Quantifying Economic Loses from Travel Forgone Following a Large Metropolitan

EarthquakeCaltrans Division of Research and InnovationResearch Connection Video Conferencing SeriesJuly 18, 2006

Professor Jim Moore, USC

Professor YueYue Fan, UCD

Sungbin Cho, USC/ImageCat™

Professor Anne Kiremidjian, LSJU

Stu Werner, Seismic Systems and Engineering Consultants

Acknowledgements and Disclaimer

• This work was supported primarily by the Earthquake Engineering Research Centers Program of the National Science Foundation under award number EEC-9701568 through the Pacific Earthquake Engineering Research Center (PEER).

• This work made use of the Earthquake Engineering Research Centers Shared Facilities supported by the National Science Foundation under award number EEC-9701471 and by the Federal Highway Administration through the Multidisciplinary Center for Earthquake Engineering Research (MCEER).

• Any opinions, findings, and conclusion or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the National Science Foundation or the Federal Highway Administration or the California Department of Transportation.

Key References

Moore, II, J.E, S. Cho, YY. Fan, and S. Werner (2006) Quantifying Economic Loses from Travel Forgone Following a Large Metropolitan Earthquake, PEER Report 2007/**, Berkeley, CA: Pacific Earthquake Engineering Research Center, forthcoming.

Kiremidjian, A., J. E. Moore, II, YY. Fan, N. Basoz, O. Yazali, and M. Williams (2006) Pacific Earthquake Engineering Research Center Highway Demonstration Project, PEER Report 2006/02, Berkeley, CA: Pacific Earthquake Engineering Research Center, forthcoming.

Werner, S. D., C. E. Taylor, S. Cho, J. P. Lavoie, C. K. Huyck, C. Eitzel, R. T. Eguchi, and J. E. Moore, II (2004) “New Developments in Seismic Risk Analysis of Highway Systems,” Paper 2189, Proceedings of the 13th World Conference on Earthquake Engineering, Vancouver, BC.

Cho, S.B., YY. Fan, and J. E. Moore, II (2003) “Modeling Transportation Network Flows as a Simultaneous Function of Travel Demand, Earthquake Damage, and Network Level of Service, Advancing Mitigation Technologies and Disaster Response for Lifeline Systems, Proceedings of the 6th US Conference, Long Beach, CA.

Motivation

• Caltrans District 7 was immediately attacked in the press following the Northridge Earthquake.• Some facilities had failed.• The media has a tendency to equate bad

outcomes with bad decisions.

• Repair of the I-10 bridges following the Northridge Earthquake in 1994 produced two controversies.• Bonuses paid to C. C Meyers to accelerate the

work were thought by some to be a poor use of public resources.

• Some prominent earthquake engineers criticized the design standards of the new bridges as not sufficiently earthquake resistant.

Insights

• There is more to a facility’s importance than Average Daily Traffic.• Available redundant capacity in the network should be

accounted for.• Prioritizing bridge retrofits (or reconstructions) is an

exercise in network design.

• Resources are scarce.• We cannot afford to design every transportation structure

in the inventory to withstand a maximum credible earthquake.

• District 7 still did one hell of a fine job deploying innovative, low cost retrofits prior to the Northridge Earthquake.

• There is considerable serious work to be done at the interface of transportation engineering and earthquake engineering.

Stages of Interdisciplinary Work

• Your field’s problems must be trivial, otherwise my own field’s methodologies would have already addressed them.

• Your field focuses on substantive problems, but these must be intractable, otherwise my field’s methodologies would have already addressed them.

• Denial, Anger, Bargaining, and Acceptance• Your field includes methodologies that might be

relevant to standing problems in my own field.• Understanding your field’s methods helps define

new problems and opportunities in my own field.

Imposing Pre-earthquake Travel Demand on a Post-earthquake Network:

• Fails to account for:• Movement along the travel demand curve, or• Shifts in the travel demand curve.

• Overestimates post-earthquake travel volumes.

• Generates unrealistic volume/capacity ratios.• Generates unrealistic travel delays.• Is a source of embarrassment for

transportation engineers who are attempting to persuade earthquake engineers of the importance of transportation engineering.

Applying Standard Transportation Planning Models to Earthquakes

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Treating Post-earthquake Travel Demand as a Function of Network Level of Service:

• Adds considerable economic and behavioral realism by allowing equilibria in the market for transportation to shift along a conventional demand curve.

• Better estimates post-earthquake travel volumes.

• Generates wholly realistic volume/capacity ratios.

• Generates wholly realistic, yet elevated zone-to-zone travel delays.

Stepping Back: Recognizing that Travel Demand is a Function of Level of Service

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Treating Travel Demand as a Function of Network Level of Service:• Substantially complicates network assignment

calculations intended to identify user equilibrium flows.

• Is outside standard practice, but almost within the grasp of standard computational tools, and should likely become standard practice.

• Generates an apparent reduction in total travel delay due to reduced travel demand, thereby• Making it appear that earthquakes improve

transportation system performance, and• becoming a source of embarrassment for

transportation engineers who are attempting to persuade earthquake engineers of the importance of transportation engineering.

Cumulative Distribution of Post-earthquake Volume/Capacity Ratios

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Variable-demandHayward 7.5

REDARS (Risks from Earthquake Damage to Roadway Systems)

• Software package supplied by the Federal Highway Administration (FHWA).

• An advanced seismic risk analysis (SRA) tool that enable users to better plan for and respond to earthquake emergencies.

• Methodology’s risk-based framework uses• models for seismology and geology,

engineering (structural, geotechnical, and transportation), repair and reconstruction, system analysis, and economics to

• estimate system-wide direct losses and indirect losses due to reduced traffic flows and increased travel times caused by earthquake damage to the highway system.

REDARS (cont.)

• Developed by FHWA and the Multi-Disciplinary Earthquake Engineering Research Center (MCEER) as a future public-domain software package.

• REDARS 2.0 incorporates a version of the Variable Demand Model operationalized in the PEER Highway Demonstration Project.

• Successfully applied to the• Memphis, TN highway network, a location that is vulnerable to a

repeat of the 1812 New Madrid zone earthquakes, and to a • limited portion of Caltrans’ highway network extending from

Fairfield to Oakland.

• The California project was intended to transfer technical expertise from the developer community within FHWA and MCEER to Caltrans.

REDARS methodology

System Components and Sites(locations, configurations, attributes

Origin Destination Zones and Travel Demand Functions

Earthquake Scenarios(Walkthrough Table)

Seismic Hazards(ground shaking, liquefaction, surface fault rupture)

Component Performance(damage states, repair cost, downtime, post-EQ functionality)

System States(road closures system-wide)

Transportation Network Analysis(post-EQ traffic volumes and travel times)

Losses / Consequences(economic losses, reduced access to key locations, etc.)

3. INCREMENTATION

2. SYSTEM ANALYSIS(for each EQ-scenario/simulation)

1. INITIALIZATION(Input Data, Model Parameters)

4. AGGREGATION(of results for all EQ-scenarios and simulations)

Incrementation CompletedTo Next EQ-

Scenario/Simulation

SRA MODULES

REDARS Seismic Risk Analysis (SRA) Modules

SRA Methodology

Network InventoryTraffic DataOrigin-Destinations ZonesTrip TablesTraffic ManagementNetwork Analysis Procedure

Economic Sectors· Locations· Productivity· DamageabilityStakeholder ImpactsEconomic Models

Data· Structural· Repair Procedures· Traffic StatesModels· Damage States· Post-EQ Functionality· Repair Costs· Uncertainties

Earthquake Walkthrough TableLocal Soil ConditionsGround Motion ModelsLiquefaction ModelsSurface Fault Rupture ModelsModel Uncertainties

Hazards Module Components Module

Economics ModuleSystem Module

The PEER Variable Demand Model is incorporated into REDARS 2.0

• For a given earthquake scenario and network data, REDARS 2.0 sequentially analyzes• ground motion• bridge / tunnel / roadway damage states• network configurations• executes a VDM analysis of network level of

service

• Reports results:• 7 days• 60 days• 150 days following the event.

Endogenizing Travel Demand:

• Requires parameterization of travel demand curves,• Which can be done on a zone-to-zone basis

• Based on baseline travel demands and costs, and • A gravity model calibration, or equivalent calculation.

• But which ideally would be based on a model of the urban activity system

• Makes it possible to determine• The total increased delay experienced by travelers

who remain on the network, and• The number of trips eliminated from the network,

and their value to the people who were previously making them, thereby

• Providing a long sought after source of credibility for transportation engineers who are traveling in the company of earthquake engineers.

Economic Losses Linked to Network Level of Service Following an Earthquake

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Obtaining Empirical Estimates of Coefficients for Monotone Travel Demand Functions

Empirical Travel Demand Curves are Non-monotonic

REDARS 2.0 Import Wizard

• Combines federal, state, and local data from public sources to generate transportation network data for study area.

• Public data sources used to compile the network database consist of:• National Highway Planning Network (NHPN) from

the Federal Highway Ad-ministration (FHWA),• FHWA Highway Performance Monitoring System

(HPMS)• FHWA National Bridge Inventory (NBI),• Bay Area transportation analysis zone map from

the Metropolitan Transportation Commission (MTC), and

• MTC 1998 Bay Area (passenger) trip table (Peak 4 hours).

The Bay Area Highway Network Model Characterized by the Import Wizard Includes:

• 10,154 directional links• 3,288 nodes,• 1,136 Travel Analysis Zone (TAZ) centroids,• 1,475 bridges, and• eight tunnels.

The San Francisco Bay Area Roadway Network Characterized by the REDARS 2.0 Import Wizard

Hayward Fault Scenario Earthquake

• Moment magnitude 7.1 event along the Hayward fault.

• Epicenter at -122.0866 o / 37.7266 o in decimal longitude and latitude.

• REDARS 2.0 estimates• 92 bridge collapses• 466 damaged bridges• 36 links subject to pavement failures due to

liquefaction• Full reconstruction or repair in 231 days,

assuming no constraints on resources

Bridge and Link Damage States Associated with the Hayward Fault Scenario Earthquake

Epicenter

Fault line

Variable Demand Model Algorithm Performance

• Four minutes of calculations using desktop computing resources.

• Travel demands associated with only 20% of the origin-destination zone pairs have converged to values consistent with the associated set of empirically estimated travel demand functions.

• The flows associated with these zone pairs account for 95 percent of the total trips in the system.

• The remaining 80% of the zone pairs account for only about 5 percent of the trips.

Variable Demand Model is Effective for Most travel, but not Most Zone Pairs

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Proportion of O-D Zone Pairs Ranked by Basline Trips

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80 % of Zone pairs for which VDM convergence is incomplete.

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7 Days After the

Earthquake60 Days After

the Earthquake150 Days After the Earthquake

Number of Severed Network Links 210 94 46

Total Trips (PCE) 847,535 1,169,352 1,193,284

Trip Reduction Relative to thePre-earthquake Baseline (%) 31.5 5.5 3.6

Average Travel Time (Minutes) 43.37 42.74 42.17

Travel Time Increase Relative tothe Pre-earthquake Baseline (%) 8.30 6.73 5.29

Additional Travel Time Accruing to Remaining Trips (PCE-Hours) 38,236 49,218 40,230

Value of Forgone Trips(PCE-Hours) 14,605 3,339 1,808

Total Impact (PCE-Hours) 52,842 52,557 42,039

Daily Impact ($Millions)1 3.80 3.78 3.03

Total Household Transportation Impacts

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Earthquake Impact (PCE-Hours).

Value of Forgone Trips

Additional Travel Time Accruing to Remaining trips

Total Impact

Extensions

• Treating freight flows.• Accounting for demand shifts, as opposed

to movements along a demand curve.• Decision support and network design.

Freight Trip Generation

• MTC does have freight origin-destination tables available.• Alternatively, employment data from the 2000 Census

Transportation Planning Package (CTPP) for the San Francisco Bay Area can be used to construct intra-regional freight trip generation estimates.

• The CTPP includes employment data by economic sector and by place of employment (by Traffic Analysis Zone).• Commodity flows between industries can be used to estimate freight

trip productions and attractions.• To convert this aspatial information to spatial flows, disaggregate

and assign these interactions to each TAZ based on 2000 CTPP employment by TAZ and by sector.

• Interregional flows are estimated by• Identifying network locations associated with inter-regional freight

movement, including seaports, airports, rail yards, and highway network entry points, and

• Assembling freight tonnage data for inbound and outbound freight for each of these sites,

Movement Along a Demand Curve versus a Shift in Demand

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Network Design Problem

• Broadly stated, our research goal is to find, subject to certain resource constraints, which network components should be retrofitted, and where new components should be added so that the overall performance of any metropolitan transportation system is maximally improved.

• This well-defined network design problem is important in the transportation network literature (Yang and Bell 1998).

Deterministic Network Design is Reasonably Difficult

• Individual users and network planners do not have the same objectives. Consequently, the network design problem involves multiple levels of optimization.• At the upper level, the system planner makes

decision on resource allocation to achieve the best system performance.

• At the lower level, the network users make their travel decision based on their individual travel preferences.

• For a large network, this kind of network design problem is computationally challenging.

Stochastic Network Design is Even More Difficult

• Uncertainty makes the pre-event network design problem very challenging. The problem has been formulated (Yang and Bell 1998), but never treated at a realistic scale.

• Subject to budget constraints, the objective is to find the transportation network configuration on which user equilibrium flows produce the minimum expected total congestion.

• This stochastic version of the problem is an embedded optimization problem with a tri-level structure.• The upper level is the decision by the network authority, in this

case a pre-event retrofit or reconstruction decision.• The intermediate level outcome, a function of the upper level

decision, is a random result of nature.• The lower level, a function of the upper level decision and the

intermediate outcome, is the decision by the network user.

Complexity

• Assuming that retrofitting transportation structures is not a matter of degree, but rather a binary decision;• then a network with M transportation

structures supporting its links presents 2M retrofit options.

• A random act of nature converts the network to a collection of L < M links.

• The total number of possible networks to be considered is thus an impossibly large value,

• Explicit enumeration of options is out of the question, so now what?

L

LLM C 2