Strategic Investment Prioritization for Management of Risk of Land Development Adjacent to Critical Private Infrastructure Networks

Transcription

1 Strategic Investment Prioritization for Management of Risk of Land Adjacent to Critical Private Infrastructure Networks Shital Thekdi and James H. Lambert 2011 Winter Global Conference on Business and Finance Las Vegas, Nevada January 05, 2011

2 Agenda Motivation Goal Technical Approach Discussion Conclusions/Future Work 1

3 $5.25 billion Four years of construction Exacerbated by inefficient land use dullesmetro.com2

4 Need For This Effort to Forecast Risk and Regret Large scale transportation network Ten-year and nearer horizons in order to avoid surprise, regret, and belated action Need for repeatable and data-driven methodology Portfolio of alternatives evaluated using multiple performance criteria Escalating land values affects right of way acquisition Desire to avoid unnecessary congestion and costly retrofits Situation involves volatile and uncertain future conditions Need for prioritization and risk-based methodology 3

5 land development change adjacent to corridors 4

6 Motivation.. help improve access to affordable housing, more transportation options, and lower transportation costs while protecting the environment in communities nationwide Enhance economic competitiveness - through reliable and timely access to employment centers, educational opportunities, services and other basic needs by workers, as well as expanded business access to markets. Coordinate and leverage federal policies and investment. Align federal policies and funding to remove barriers to collaboration, leverage funding, and increase the accountability and effectiveness of all levels of government to plan for future growth Source: DOT, HUD, and EPA partnership for sustainable communities (2009) 5

7 Agriculture & Food Banking & Finance Chemical Commercial Facilities Communications Critical Manufacturing Dams Defense Industrial Base Emergency Services Energy Government Healthcare & Facilities Public Health Information Technology Nat l Monuments & Icons Nuclear Reactors, Materials & Waste Postal & Shipping Transportation Systems Water Diverse infrastructure systems are subject to risk and regret from adjacent land development Source: U.S. Department of Homeland Security 6

8 Agriculture & Food Banking & Finance Chemical Commercial Facilities Communications Critical Manufacturing Dams Defense Industrial Base Emergency Services Energy Government Healthcare & Facilities Public Health Information Technology Nat l Monuments & Icons Nuclear Reactors, Materials & Waste Postal & Shipping Transportation Systems Water Diverse infrastructure systems are subject to risk and regret from adjacent land development Source: U.S. Department of Homeland Security 7

9 Motivation (cont.) ASCE Report Card for America s Infrastructure Based on capacity, condition, funding, future need, etc. Between 2005 and 2009, little change in condition, but cost of improvements raised by $0.5 Trillion 8

10 Agenda Motivation Goal Technical Approach Discussion Conclusions/Future Work 9

11 Goal Develop repeatable data-driven methods to identify and prioritize sections of multimodal transportation infrastructure that are vulnerable to land development, and address the implications for risk assessment 10

12 Goal (cont.) Virginia 5,700 miles maintains of the transportation third largest miles of corridor state owned roads in the US 11

13 Technical Background Importance of proactive protection Avoiding costly retrofits Access management Preserving arterial capacity Minimizing disruption of nearby entities Example of related efforts SLEUTH Combines satellite imagery with NASA data in US Geological Survey Model Simulate future impact of future land use policy scenarios 12

14 Technical Background (cont.) Relevant factors in description of land development Distance from roads (county roads, highways, streets) Distance from bodies of water Distance from city centers Agriculture density Distance to recreation Population Population Projections Housing units/population ratio Soil type Slope Sources: Pijanowki 2003, Thekkudan

15 Technical Background (cont.) Linthicum and Lambert Risk management for infrastructure corridors vulnerable to adjacent land development. Journal of Risk Research Thekdi and Lambert Risk-Based Forecasts of Land for Protection of Mobility Options of People and Goods. Transportation Research Forum Thekdi and Lambert Risk-Based Priorities to Protect Transportation Infrastructure Corridors from Adjacent Commercial and Residential Annual Meeting Society for Risk Analysis. 14

16 Agenda Motivation Goal Technical Approach Discussion Conclusions/Future Work 15

17 Key Challenges Time domain Decisions for effective corridor protection policy How sensitive are decisions to the forecasts and related assumptions Regional classification based on similar characteristics Corridor sections based on Census blocks 197,000+ blocks in Virginia 43,000+ blocks adjacent to SMS 16

18 Key Challenges (cont.) Not all relevant factors for forecasts are available on a statewide level Parcel level land values not available in consistent statewide format Currently the data is maintained at the county level SMS joins 95 counties and 39 independent cities SMS neighbors five states (MD, WV, KY, TN, NC) Other relevant characteristics available at varying scales Population projections at county level Land values at zip code level Population at census block level 17

19 Potential Regret in Adjacent Land Congestion/inefficiencies Risk Time & resources Political Pollution Legal costs Greater efficiency in underutilized areas Opportunity Public transport expansion Park and ride facilities Economies of scale Tax revenues/employment 18

20 Quantification of Consequences Costs Time of planners Financial resources for planning Right of way Construction/Operational Access management Opportunities Projected economics of scale Reduction of road volume 19

21 Overview of Modeling Layers All Factors Time for Protection Influence Diagrams Fault Trees Scenario Analysis Markov Models Decision Trees Most Relevant Factors Max Regret Combinations Likelihood of Time to 20

22 Diverse Sources of Qualitative and Quantitative Data Diverse sources of quantitative and qualitative databases 21

23 Diverse Sources of Qualitative and Quantitative Data (cont.) Virginia Employment Commission population forecasts Landsat/NLCD satellite imagery elevation and land use Census TIGER population and housing City-data land value HUD Housing Affordability SOCD Building Permits Virginia Workforce Connection population and employment projections Small Business Administration HUBZones Conserved, protected, and federal lands City-data home age Property crime Distance from intersections Employment centers Others 22

24 Modeling Layer 1: What are the factors that most contribute to risk of adjacent land development Access Points Public Transportation Infrastructure Population Forecast Population Road Volume Reduction Right of Way Costs Legal Fees Commercial Land Values Undervalued Land Efficiency Potential Opportunities from Protection Time and $ Operational Employment Centers Time to Financial Costs And Opportunities Costs for Protection Access Mgmt Decision Uncertainty Objective 23

25 Modeling Layer 2: How can selected factors combine to result in maximum regret Unprotected Land Occurs Corridor not Protected Economic Environmental Political Insufficient Funds Unplanned Employment Land Values Climate/ Land Transp. Access Taxes Policy Employment Centers Pop. Growth Water Soil Roads Public Transport Capacity Flow Cost Accessib ility 24

26 Modeling Layer 3: What are the scenarios that contribute most to risk of land development Factors Emergent Conditions Socio-demographic Environmental Others Population forecast Present Time Future Time Employment forecast Decrease - - Land value current - Increase - Conservation easements Decrease - - As planned

27 Application of the Five Modeling Layers for Risk and Regret Employment Centers Undervalued Land Transportation Access Population Forecast Assessment Values Population Employment Centers Undervalued Land Transportation Access Likelihood Population Forecast Assessment Values Population Likelihood Employment Centers Undervalued Land Transportation Access Population Forecast Assessment Values Population Employment Centers Undervalued Land Transportation Access Population Forecast Assessment Values Population Likelihood Likelihood 26

28 Likelihoods of Adjacent Land Fuzzy reasoning Vague or incomplete knowledge Rule based logic If-then rules 27

29 Likelihoods of Adjacent Land High Medium Low 2000 miles identified as low 400 miles identified as high 28

30 Modeling Layer 4: What is the time horizon to adjacent land development 29

31 Modeling Layer 5: What actions can be effective to mitigate risk of adjacent land development Actions Taken Right of Way Planning Budgetary Planning No No Potential for Right of Way Planning Budgetary Planning Right of Way Planning Budgetary Planning Right of Way Planning Budgetary Planning Right of Way Planning Budgetary Planning No No No No No No No No 5 yrs 5 yrs 5 yrs C o n s e q u e n c e s 30

32 Modeling Layer 5: What actions can be effective to mitigate risk of adjacent land development (cont.) Actions Taken Surveying Budgetary Planning No No Potential for Right of Way Planning Access Management Right of Way Planning Access Management Right of Way Planning Access Management Right of Way Planning Access Management No No No No No No No No Planning Agencies Localities Private Developers C o n s e q u e n c e s 31

33 Prioritization of Corridors Vulnerable to Adjacent Land 0-5 Years 5-10 Years 10+ Years 32

34 Prioritization of Corridors Vulnerable to Adjacent Land (cont.) miles of corridor Access Points Bealeton Midland Calverton Calverton Catlett Low Volume Access Points High Volume Access Points

35 Prioritization of Corridors Vulnerable to Adjacent Land Time Horizon Corridor ID Priority Score # of Miles 0-5 yrs I-95 S1 1 3 I-81 S5 2 9 US-250 S yrs US-250 S6 1 4 US-250 S3 2 5 I-64 S yrs I-64 S5 1 8 US-250 S1 2 6 I-81 S

36 Interdependencies of Modeling Layers Influence Fault Scenario Markov Decision Diagrams Trees Analysis Models Trees 35

37 Agenda Motivation Goal Technical Approach Discussion Conclusions/Future Work 36

38 Related Literature and Interest Bier, V.M. et al., Optimal resource allocation for defense of targets based on differing measures of attractiveness, Risk Analysis28, no. 3 (6, 2008): Evaluation of Expected Life-Cycle Maintenance Cost of Deteriorating Structures -Jung S. Kong, M.ASCE, and Dan M. Frangopol, F.ASCE, JOURNAL OF STRUCTURAL ENGINEERING ASCE / MAY 2003 Evaluation of Expected Life-Cycle Maintenance Cost of Deteriorating Structures -Jung S. Kong, M.ASCE, and Dan M. Frangopol, F.ASCE, JOURNAL OF STRUCTURAL ENGINEERING ASCE / MAY 2003 Apostolakis, G.E. and D. M. Lemon, A screening methodology for the identification and ranking of infrastructure vulnerabilities due to terrorism, Risk Analysis25, no. 2 (2005):

39 Agenda Motivation Goal Technical Approach Discussion Conclusions/Future Work 38

40 Future Effort Prepare paper for The International Journal of Business and Finance Research Extend models and apply to other critical infrastructures 39

41 Thank You More information at: 40