditions increases value. The benefit obtained from information gathering also has two influences. The first is information that is obtained simply by waiting for the future to unfold. The second source of information comes from the ITS system itself. ITS information technology capabilities can be harnessed to increase the understanding of the transportation system and future needs to a greater extent than could be done

13 Note that this value can be positive or negative. If future revenue streams, as well as costs, are discounted, the total net present value of the project may decrease.


without the ITS capabilities. In this case study, only the first type of information gathering is included. The last source of benefits behind ITS as a real option in this case study is the availability of a new set of decisions. At future times, additional capabilities may be possible that have not been identified at time zero. This source of benefits has not been looked at for this case study. The value of the flexibility in this case study is taken as the difference between the expected value of the ITS as a real option and the set of expected values generated if no additional decisions and information was present. Eq. 2 [ ]choicesflexiblenonsystemflexible NPVNPVvalueyflexabilit −−= If the flexibility value is greater than the cost of the option, then the option is economically feasible. Effects of Information, Discount Rate and Construction Cost Growth Several parameters have an important effect on the value of the flexibility of the option. The purpose of this section is to quickly look at the effect that three such parameters have on the value of flexibility and to discuss some of the implications this has on the system, enterprise and institutional architectures. In Figure A11, flexibility value is presented on the Y-axis and discount rate is displayed on the X-axis. The top group of four lines (in the exponential decay) represent the gathering of perfect information, while the bottom grouping of four lines represent low information generation through ITS. In each grouping, the future investment in construction is varied ranging from a -2% annual growth rate to a +2% annual growth rate (decreasing and increasing construction costs, respectively).

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Figure A11. Value of flexibility as a function of discount rate, construction cost growth,

information. Note, values used in this figure were taken from an earlier simulation and should not be compared with previous values.

From this chart several trends are observed. First, and not surprisingly, more information is better to have than less information. This requires that the technology and enterprise be designed to actively gather and use information, reducing “known unknown” uncertainties.

Perfect Information

Low Information

Increasing Construction Costs


Second, flexibility is less valuable when the future construction costs growth rate decrease. This has implications for transportation architecture and technology choices. This is the trend for many computer, software, and information technology based capabilities. What the future system architecture will look like will influence the value, and need for, flexibility that is designed into the system today. Third, flexibility is worth less at higher discount rates. This has an important implication for institutional arrangements. Government and private industry both use different discount rates when valuing investment decisions. This means that for the same project, the value of flexibility will likely be worth different amounts depending on whether a government agency or private entity is planning on undertaking the project. This is particularly import in countries like Malaysia, where both public and private institutions invest in transportation projects. Feasibility of Exercising Flexible Solutions While the aggregate costs and benefits of implementing the flexible system using ITS capabilities seem desirable, this may not be enough to actually exercise the option. For example, postponing infrastructure construction is very likely to create resistance from stakeholders with entrenched interests in continuing the status quo of building infrastructure, such as the construction industry. If the stakeholders with entrenched interests are well organized and concentrated, they will likely easily overcome the diffuse stakeholders that would benefit from lower transportation system costs (i.e. common taxpayers) or unidentified stakeholders that would have benefited from ITS capabilities (i.e. future ITS centered firms that currently either do not exist or are small and disorganized) [Olsen 1982] . These stakeholders can often “capture” regulatory agencies, causing the agencies to act in the best interests of these stakeholders instead of for society at large [Stigler 1971]. Activating a flexible solution requires that a standard way of doing business must be changed and substituted for another. While this may appear a good idea, especially if the current operations or system are performing poorly, this can also encounter resistance from a large number of stakeholders, as well as formal and informal norms. Stakeholders, such as program mangers may be opposed to flexibility, as they could view a change in direction as an opportunity to question their program and even perhaps kill it [Brand 2000, Mehndiratta 2000]. Groups that were against the project at t = 0 that lost the fight may have a new opportunity to question, slow or kill the program while the flexibility is being exercised. Formal norms, regulations, legislation, labor contracts, etc. may prevent such a radical change in direction, such as the abandonment of a poorly performing project [Ramirez 1997]. Informal norms, such as the desire to keep ambiguity in future decisions and system goals [Stone 2002] may act to cloud the issue of what should be done and when flexibility should be implemented. The challenge then is to design flexibility into the system that not only achieves the technical flexibility that is necessary to solve technical problems and meet economic constraints, but that also is able to overcome these two implementation barriers. This desire to look at considerations beyond technical and aggregate economic costs and benefits is analogous to looking at an “expanded objective function” in optimization, i.e. additional considerations need to be included for a “good” flexible solution to emerge. Political options are designed to explicitly address these concerns. Designing Political Options to Overcome Option Exercise Resistance While there are several ways of encouraging and enacting change (regulation/laws/mandates, pressure/coercion, incentives, crises, new norms), this case study focuses on exercising flexibility through selection of technologies and architecture choices.14

14 While the creation of new laws and regulations is a common way of forcing unwanted change, the use of incentives from a specific system architecture and set of technology choices to create support for change has an


As mentioned above, stakeholders, such as the construction industry, can be expected to resist exercise of real options that involved postponing or eliminating investments in infrastructure. Although these choices may be beneficial when evaluated from the standpoint of aggregate societal benefits and costs, the loss of business for delaying infrastructure falls on the construction industry. To avoid this loss in business, the construction industry can be expected to lobby government officials to oppose ITS investments that harm their interests. Any additional revenues that would be generated to the ITS industry from additional ITS investments would not have the same effect. This is due both to the fact that the ITS investments are much smaller, often up to an order of magnitude, and the ITS industry is younger and not as established as the construction industry in successfully playing the lobbying game. The primary mechanism of interest in this case study for overcoming this resistance is by creating a new stakeholder coalition that is capable of serving as a counterbalance to the interests of the construction industry, namely a coalition that would be in favor of ITS investments or expansion of ITS investments. It is proposed that the technology and architecture decisions that go into designing ITS investments to respond to technical needs be modified to take into account the needs of creating interest in ITS capabilities, as well as meeting the technical needs of the system. In this way, a transportation agency will be deploying technologies and architectures that can be used to create a “portfolio of stakeholders” that will have incentives tied to different aspects of the transportation system and which can be used to help create support for various technologies and architectures. ITS technologies and architectures are based on information technology, which are quickly becoming more useful and profitable for a host of non-transportation industries. Many ITS technologies, like smart cards, can find applications in other domains, such as banking, retail purchases, information collection and parking payments. Currently, most ITS applications are single use technologies and closed architectures that are optimized for use solely in the transportation system. Designing and deploying technologies that are capable of multiple uses or have an open architecture to allow new applications to be added are important for appealing to non-transportation users. A primary asset of many ITS technologies is that there is a built in user base that is very large. For example, if a congestion pricing system is deployed that utilizes smart cards, a large percentage of the population must own a smart card if they want access to areas inside the congestion pricing cordon. This translates into a large customer base for companies, if access to travelers and access to the deployed ITS technologies and architectures can be achieved. In this case study, one possible way of attracting new stakeholders supportive of ITS can be envisioned as follows. HOT lanes could be implemented at time zero, where access requires the use of smart cards. This would result in a certain proportion of the population that would desire use of the HOT lanes to purchase smart cards. If these cards had been previously co-designed with additional stakeholders, such as banks or retail outlets, to serve a multitude of purposes (HOT lane

interesting additional benefit that is not present in regulatory instruments. With enough political support or resistance, regulations can often easily be overcome. However, as described in Lessig [Lessig 2003], physical architecture is not as easily changed. Once a decision has been made and implemented, the system and technology “locks in”. While technology lock in may have several downsides associated with it, a potential upside is creating stability and reducing uncertainty for potential stakeholders that want to capitalize on a transportation agencies’ investment in ITS. In this manner, the creation of incentives and pressures tied to physical system architectures may make it easier to form coalitions in support of ITS as a real option. Additionally, as the technology becomes set and an increased number of stakeholders have application tied to the technology, network externalities become apparent. These network externalities create further technology lock in, but also create additional opportunities for complementary products and services (i.e. additional upside opportunities) to be offered. In this manner, we have flexible solution “lock in” as the initial adoption of the technology creates an expanded potential for upside opportunities.


access, ATM card, credit card, health insurance, etc.) additional value is being created, both for travelers/customers and for businesses associated with the smart cards. As most businesses benefit from a larger customer base, these businesses would likely be supportive of efforts to expand ITS usage. Expansion of HOT lanes into a congestion pricing system would require a much larger percentage of travelers own smart cards, increasing the customer user base further. The goal is to create a coalition of stakeholders supportive of ITS large enough to counter coalitions not in favor of ITS. Ideally, the new coalition should be just large enough to act as a counterbalance, as any larger would create difficulties in freely exercising non-ITS options. Inclusion of additional capabilities to be included in the ITS technology and architecture would need to be identified. This can be aided from some preliminary quantitative analysis. Analyzing the cash flows for each involved stakeholder, both the size and timing of the cash flows is important. To offset the desire that the construction industry has for the cash flow it would receive from an investment in infrastructure, a comparably sized cash flow must be created for the new stakeholder coalition. Additionally, the timing of the cash flows must be comparable, as large cash flows that occur much later than the exercise decision would likely not create early support needed for option exercise. The analysis of cash flow magnitudes and timing can help identify additional stakeholders and design decisions that are feasible for this task. If dual uses can not be found that would create the needed cash flows, then alternative methods would be needed for generating support for option exercise. In reality, a combination of methods will likely be needed. To further expound on this concept, the related concepts presented by Kingdon [Kingdon 2003] and Ashford [Ashford 2000] are brought forward. Kingdon presents the concept that problem, policy and politic streams must all coincide before an organization will act. Ashford forwards a similar notion that willingness, capacity and opportunity must all exist before organizations will act. Kingdon presents this model as independent streams that will only come together at certain times, and it is during these times that action must be taken or the window of opportunity will close. While he advocates that when the opportunity window closes “softening up” actions can be undertaken to prepare for the opportunity window to open again, he does not address how to actively create convergence of these three streams to create an opportunity window “on demand”. A goal of the design of the ITS as a real options , when taking into account political options, is to help enable such a convergence and the creation of windows of opportunity every time a need to trigger the flexibility arises. The flexibility objectives are aligned with the stakeholder coalition incentives, with the objective of creating support for the triggering of the flexibility when the need for the triggering arises. In essence, by properly designing the ITS as a real option so that the flexibility benefits are aligned with stakeholders that can exert pressure to trigger the flexibility, the streams of problem, policy and politics align and an opportunity window is created. In future work, specific technology and architecture recommendations for ITS capabilities brought about from political options will be identified. These technology and architecture recommendations will be analyzed from the perspective of individual stakeholders to assess the potential for overcoming barriers to exercise. Additionally, these technology and architecture recommendations will be incorporated back into the ITS as a real option design and re-analyzed, to ensure that the inclusion of political options did not adversely change either the technical efficacy or economic efficiency of the system to the point that it becomes unviable.

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