Using Multi-Attribute Tradespace Exploration for the...

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Using Multi-Attribute Tradespace Exploration for the Architecting and Design of Transportation Systems

Julia NickelFinal Presentation

(S.M. Engineering Systems)

February 25, 2010


Motivation

In Engineering Systems we seek to develop generally applicable, domain-independent methods. The application of sophisticated decision and design methods such as MATE* across domains supports this research by uncovering commonalities and domain differences. The transportation domain was chosen because of its relative similarity to the aerospace domain, which helps to control for a number of factors.

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*MATE = Multi-Attribute Tradespace Exploration (Ross et al. 2004)


Thesis Structure

Chapter Content

1. Introduction Motivation, Research Questions…

2. Literature Review MATE, Transportation planning ,CBA

3. Implementation Issues in applying MATE within the Transportation Domain

Updated CSER 08 paper: Domain differences

4. Case study 1: Chicago Airport Express At length case study, MATE, CBA, political feasibility

5. Case study 2: Portuguese High-Speed Rail

MATE Set-up, local context

6. Discussion Answer 4 research questions, future work

7. Appendix Chicago data, model summaries, interesting tradespaces

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Thesis Structure

Chapter Content

1. Introduction Motivation, Research Questions…

2. Literature Review MATE, Transportation planning ,CBA

3. Implementation Issues in applying MATE within the Transportation Domain

Updated CSER 08 paper: Domain differences

4. Case study 1: Chicago Airport Express At length case study, MATE, CBA, political feasibility

5. Case study 2: Portuguese High-Speed Rail

MATE Set-up, local context

6. Discussion Answer 4 research questions, future work

7. Appendix Chicago data, model summaries, interesting tradespaces

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Research Questions

1. What design methods are used for transportation systems planning? What are their limitations? What alternative system analysis methods are available?

2. What implementation issues arise if MATE, as a systems analysis method developed within the aerospace domain, is applied to the transportation domain?

3. What methodological insights emerge through the application of Cost-Benefit Analysis and MATE, individually and complementarily?

4. What insights are gained from the application of MATE to two transportation case studies for both MATE and transportation planning?

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Research Question 1/4

What design methods are used for transportation systems planning? What are their limitations? What alternative system analysis methods are available?

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Analytic toolsCost-Benefit AnalysisFinancial AnalysisEconomic Impact AnalysisEnvironmental Impact Analysis

Planning modelsRational planningSatisficingIncremental changeOrganizational processPolitical bargaining

References: de Neufville (1990), Luzzi (2001), Meyer and Miller (2001)

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Analytic toolsCost-Benefit AnalysisFinancial AnalysisEconomic Impact AnalysisEnvironmental Impact Analysis

Planning modelsRational planningSatisficingIncremental changeOrganizational processPolitical bargaining

References: de Neufville (1990), Luzzi (2001), Meyer and Miller (2001)

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Research Question 2/4What implementation issues arise if MATE, as a systems

analysis method developed within the aerospace domain, is applied to the transportation domain?

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Implementation issues encountered• A mission objective does not need to be an integral part of a project

definition• The complex, multi-layered stakeholder structure cannot be reduced to 1, 2,

or one aggregate stakeholder• Equity and value judgments cannot be excluded from the technical design

decision• Inheritance (hard and soft) bring with them stickiness of the status quo

(-> Prospect Theory)• Several cost types play an important role. A distinction needs to be made

between resource expenditure (money, time) and potentially non-reparable adverse effects that extend to third parties (harmful effects to life)


Decision making processes that do not involve a mission objective

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Economy Environment

Equity

Three E’s of political sustainability according to Fred Salvucci

1) Problem definition, analysis of options based on (marginal) changes to statusquo and unintended, adverse effects (often not fully evaluable).Example: dealing with inner-city congestion

2) Goal definition, recognition of competing objectives (some variation of 3 Es) and pledge to more or less reconcile them in whatever is done, actions filtered by what is politically acceptable to constituents

3) Mission focus: Stepping back to identify core mission, identification of solutions and evaluation criteria, evaluation, decision making, implementation

Challenge: Accurately identify implementation constraints on solutions (notoverly constrained so as to limit creativity, not under-constrained to generate

realistic solutions).



What methodological insights emerge through the application of Cost-Benefit Analysis and MATE, individually and complementarily?

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Chicago Airport Express Portuguese High-Speed RailCase studies




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Insights (MATE and CBA)Complementary methodsCBA: broad view, quantifies net benefits to society as a wholeMATE seeks to best meet decision makers’ expectations for a system




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Insights (MATE and CBA)Complementary methodsCBA: broad view, quantifies net benefits to society as a wholeMATE seeks to best meet decision makers’ expectations for a system


Chicago Airport Express


CTA Blue Line

Kennedy ExpresswayO’Hare

Loop




CTA Blue Line


Loop

Cab fare ~ $45




CTA Blue Line


Loop


Chicago Case Study Overview

(1) description of the approach(2) an introduction(3) identification of stakeholders(4) elicitation of their attributes using both CBA and MATE(5) generation of alternative system concepts(6) calculation of Cost-Benefit Values for different options(7) calculation of aggregated Utility-Expense and plot of

tradespaces for different options(8) tradespace exploration(9) evaluation of the best design option(10) discussion of the shortcomings of CBA (discussed in

Chapter 2) (11) discussion of political feasibility and potential opposition(12) technical recommendations

18© 2010 Massachusetts Institute of Technologyseari.mit.edu

Introduction

Attribute elicitationArchitecture concepts generation

CBA

MATE

Technical evaluation

Political feasibility evaluation


MATE and CBA Definitions

MATE:

CBA: Quantify and aggregate first-order (tangible) costs to society, discount over system lifetime, choose highest Net Present Value

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(DV1, DV2, …DVm) (X1, X2, … Xn) U(X)

Technical and cost models

Utility models, based on stakeholder interviews

Expense

Utility Explore tradespace, present,

discuss, start over


Approach

• 3 decision makers: City of Chicago, CTA, Private Operator

• Technical data from CTA, collected during summer internship 2008

• Missing data filled in by proxy assumptions• Semi-structured utility interviews with

decision makers elicited very different attributes -> grouped into hierarchy tree

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Hierarchy of Attributes

“Program” and portfolio decisions overlapdue to lack of continuous funding

How to deal with attributesat different levels?

Causal relationships for higher level goals not always well understood(no models available)

In thesis: decomposition(not ideal, ignores cross-reactions, “dummy” attributes)

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StakeholdersCity of Chicago ■CTA ΘPrivate Operator ж


Contractual Attributes

Contractual attributes (depend on negotiations between stakeholders)

•Freedom of Private Operator to make changes ж

Competition agreements with City, CTA ж

Length of concession contract ж Θ

Dispersion of (managerial) control ж Θ

Fare level growth per year ж

How to model? Solution in thesis for most part: modeling as design variables

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Rationale: Outcome of negotiations unknown, enumerate many different possibilities of outcomes to see how they perform

StakeholdersCity of Chicago ■CTA ΘPrivate Operator ж


Nr. Concepts Abbreviation

Description Rejected? Reason

0 Base case Base Minimal improvement to status quo infrastructure

yes Requirements specify minimal improvement from status quo

1 Direct service

Route 1a

Train solution, shared tracks with local train

yes Violates reliability requirement of 80% on time

Route 1b

yes Cannot be financed based on willingness to pay information from stakeholder interviews

2 Express service

Route 2a

Train solution, individual right-of-way no -

3 Bus Rapid Transit

BRT1 Rapid buses on Kennedy Expressway, no separate lane

yes Violates reliability requirement of 80% on time

BRT 2 Rapid buses on separate lane of Kennedy Expressway

no -

4 Blue Line Switch

BLS Rapid buses on separate lane on Kennedy Expressway replacing local

train, Airport Express on freed-up tracks from base case

no -

Screening Architecture Concepts

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Three architecture concepts meet cost constraint and minimum reliability requirements -> further MATE and CBA analysis


Rights-of-Way of Architecture Concepts

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Route 2


Rights-of-Way of Architecture Concepts

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Route 2 Blue Line

Kennedy Expressway


Cost-Benefit Analysis (CBA)

i - j)/ ( 1+d)t = NPV

B1 or C1: Capital cost, consisting of construction cost and vehicle acquisition, and (not applicable in the case study) income from sales of vehicles or infrastructure;B2 or C2: Increases C2 (or reduction B2 from discontinuing service) in operating cost, calculated for fuel cost, personnel (maintenance and operations) and vehicle replacement costs individually. B3 or C3: Reductions (B3) or increases (C3) in emissions, calculated for the emission types CO (Carbon monoxide), NOx (Nitrogen oxide), PM10 (Particulate Matter of of 10 micrometers or less), SOx (Sulfur Oxide) and VOC (Volatile Organic Compounds) individually; B4 or C4: Savings (B4) or increases (C4) in travel time to Blue Line riders, airport riders and drivers on the Kennedy Expressway, calculated for each group individually.

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Notional


CBA Results for Feasible Architecture Concepts (1)

In $ 2008 M Base

case

Route 2 BRT Blue Line Switch (excl.

former Blue Line)

d=7% 0 -97 -70 718

d=10% 0 170 -37 447


Smaller is better! (Savings to society)

Discount rates based on ESD.71, de Neufville (gov’t vs. industry discount rate)



In $ 2008 M Base

case


former Blue Line)

d=7% 0 -97 -70 718

d=10% 0 170 -37 447


Smaller is better! (Savings to society)



In $ 2008 M Base

case


former Blue Line)

d=7% 0 -97 -70 718

d=10% 0 170 -37 447


BRT is the only robust design for varying discount rates (7-10%)


Airport Express Model: Attributes and Design Variables (DVs)

Attributes DM Metric Weight

Expense

City's initial cost City $M 0.16

City's cost share City % 0.12

CTA initial cost CTA $M 0.2

Shared tracks with

Blue Line (BL)

CTA % BL

capacity

reduced

0.4

Operating costs PO $/day 0.1

Concession payment PO $M 0.2

Utility

QOS City Scale [1 to 5] 0.28

Span of service CTA Hrs/day 0.1

QOS_PO PO Scale [1 to 5] 0.2

Freedom to make

changes

PO Scale [1 to 5] 0.15

Competition

Agreements

PO Scale [1 to 5] 0.15

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DVs Range Measure

Concept [1, 2, 3] Route 2, BRT, BLS

Fare level [10, 20] $

Frequency [5, 20] headway in min

Travel time [20, 30] min

Amenities [1,2,3,4,5] Qualitative Scale, 5

most amenities

Span of service [16, ...,24] hr/day

City cost share [10, 50] %

Freedom to make

changes

[1,2,3,4,5] Qualitative Scale, 5

most freedom

Competition

agreements

[1,2,3,4,5] Qualitative Scale, 5

most protection from

competition

CTA payment [0, 100] $M

Max design-space = 3,600,000,000

Based on previously captured value propositions


City Tradespace (by Concept)

Route 2

BRT

BLS


n=20,000

Red (Route 2) is the only concept planners had initially considered.

Quality of Service:Fare levelFrequencyTravel timeAmenitiesSpan of service

City cost share

City’s initial cost

Concept= BRT, Fare= $10Frequency= 8 min Travel time =27minAmenities = 1Span of service = 19 hrsCity Cost Share= 10%Comp agreements= 5Freed. to make changes =4CTA paym.= 15


CTA Tradespace (by Concept)

Route 2

BRT

BLS


n=20,000CTA payment

Maintenance time

CTA payment =0Span of service < 18 hrs


Private Operator Tradespace (by Concept)

Route 2

BRT

BLS


n=20,000


Operating costsConcession payment

•Freedom to make changes

•Competition agreements

•QOS_PO:Fare levelFrequencyTravel timeAmenitiesSpan of service


•Freedom to make changes



Private Operator Tradespace (by Concept)

Route 2

BRT

BLS


n=20,000


If I relax maximum Operating Cost, more red designs are feasible

Operating costsConcession payment


Utility for Three Decision Makers (vs. Operating Cost)

CTA’s and Private Operator’s utility aligned and correlate with lower Operating Costs (red).

CTA


Best for all

infeasible

City UtilityQuality of Service:Fare levelFrequencyTravel timeAmenitiesSpan of service

PO Utility•Freedom to make changes



CTA Utility

Maintenance timeOps Cost ($/day)


Utility for Three Decision Makers (vs. Total Construction Cost)

CTA


Best for all





CTA Utility

Maintenance timeTCC ($B)


Utility for Three Decision Makers (vs. Total Construction Cost)

CTA


Best for all

Cheap design exists with high utility for PO and CTA





Concept= BRTSpan of service < 18 hrsCTA payment =0City Cost Share= 30%Fare= high

TCC ($B)


Fares: True Conflict of Interest

Private Operator

City

Lower fares (in $)

Higher fares



Fares: True Conflict of Interest

Private Operator

City

Lower fares

Higher fares


… but also possibility for negotiation


Insights from MATE and CBA

• Complementary methods• CBA: broad view, quantifies net benefits to society as a

whole, prescribes what should be cared about• MATE seeks to best meet decision makers’ expectations

for a system, can quantify intangible benefits• Issues with representation of constituents (value-based

interviewing)• Recommendation from case study: BRT

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Political Feasibility

• Dedicated BRT line under-uses resources(mitigation: use for toll)

• Prestige concerns of BRT (mitigation: marketing)• Employment generation and job losses (BLS: -50 + 120

= 70 net new jobs, but…)• Dispersion of investment: electricity generated

domestically, investments in vehicles and fuel leave state

• Uncertainty and scalability of operations

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Next Thesis*: Larger issue of political feasibility

• Tradeoff between political feasibility and technical optimality

• May limit solution space early on, without exploration of possible mitigation

• Hopefully: What factors increase political feasibility, and how can they be incorporated in the conceptual design phase?

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*Candidate for S.M. in Political Science, August 2010


Thank you.

Questions?

Contact: [email protected]

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References

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Citations• de Neufville, R. (1990). Applied Systems Analysis, Engineering Planning and Technology

Management. New York, NY, McGraw-Hill.• Luzzi, J. (2001). The Rational Planning Model in Forest Planning: Planning in the Light of

Ambivalence. Ecosystem Workforce Working Papers, Ecosystem Workforce Program, University of Oregon.

• Meyer, M. and E. Miller (2001). Urban Transportation Planning. New York, NY, McGraw-Hill.• Ross, A.M., Hastings, D.E., Warmkessel, J.M., and Diller, N.P. (2004) “Multi-Attribute

Tradespace Exploration as a Front-End for Effective Space System Design.” AIAA Journal of Spacecraft and Rockets. Jan/Feb 2004.

Publications• Nickel, J., Using Multi-Attribute Tradespace Exploration for the Architecting and Design of

Transportation Systems, S.M. Thesis, Engineering Systems Division, MIT, Feb 2010. [pdf]• Nickel, J., Ross, A.M., and Rhodes, D.H., "Comparison of Project Evaluation Using Cost-Benefit

Analysis and Multi-Attribute Tradespace Exploration in the Transportation Domain," 2nd International Symposium on Engineering Systems, Cambridge, MA, Jun 2009. [pdf]

• Nickel, J., Ross, A.M., and Rhodes, D.H., "Trading Project Costs and Benefits in Multi-Attribute Tradespace Exploration," 7th Conference on Systems Engineering Research, LoughboroughUniversity, UK, Apr 2009. [pdf]

• Nickel, J., Ross, A.M., and Rhodes, D.H., “Cross-domain Comparison of Design Factors in System Design and Analysis of Space and Transportation Systems,” 6th Conference on Systems Engineering Research, Los Angeles, CA, Apr 2008. [pdf]


Utility and Expense functions

Ux= wi*

Ex= wi*

• diminishing return function (γ= 1/2, δ=1/2) underlies utility and expense functions in the tradespaces

• weights wi are derived from weights distributes in interviews, normalized over the selected attributes


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