Overview 0 Highlight sessions conducted at the 9th National

Conference on Transportation Asset Management, which took place on April 16-18, 2012, in San Diego, California

0 Four conference tracks: 0 Pavement and Bridges (24 presentations) 0 Beyond Pavement and Bridges (18) 0 Focus on Implementation (20) 0 Transit State of Good Repair (21)

0 Today is just a small taste of the conference experience

Objectives for Today 0 Enhance knowledge of all owners and operators of

pavements and bridge assets 0 Learn about successful asset management programs 0 Learn successful stakeholder communications

methods 0 Understand program development techniques 0 Learn to develop clear performance metrics

Presenters Today 0 Ron Hagquist, Operational Excellence Manager, Texas DOT 0 Use of Management Science Analytics for Asset Management at

Texas DOT 0 Sui Tan, Regional Streets and Locals Program Manager, California Metropolitan Transportation Commission 0 Performance-Based Approach to Funding Policy for Local

Streets and Roads 0 Eleni Bardaka, Graduate Assistant, Purdue University 0 Forecasting the Life of Asset Preservation Treatments: A

Comparative Evaluation of Alternative Tools and Techniques 0 Mohammadsaied Dehghani, Graduate Student, Virginia Tech 0 Corridor-Level Performance Measures to Support Resource

Allocation Strategies in Highways

Summary 0 Four presentations providing some highlights from

the 2012 National Transportation Asset Management Conference

0 Conference materials including presentations available from the TRB website

0 Planning has started for the 10th National Conference, most likely taking place in the spring of 2014 0 Pooled fund participation available

Questions? 0 Ron Hagquist, Operational Excellence Manager, Texas DOT 0 Use of Management Science Analytics for Asset Management at

Texas DOT 0 Sui Tan, Regional Streets and Locals Program Manager, California Metropolitan Transportation Commission 0 Performance-Based Approach to Funding Policy for Local

Streets and Roads 0 Eleni Bardaka, Graduate Assistant, Purdue University 0 Forecasting the Life of Asset Preservation Treatments: A

Comparative Evaluation of Alternative Tools and Techniques 0 Mohammadsaied Dehghani, Graduate Student, Virginia Tech 0 Corridor-Level Performance Measures to Support Resource

Allocation Strategies in Highways

Use of Management Science Analytics for Asset Management

At TxDOT

Ron Hagquist, TxDOT Operational Excellence Office Transportation Research Board Webinar September 19, 2012

ANALYTICS

Operations Research

Management Science

Determining Optimality & Efficiency Assessment

Data Analysis, Forecasting, &

Decision-Making Methods

DATA PROBLEMS

Data

Information

DECISION

Statistical Analysis

MS/OR* Decision Methods

Analytics Basic statistical analysis can often provide adequate basis for making decisions.

But sometimes the number of choices and mathematical considerations require structured decision methods and software.

* Management Science / Operations Research

Organizational Levels Of Analytics Capabilities

Transformed Experienced Aspirational

Prescribe actions Justify actions

Analytics used to:

………………………………………………

………………………………………………

………………………………………………

Guide actions

Organizational Analytics Capabilities

Well…

A key finding of the MIT Sloan School study was

that “top performing organizations use analytics

five times more than lower performers.”

-- Page 2, Figure 1 Analytics: the New Path to Value

Limited skill base in an organization

Lack of or limited understanding on how analytics can be used to improve an organization

Unclear ownership or governance of the data Lack of or limited sharing of data

A new administration, with an increased commitment to analytics for data-based decision making.

Creation of an Analytics Strategic Plan based on MIT study.

• Creation of in-house consulting group with operations research & management science capabilities.

• Creation of “Operations Research Analyst” job classification.

• Conducting tailored in-house OR/MS training for analysts.

A TxDOT project is currently a semifinalist for the INFORMS Innovations

in Analytics Award.

Increased emphasis on major analytics projects.

The Story of Analytics at TxDOT

8

“Gentlemen, we are out of money. We shall have to think. ” -Address to Parliament by Winston Churchill

So… how hard do WE have to think?

Needs & encumbrances A BASEBALL

Revenues A GOLF BALL

Challenge A TENNIS BALL

LESS:

EQUALS:

Stimulus funds AN AIR-RIFLE BB

Sports Analogies 2009 – 2030 Time Horizon

What to do ?. . . . . …. . find more money. . ?

$155

$27$12

$21$12

$5$0

$20

$40

$60

$80

$100

$120

$140

$160

$180

StandardRevenue

DoubleVehicle

RegistrationFees

Index the GasTax to CPI

End DPSTransfers

Raise theState GasTax by 5

Cents

Prop 12

Bill

ions

$ 77 B

Estimates Of Possible Revenue Enhancements

Possible Revenue Enhancements

What else to do? 1. Improve internal efficiencies 2. Improve allocation methods

ANALYTICS

Operations Research

Management Science

Determining Optimality & Efficiency Assessment

Data Analysis, Forecasting, &

Decision-Making Methods

DATA PROBLEMS

What else to do? 1. Improve internal efficiencies: Organizational (management audits, restructuring, modernization initiative) Operational (modernization initiative, operations research projects, Operational Excellence Office)

The benefit of ROW advance-purchase is avoiding property price escalation.

The benefits of accelerating project completion are [1] avoiding highway construction cost inflation and [2] earlier delivery of benefits to travelers.

Project Purpose: to develop a tool to find the economic balance between ROW advance purchase and ongoing project acceleration

Advance ROW Optimization Tool

www.library.ctr.utexas.edu/

District Number of Parcels

Difference in Mean Cost Due to Early

Acquisition

Mean Cost of Early Acquisition

Saving/Cost Ratio

Early Acquisition Parcel ID

Austin 20 $769,000 $181,000 4.25 5TE

Dallas 10 $44,653,000 $16,781,000 2.66 9

El Paso 19 $475,000 $475,000 1.00 3

Houston 28 $172,791,000 $183,057,000 0.94 2

Advance ROW Optimization Output

Equipment Replacement Optimization

TxDOT fleet: 17,000 vehicles Annual replacement budget: $50,000,000 Estimated annual savings = $2,500,000 - $5,000,000

Project Purpose: to develop a tool to find the minimum-cost vehicle-class replacement policy which can be updated as parameters change over time.

www. library.ctr.utexas.edu/

Year 1 Year 2 Year 3……………….Year 20

……………

…………… ……………

…………… ……………

…………… ……………

……………

Keep Replace

Keep Replace Keep

Replace

Solution Method: Dynamic Programming

Problem: What is the least-cost sequence of 200,000,000,000,000,000,000 K-R decisions ?

Project Evaluation Tool

the

A Benefit/Cost Toolkit for Anticipating & Evaluating the Impacts of Roadway Improvements

Examples of projects that PET can analyze:

1. Capacity Expansion & Tolling

2. Demand Management

3. Shoulder Lane Use

4. Speed Harmonization

5. Work Zone Phasing

6. Incident Management

7. Transit

Austin Network & Case Studies - $131 M

capacity expansion project in East Austin.

- 3 alternatives analyzed:

- Freeway upgrade;

- Tollway upgrade;

- TOD tolling

Project Financing Base Case: No Build

Freeway Upgrade

Tollway Upgrade

Tolling by Time of Day

NPV of New Tolling Revenues $0 -$88 K $118 M $142 M NPV of Initial and Future Project Costs $0 $123 M $138 M $138 M Project Financing Perspective NPV $0 -$123 M -$20 M $4.2 M Project Financing Perspective IRR N/A N/A 6.0% 8.2% Project Financing Perspective PP N/A N/A > 20 years 17.8 years

User Benefits Base Case: No Build

Freeway Upgrade

Tollway Upgrade

Tolling by Time of Day

Net Present Value $0 $690 M $376 M $265 M Internal Rate of Return N/A 60% 29% 21% Benefit / Cost Ratio N/A 6.6:1 3.7:1 2.9:1 Payback Period N/A 2.0 years 4.8 years 8.1 years

All PET files are available at www.caee.utexas.edu/prof/kockelman/PETwebsite

Management Science Applications for TxDOT

- Scoping Study -

Project Purpose: to conduct a management science “audit”

– to examine TxDOT business decisions which are amenable to

improvement with MS/OR methods – generating problem

statements for in-depth research.

www.library.ctr.utexas.edu/

WHY THIS PROJECT ?

1. Many operational problems at TxDOT are not unique to TxDOT, or even to transportation – they are problems common to many industries.

2. Only a small fraction of the universe of MS techniques have been applied to transportation.

3. Many of the successful decision-support projects at TxDOT have been due to the right department person finding the right researcher at the right time for the right problem – ad-hoc – how to systematize?

Process of mapping applications:

TxDOT OR/MS Experts

technique-driven

task-driven

Examples of Identified Analytics Applications • Forecasting – revenue; traffic; asset condition

• Multi-criteria prioritizing – research projects; hiring

• Location & scheduling optimization – emergency

response; fleet replacement; bridge & pavement management

• Project Selection – road capacity projects; bridge projects

• Multiple-objective funds allocation – MPO funds

allocations; capacity vs. maintenance allocations

• Life-cycle cost analysis – asset management

• Efficiency analysis – maintenance sections

“Gentlemen, we are out of money. We shall have to think. ” -Address to Parliament by Winston Churchill

What else to do? 2. Improve allocation methods

ANALYTICS

Operations Research

Management Science

Determining Optimality & Efficiency Assessment

Data Analysis, Forecasting, &

Decision-Making Methods

DATA PROBLEMS

Allocation Formula Advisory Project

Goal: To produce the best possible allocation formulas. Mission: To employ the best available expertise to review and improve TxDOT maintenance funds allocation formulas, and incorporating input from transportation stakeholders. Team: Texas Transportation Institute; Cambridge Systematics Inc.; expert panel; stakeholders; TxDOT Maintenance Division; and TxDOT Operational Excellence Office

Criteria Discipline Expert

Allocation Formula Advisory Project

Panel Selection Process:

When considering a system for allocating scarce transportation funds, there are six basic criteria:

1. Does this allocation produce the “most bang for the

buck”?

2. Does this allocation consider all relevant factors of

need?

3. Does this allocation make economic sense?

4. Do the formulas make mathematical sense (are they

specified correctly)?

5. Does the allocation consider fairness?

6. Does the allocation consider customer values?

Corresponding Mathematical Disciplines

1. Optimization – most bang for the buck

2. Decision Analysis – multiple considerations

3. Macroeconomics – “it’s the economy…”

4. Econometrics – getting the equations right

5. Fair Division – money is short

6. Market Analysis – knowing customer values

District Allocation Formulas

Customer Input –

Conjoint Analysis

TxCAP Road Quality

Index

Beginning Integration of Activities

Pavement Management

System

MAINTENANCE FORMULA PROJECT

35

Maximize customer value within a budget

A Report of Findings from the 2010 New Intelligent Enterprise Global Executive Study and Research Project

By IBM Analytics and the MIT Sloan School of Management

RECOMMENDATION 1: Focus on the biggest and highest value opportunities

TxDOT IMPLEMENTATION PRIORITIES:

1. Road maintenance optimization

2. Bridge maintenance optimization

3. Capacity project selection

4. Performance efficiency analysis

5. Workforce optimization

RECOMMENDATION 2: Within each opportunity, start with questions, not data

KEY QUESTIONS FOR TXDOT:

1. Within a budget, what is the least life-cycle cost of maintaining which roads, given traveler values?

2. Ditto for bridges.

3. Which capacity investment projects bring the greatest rates of return, and how to account for equity considerations?

4. How to systematically assess operational efficiency?

5. How to optimize personnel placement and training?

RECOMMENDATION 3: Embed insights to drive actions and deliver value

TRANSFORMING INSIGHTS INTO TXDOT ACTIONS: • Determine what pro forma information is needed in

dashboard form.

• Build pro forma dashboards linked to simulations of alternative scenarios.

• Quantify customer values and preferences in ways conducive to making decisions.

RECOMMENDATION 4: Keep existing capabilities while adding new ones

TXDOT ACTIONS TO AUGMENT ANALYTICS: • Provide training on basics of spreadsheet engineering,

data analysis, and optimal visual presentation of quantitative data.

• Provide training on intermediate and advanced analytics.

• Establish an Analytics career ladder supported by appropriate training.

• Provide briefings on the utility of analytics to managers.

RECOMMENDATION 5: Use an information agenda to plan for the future

A TXDOT INFORMATION AGENDA: • Chief Information Officer reconfigures agency data

systems to be integrated, consistent, and trustworthy.

• Align priority analytics activities to support strategic goals.

• Use a forward-looking information agenda to enable TxDOT to keep pace with advances in mathematical sciences and technology.

Analytics Career Ladder Analytics Training Analytics Competency Center

• Ad-hoc analytics projects

• Management Science Scoping Project

• Operational Excellence Office • Analytics Strategic Plan • In-house training • Analytics career track

Continued commitment to analytics for data-based decision making.

Carry out the Analytics Strategic Plan based on recommendations of MIT Sloan School Study, Analytics: The New Path to Value :

• Tailored in-house OR/MS training for analysts - enterprise-wide.

• Increased collaboration with Business and Industrial Engineering Schools

• Possible creation of departmental Analytics career path Increased emphasis on analytics projects

The Future of Analytics at TxDOT

Supporting Performance, Accountability, and Transparency at Regional Level

Sui Tan, MTC 9th National Conference on Transportation Asset

Management Webinar September 19, 2012

Business As Usual ???

• MAP- 21 focuses on performance-driven framework: Performance Accountability Transparency

How We Get into Performance

Unfair funding policy for local agencies

Population, mileage, and needs

“Worst first” practice prevalent

Conditions are facing steep decline

Gaps in full pavement management cycle

San Francisco Metropolitan Region

Population = 7.3 mil

9 counties

100 cities

42,500 lane-miles

1,500 miles of highway

23 transit agencies

7 toll bridges

One MPO: MTC

Bay Area Local Street and Road Conditions

Average PCI = 64

Reflect U.S. economic downturn

Closer to “tipping point”

60

62

64

66

68

70

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

San Francisco Bay Area Pavement Conditions

64 P

CI

Why are Local Streets and Roads a Regional Concern?

Supports All modes of

transportation

$50 billion replacement value

Conditions are facing steep decline

Escalating deferred maintenance jeopardizes funding for All transportation priorities

MTC’s StreetSaver®

Used by all 109 Bay Area jurisdictions; 300 nationwide

Developed 25 years ago

Designed specifically for local agencies

Minimize costs - maximize benefits

Purpose:

Document conditions & needs

Promote pavement preservation

Better Pavement Management in Bay Area

MTC is recognized by the FHWA as “one of the first regions in the country to implement a pavement management system— FHWA Office of Asset Management

Pothole Report: http://tinyurl.com/6t6y5lm

28-Year Needs Assessment

County Available Revenues Pavement Needs Non-Pavement

Needs Total Capital

Needs Total Remaining

Capital Needs

Alameda $ 2,148 $ 3,715 $ 4,082 $ 7,798 $ 5,650

Contra Costa $ 2,915 $ 3,111 $ 2,674 $ 5,786 $ 2,871

Marin $ 655 $ 865 $ 641 $ 1,506 $ 852

Napa $ 219 $ 1,087 $ 429 $ 1,516 $ 1,297

San Francisco $ 2,299 $ 2,416 $ 2,363 $ 4,778 $ 2,480

San Mateo $ 1,440 $ 1,929 $ 1,984 $ 3,913 $ 2,473

Santa Clara $ 3,374 $ 5,776 $ 5,118 $ 10,894 $ 7,520

Solano $ 488 $ 1,906 $ 1,289 $ 3,195 $ 2,707

Sonoma $ 994 $ 3,699 $ 1,319 $ 5,018 $ 4,023

REGION $ 14,531

$ 24,504

$ 19,899

$ 44,404

$ 29,872

($ in Billion)

Performance Measures

Deferred Maintenance Pavement Condition Index (PCI)

Remaining Service Life Threshold

Remaining Service Life Min % in Very Good Condition Max % in Poor Condition Pavement Maintenance Index Actual PM Work / PM Work Recommended

Performance Measures

Difficult to find a “One Size Fits All” performance measure

Guiding principles:

Measurable

Objective as possible

Can be fairly applied

Utilizes data widely available

Meaningful (promotes pavement preservation)

Performance Measure Criteria

Performance‐Based Funding Allocation Formula

Shortfall

Lane Miles

Pavement Preservation Index

Shifts practice from “worst first” to preventive maintenance

Replaces “Maintenance of Effort”

No advantage or disadvantage due to existing network features or budget

Promotes pavement preservation principles

Data from StreetSaver

Outcome-Driven Performance Measure

Your Tax Dollars At Work

Performance

Use of Asset Management

Promote sound pavement preservation

Environmental Sustainable technologies

Outcome driven – “worst first” to preventive maintenance

Accountability

Taxpayers know where the money is spent

Establish “maintenance of effort” for local agencies

Transparency

Report card on pavement conditions

Summary

Stretching Maintenance $$$

Environmental Sustainability

Performance Preservation

Contact

Sui Tan, P.E. StreetSaver Program Manager

Metropolitan Transportation Commission Oakland, California

(510) 817-5844 stan@mtc.ca.gov

Forecasting the Life of Asset Preservation

Treatments: A Comparative Evaluation of Tools and Techniques

Eleni Bardaka, Purdue University Samuel Labi, Purdue University

John Haddock, Purdue University

TRB Webinar: Transportation Asset Management September 19th 2012

Outline

• Introduction • Objectives & Scope • Literature Review • Methodology • Application to PMS Practice • Conclusions

2

Asset Performance Predictions • Essential Input for: Preservation Treatment Evaluation

- Ascertain effectiveness of actions - Establish optimal preservation strategies that minimize asset

life-cycle cost - Re-Evaluate design guides & preservation manuals

Project-Level Planning & Decision-Making

Network-Level Physical and Fiscal Needs Assessment - Project prioritization & programming

Asset Valuation

3

Problem Statement • Asset Management (AM) needs appropriate preservation

modeling techniques to ascertain the reliability of system decisions and operation.

• Most techniques used in the past yield models that:

▫ Need further refinement to become more reliable

Or

▫ Are not practical for PMS use

4

Study Objectives • Develop an enhanced methodology for performance

prediction that duly considers: ▫ Nature of preservation data ▫ Reliability of predictions ▫ Purposes and integration requirements of AM

• Use the developed methodology to: ▫ Predict post-rehabilitation performance ▫ Estimate treatment effectiveness (service life) ▫ Assess future needs

5

Study Scope AM Component System: PMS

Preservation Type: Rehabilitation, Thin Overlay

Data for illustration and configuration of the proposed methodology:

Indiana Interstates, rehabilitated during 1996-2006

Treatments: 1. Thin HMA Overlay 2. Structural HMA Overlay 3. Functional HMA Overlay 4. Crack & Seat PCC, and HMA

Overlay 5. Repair PCC and HMA Overlay 6. Rubblize PCC and HMA Overlay 7. PCC Overlay on PCCP

6

Review of Past Practices and Available Techniques 1/3

Empirical Technique

Highway Agencies

Advantages for PMS Application

Disadvantages for PMS

Application Illinois DOT, Washington State DOT, Louisiana DOT, Utah DOT, Minnesota DOT

- Requires one explanatory variable

- Does not require

investigation of the relationship between deterioration and its causes

- Not always reliable

Markov Chains Arizona DOT, Michigan DOT, Ohio DOT, Georgia DOT

- Reliable representation of the network condition

- Can be integrated with Network Programming and Budgeting

- Inconvenient for project-level management

Techniques Widely Used by Highway Agencies

Refs: Bham et al, 2003; Butt et al, 1994; Chou et al, 2008; Golabi et al, 1982; Jiang et al, 1988; Li et al, 1996; Li et al, 2006; Madanat et al, 1995; MnDOT, 2011; Silva et al, 2000; Pierce et al, 2004; UDOT, 2009; Wang et al, 1992 & 1994; Wang et al, 2010; Washington et al, 2011.

7

Empirical Technique

Investigated for PMS By:

Advantages for PMS Application

Disadvantages for PMS Application

Multivariate Regression

Rajagopal and George, 1991; Sebaaly et al, 1995; Mohamad et al, 1997; AASHTO, 2008 (ME-PDG)

- Relatively easy to estimate - Results easy to interpret

- Requires multiple explanatory variables

Bayesian Regression

Hajek and Bradbury, 1996; George,2000

- Useful for agencies that face lack of field data

- Requires a source of “prior” information/knowledge along with actual performance data

Autoregressive Models

Abu-Lebdeh et al, 2003 - Suitable for short-term performance prediction

- Requires information on previous year’s performance

Neural Networks Ferregut et al, 1999; Lou et al, 2001; Yang et al, 2003; Pekcan et al, 2008

- Can more easily model nonlinear data and complex interactions

- The estimation procedure resembles a “black box”

- Long and complicated equat.

Survival Analysis Gharaibeh and Darter, 2003; NCHRP 08-71, 2011

- Flexible in terms of data requirements

- Difficult result interpretation - Used for service life only

Refs: Aguiar-Moya et al, 2011; Amador and Mrawira, 2011; Attoh-Okine, 1994; Kajner et al, 1996; Kargah-Ostadi et al, 2010; Labi and SInha, 2004; Morian et al, 2011; Paterson, 1987; Paterson and Chesher, 1986; Prozzi and Madanat, 2003; Prozzi and Madanat, 2010; Wang et al, 2005; Washington et al, 2011; Yang, 2007; Yu et al, 2008.

8

Review of Past Practices and Available Techniques 2/3

Techniques Investigated for Implementation in PMS

Empirical Technique

Pavement Research

Advantages for PMS Application

Disadvantages for PMS

Application Mixed Linear Models (One-Way Random Effects)

Madanat and Shin, 1998; Archilla, 2006; Yu et al, 2007; Chu and Durango-Cohen, 2008; Hong and Prozzi, 2010

- Account for unobserved variables

- Reliable “population-wide” and

in-sample predictions

- Flexible to accommodate complicated data structures

- In-Sample predictions require the estimation of the model in parallel

Seemingly Unrelated Equations (SURE)

Prozzi and Hong, 2008; Anastasopoulos et al, 2012

- Captures the correlation of the different performance indicators, and thus provides a more efficient estimation than single-equation approaches

- Requires multiple explanatory variables and at least two indicators of pavement performance

Refs: Hsiao, 1993; Littell et al, 2006; Washington et al, 2011.

9

Review of Past Practices and Available Techniques 3/3

Other Available Techniques

PMS Purposes and Requirements Related to Treatment Analysis

• Project-Level Planning & Decision-Making ▫ Short-term/Long-term performance prediction for a

treatment applied to a specific pavement section

• Network-Level Needs Assessment ▫ Remaining Service Life for past-rehabilitated pavements

• Treatment evaluation ▫ Long-term treatment effectiveness models or estimates,

such as the average service life

10

Characteristics of Pavement Rehabilitation Data

Rehabilitation Treatment X

Contract 1 Constructed in year t

on j road miles

Condition Measurements

for

Section 1 - year t - year t+1

- … - year T

…...

Condition Measurements

for

Section j - year t - year t+1

- … - year T*

…… Contract i

Constructed in year t’ on j’ road miles

11

Formulation Assumption

•

Contract i

• 1st Level

Pavement Section j • 2nd Level

Repeated Measures • 3rd Level

12

3-Level Nested Linear Model Case Study: Repair PCC and HMA Overlay, Indiana IS

Independent Variable Parameter Estimate t-Statistic Constant -162.85 -5.67

Treatment Age [years] 4.104 25.28

Commercial Vehicles [1000vehicles/day] 2.588 6.52

Log(Pre-Treatment IRI) 93.338 6.81

Model Statistics Contracts: 12, Pavement Sections: 72, Observations: 694

166.370 2.01

62.267 4.13

121.120 17.44

Restricted LL (convergence) - 2,719.9

Restricted LL (regression) -2,902.2

Performance Indicator: IRI

13

Applicability to PMS Practice

•

14

Functional Overlay Treatment Age

Average IRI Estimate Lower 95% Limit Upper 95% Limit

12 109.8 102.6 117.0 13 114.7 107.2 122.1 14 119.5 111.9 127.2 15 124.4 116.5 132.3 16 129.3 121.0 137.5 17 134.1 125.7 142.6 18 139.0 130.2 147.8

Service Life Estimate and Range

Range of Service Life

There is 95% certainty that, 12.6 years after the treatment application, the average treatment performance will be equal to or higher than the performance threshold.

There is 95% certainty that, 15.8 years after the treatment application, the average treatment performance will be lower or equal to the performance threshold.

15

Applicability to PMS Practice

Treatment Service Life (years) [Linear Regression]

Service Life (years) [Mixed Models]

Thin HMA Overlay 12 (11-13) 12 (9.6-13.4)

Structural HMA Overlay >40 24.5 (20 -31.4)

Functional HMA Overlay 18 (17-19) 12.7 (11.3-14)

Crack & Seat PCC, and HMA Overlay

23 (17-29) 10.7 (9.6-12)

Repair PCC and HMA Overlay 17 (16-18) 13 (11.2-14.8)

Rubblize PCC and HMA Overlay 31 (27-35) 17.8 (16-20)

Average Treatment Service Life Estimation: Interstate Pavements, Indiana

BLUP using Mixed Model Regression

17

•

Applicability to PMS Practice

Performance-Based Needs Assessment Framework

(Sinha et al, 2005)

Determine Remaining Service Life of Each Pavement Section in the Network

Assess Physical Needs for Each Pavement Section in the Network

Assess Monetary Needs for Each Pavement Section in the Network

Determine Inventory

Asset Performance Models

Establish Performance Thresholds

Develop Cost Models for Each Preservation Treatment

Define Horizon Period

Select First Year of Horizon Period

Repeat Analysis for All Years within the Horizon Period

The BLUP method, using à priori information, can offer reliable predictions for each section in the network of rehabilitated pavements.

These predictions could be used to more reliably assess the future preservation needs of these pavements.

The effect of increased prediction reliability on needs assessment was found higher for short-term monetary needs estimation (35% underestimation).

18

Applicability to PMS Practice

Conclusions • The developed framework for treatment analysis :

▫ Takes into account the challenging structure of pavement rehabilitation data

▫ Covers the purposes and requirements of PMS regarding treatment analysis:

Project-Level Planning & Decision-Making → “population-wide” predictions

Network-Level Needs Assessment → conditional predictions (BLUP method)

Treatment Evaluation → Service Life Estimates and Range

▫ Accounts for unobserved variables:

Among pavement sections that belong to the same contract (e.g. construction quality)

Among measurements that refer to the same pavement section (e.g. layer thicknesses)

• The presented model formulation:

▫ Can be used for continuous performance indicators ▫ Can be easily modified to incorporate non-linear functions

19

Forecasting the Life of Asset Preservation

Treatments: A Comparative Evaluation of Tools and Techniques

Eleni Bardaka, Purdue University Samuel Labi, Purdue University

John Haddock, Purdue University

TRB Webinar: Transportation Asset Management September 19th 2012

Supplementary Slides

Disclaimer

• This research is part of an ongoing project for the Indiana DOT (INDOT).

• The research results may or may not be implemented by INDOT until the research project is finalized.

Impact of Proposed Methodology on Future Network Needs Estimation

• Investigate if increased reliability of disaggregate performance predictions has an impact on the aggregate network monetary needs.

• Case Study: Indiana Interstates

Inventory: Indiana Interstate Pavements Rehabilitated

during the period 1996-2006

Horizon Period: 2010-2020

Prediction Methods: 1. Regression

2. Mixed Linear Models

Performance Indicator: International Roughness Index (IRI)

Performance Records Availability: 1995-2009

Cost Models: Average Treatment Cost

(INDOT Contracts Division)

Impact of Proposed Methodology on Future Network Needs Estimation

Future Physical Needs of Interstate Pavements in Indiana, Rehabilitated 1996-2006

Impact of Proposed Methodology on Future Network Needs Estimation • Future Monetary Needs of Rehabilitated Interstate Pavements in Indiana

BLUP vs. Population-wide predictions

Treatment Age

Pavement Performance After Rehabilitation, represented by IRI [in/mile]

Observed Linear Regression 3-Level Nested Linear Model

5 48

6 57

7 50

8 58

9 62

10 - 98 74

11 - 102 79

12 - 105 83

13 - 109 88

14 - 112 92

15 - 116 96

Corridor-Level Performance Measures to Support Cross-Asset Resource Allocation Strategies in Highway

Mohammadsaied Dehghani Ph.D. Candidate, Center for Sustainable Transportation Infrastructure, VTTI

Gerardo Flintsch Professor, The Via Department of Civil and Environmental Engineering Director, Center for Sustainable Transportation Infrastructure, VTTI

TRB Webinar: Transportation Asset Management: Current Trends in Pavement and Bridges

Outline

o Introduction

o Methodology

o Case study

o Applications

o Summary/conclusion

Introduction - What’s the Problem?

Each component contributes to the overall wellbeing of our road segment

Need to know the overall health Corridor → System

Objective

o Developing a framework to aggregate performance of roadway assets into overall roadway performance measures for cross-asset resource allocation,

tradeoff analysis, etc.

Background

o Performance measures already developed for individual assets

o Several studies trying to aggregate all different types of performance measures for one asset (e.g. COST method)

o Aggregating performance of multiple assets into corridor-level (system-level) performance measures Not yet explicitly addressed

First Attempt

Pavements:•Cracking•IRI•Rutting•FWD Data•Etc.

Structural

Functional

Environmental

Safety

Pavement Health Rating

Bridges:•Primary Members•Abutments•Bridge Deck•Etc.

Functional

Environmental

Bridge Health Rating

Facilities:•Toll Plazas•Weigh Stations

Safety Features:•Signs•Pavement Markings

Other

Serviceability

Functional

Serviceability

Functional

Facilities Health Rating

Safety Features Health Rating

Structural

Safety

Corridor Health Rating

Performance Indicators

•PI_IRI•PI_Rut•PI_Cr

Performance Indicators•PI_Girder•PI_Abut•PI_Deck

Performance Indicators

•PI_Toll

Performance Indicators

•PI_Sign•PI_Mark

Quality Measures

Performance Indicators

Health Indicators

Asset Health Ratings

PI_Cracking

PI_Primary members

Performance Indicators

PI_IRI

PI_Rutting

PI_Abutment and Piers

PI_Deck

PI_Other elements

Structural

Functional

Asset Health Indicators

Structural

Functional

Corridor Health

Indicators

Corridor Structural Indicator

Corridor Functional Indicator

Corridor Overall Health Rating

Pave

men

tsBr

idge

s

Quality Measures

• Cracking• IRI• Rutting• FWD Data• Etc

• Primary Members

• Abutments• Bridge

Deck• Other

PI_Sign

PI_Marking

Structural

Functional

Corridor Safety

Indicator

Safe

ty F

eatu

res

• Pavement signs

• Guard rails• Pavement

markings PI_Guardrail

Safety

Safety

Safety

Revised Methodology

Performance Indicators (PI)

o Value based on quality measures o Reflects remaining time until quality measure

exceeds acceptable limits o Scale 0 to 10 10 – “Like New” condition 0 – Quality measure at unacceptable level

o Calculation different for each asset Different quality measures available

Calculations

PI_Cracking

PI_Primary members

Performance Indicators

PI_IRI

PI_Rutting

PI_Abutment and Piers

PI_Deck

PI_Other elements

Structural

Functional

Asset Health Indicators

Structural

Functional

Corridor Health Indicators

Corridor Structural Indicator

Corridor Functional Indicator

Corridor Overall Health Rating

Pave

men

tsBr

idge

s

Quality Measures

• Cracking• IRI• Rutting• FWD Data• Etc

• Primary Members

• Abutments• Bridge

Deck• Other

PI_Sign

PI_Marking

Structural

Functional

Corridor Safety

Indicator

Safe

ty F

eatu

res

• Pavement signs

• Guard rails• Pavement

markings PI_Guardrail

Safety

Safety

Safety

kjpijw

jz

k: type of quality measure j: Health Indicator type i: Asset type

1

_ min(10,max(0, -0.05 12))PI IRI IRI= × +

10ff

f

WEQPI

TEQ

= ×

f fi iWEQ EQ α= ×∑

ik kj

kij

kjk

PI pAHI

p

×=

∑∑

2

3

4

,

∑∑ ×

=

iij

iijij

j w

wAHICHI

∑∑ ×

=

jj

jjj

z

zCHIOCHR

Case Study

Pavement (for every 0.1 mile) IRI Rut depth Longitudinal cracks Alligator cracks Transverse cracks

Bridges (five condition states) Primary members Deck Abutment and piers

I-81 North bound Mileage 50-100 Assets

Pavements Bridges

Measures Structural Functional

Calculations

Results - Pavements

0

2

4

6

8

10

50 60 70 80 90 100

Stru

ctur

al

0

2

4

6

8

10

50 60 70 80 90 100

Func

tiona

l

Milepost

Results - Bridges

0

2

4

6

8

10

50 55 64 68 78 86 94 100

Stru

ctur

al

0

2

4

6

8

10

50 55 64 68 78 86 94 100

Func

tiona

l

Mile

Results (cont.)

0 2 4 6 8

10

50 60 70 80 90 100

Stru

ctur

al

0 2 4 6 8

10

50 60 70 80 90 100

Func

tiona

l

Mile

0 2 4 6 8

10

50 55 64 68 78 86 94 100

Stru

ctur

al

0 2 4 6 8

10

50 55 64 68 78 86 94 100

Func

tiona

l

Mile

Pavements Bridges

0 2 4 6 8

10

50 60 70 80 90 100 Stru

ctur

al

Results (cont.)

Homogenous corridor health indicators

Continuous profiles for each indicator

0 2 4 6 8

10

50 60 70 80 90 100 Func

tiona

l

0 2 4 6 8

10

50 60 70 80 90 100

Ove

rall

Cor

ridor

He

alth

Rat

ing

Mile

7.8

7.2

7.6

Corridor

Applications - Resource Allocation

Common “quality” scale needed for cross-asset resource allocation

Application (Simplified Example)

Assets Pavements Bridges

Health Indicators Functional Structural

Pa

vem

ent

Treatment Type

Treatment Cost

($/Lane-mile)

Extended Life (Years)

Maximum Functional Gain (unit)

Maximum Structural Gain (unit)

Preventive 10,000 3 1.5 - Corrective 80,000 8 5 2 Restorative 200,000 12 8 5

Reconstruct/ Rehabilitate 500,000 20 10 10

Bri

dge

Treatment Cost ($/ft2)

Extended Life

(Years)

Maximum Functional Gain (unit)

Maximum Structural Gain (unit)

Epoxy Overlay 70 6 4 - Deck

Replacement 120 15 6 1.5

Heavy Rehab 370 25 8 5 Reconstruction 1000 40 10 10

Pavement Bridge Lane. Mile 4 - Area (ft2) - 2000

Functional Indicator 6 4

Structural Indicator 5 7

Required Budget ($) 1,500,000

Available Budget($) 1,000,000

Application (Simplified Example)

Scenario selected

Treatment applied based on budget

Performance averaged over 5 year analysis period Optimal allocation

10% 20% 30% 40% 50%

7 7.2 7.4 7.6 7.8

8 8.2 8.4 8.6 8.8

50% 60% 70% 80% 90%

Bridge Budget Share

Indi

cato

r

Pavement Budget Share

Functional Indicator

Overall Indicator

Structural Indicator

Future Research

Consider more assets

Consider other performance measures, particularly, safety, and environmental factors

Conclusion

o Proposed method for homogenous aggregation of performance measures

o For strategic level decision making

Comparing investment trade-offs

Support cross-asset resource allocation

Acknowledgement

VDOT Tanveer Chowdhury,

Raja Shekharan and William Duke (Office of Asset Management)

Richard Thompson (Office of Structure and Bridge)

Jeff Price (Operations Planning Division)

MP 50-100

Detailed information can be found in: Dehghanisanij, M., Flintsch, G.W. , and Verhoeven, J.G. (2012). Framework for Aggregating Highway Asset Performance Measures: Application to Cross-Asset Resource Allocation. . In Transportation Research Record: Journal of the Transportation Research Board, No. 2271, pp. 37-44. Verhoeven, J. and Flintsch, G. (2011). Generalized Framework for Developing a Corridor-Level Infrastructure Health Index. In Transportation Research Record: Journal of the Transportation Research Board, No. 2235, pp. 20–27.

Please contact me if you have questions: Mohammadsaied Dehghani Ph.D. Candidate, Center for Sustainable Transportation Infrastructure, VTTI mdehghan@vt.edu Phone: 540-808-8347

TRB Webinar: Transportation Asset Management: Current Trends in Pavement and Bridges

Summary 0 Four presentations providing some highlights from

the 2012 Transportation Asset Management Conference

0 Conference materials including presentations available from the TRB website

0 Planning has started for the 10th National Conference, most likely taking place in the spring of 2014 0 Pooled fund participation available

Questions? 0 Ron Hagquist, Texas Department of Transportation 0 Use of Management Science Analytics for Asset Management at

Texas DOT 0 Sui Tan, California Metropolitan Transportation Commission 0 Performance-Based Approach to Funding Policy for Local

Streets and Roads 0 Eleni Bardaka, Purdue University 0 Forecasting the Life of Asset Preservation Treatments: A

Comparative Evaluation of Alternative Tools and Techniques 0 Mohammadsaied Dehghani, Virginia Polytechnic Institute and State University 0 Corridor-Level Performance Measures to Support Resource

Allocation Strategies in Highways