Using GIS, Genetic Algorithms, and Visualization in Highway Development

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• Using GIS, Genetic Algorithms, and Visualization
  in Highway Development
• Authors
• Jha, McCall, Scholfeld
• Instructor Prof Crouch
• Presenter Mike Jones

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Problem

• How can we best leverage Information Technology
  to improve the planning process for highway
  development in order to achieve the optimal
  balance of cost, schedule, and performance?

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Motivation

• In Maryland alone, cost overruns in highway
  projects cost 297.6 M in 1999.
• This problem is not unique to Maryland. Do you
  remember the Coliseum Central Highway Improvement
  Project?

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Before
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After
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The rest of the story
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Approach

• A blend of GIS, GA, and CV.
• GIS Geographic Information Systems
• Data Warehouse
• GA Genetic Algorithms
• Optimization
• CV Computer Visualization
• Feedback and based in intangibles
• Secure stakeholder support

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Geographic Information System

• Mines data from various sources to provide a
  concise, easily understood representation.
• Aerial photographs.
• MDPropertyView
• Raster Property maps and attribute information.
• Maryland State Highway Administration
• Soil layers, floodplains, wetlands

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Map route to data
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Geographic Information Systems

• Mines data from various sources to provide a
  concise, easily understood representation.
• Compute costs based on
• Right of way.
• Environmental concerns.

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Geographic Information Systems

• Mines data from various sources to provide a
  concise, easily understood representation.
• Compute costs based on
• Right of way.
• Environmental concerns.
• By assigning high cost to environmentally
  sensitive areas

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Geographic Information Systems

• Mines data from various sources to provide a
  concise, easily understood representation.
• Computed costs consider
• Agency costs Right of way, environmental,
  pavement, construction, maintenance, earthwork.
• User costs accidents, travel time, vehicle
  operation

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Accounting note

• One time cost per unit length construction,
  maintenance pavement.
• 5-year period cost accidents
• NPV in base year fuel, travel time
• NO CONSISTENT METHOD FOR COMPARISON!!

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Optimization

• Classic technique derivatives 2D example
• Assume an initial solution.
• Take derivative.
• If derivative is positive, decrease estimate and
  repeat
• If derivative is negative, increase estimate and
  repeat
• If derivative is zero, optimal solution found!

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Optimization

• Classic technique - derivatives

INITIAL ESTIMATE
COST
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Optimization

• Classic technique Find local, not global,
  minimum

Solution
COST
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Genetic Algorithms

• In the class of global search heuristics called
  evolutionary algorithms.
• Use multiple initial guesses, called the initial
  population
• Evaluate the fitness of each individual in the
  population
• Repeat
• Select best-ranking individuals to reproduce
• Breed new generation through crossover and
  mutation (genetic operations) and give birth to
  offspring
• Evaluate the individual fitnesses of the
  offspring
• Replace worst ranked part of population with
  offspring
• Until ltterminating conditiongt

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Recursive solution

• The cost calculated by the GIS is the fitness
  criteria used in the GA. Ideally, the GA will
  converge to the optimal solution.

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Visualization

• Used to determine feasibility of the proposed
  solution based on intangibles.

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Drape of orthophoto onto terrain
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Photo-realistic rendering
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Photo-Simulation
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Visualization techniques

• Animation

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Stakeholder commitment

• Once the optimal solution is reached,
  visualization will be used to educate and earn
  the commitment of key stakeholders
• Management
• Citizens
• Legislators (funding)

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Evaluation
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Questions