Quantifying Economic Loses from Travel Forgone Following a Large Metropolitan Earthquake

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Quantifying Economic Loses from Travel Forgone
Following a Large Metropolitan Earthquake

• Caltrans Division of Research and Innovation
• Research Connection Video Conferencing Series
• July 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
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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.

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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.

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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.

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Insights

• There is more to a facilitys 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.

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Stages of Interdisciplinary Work

• Your fields problems must be trivial, otherwise
  my own fields methodologies would have already
  addressed them.
• Your field focuses on substantive problems, but
  these must be intractable, otherwise my fields
  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 fields methods helps define
  new problems and opportunities in my own field.

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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.

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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.

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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.

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Cumulative Distribution of Post-earthquake
Volume/Capacity Ratios
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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.
• Methodologys 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.

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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.

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REDARS methodology
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REDARS Seismic Risk Analysis (SRA) Modules
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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.

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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.

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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
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Empirical Travel Demand Curves are Non-monotonic
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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).

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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.

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

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

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Variable Demand Model is Effective for Most
travel, but not Most Zone Pairs
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Total Household Transportation Impacts
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Extensions

• Treating freight flows.
• Accounting for demand shifts, as opposed to
  movements along a demand curve.
• Decision support and network design.

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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,

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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).

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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.

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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.

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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 lt 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?