Using GIS, Genetic Algorithms, and Visualization in Highway Development

Authors:

Jha, McCall, & Scholfeld

Instructor: Prof Crouch

Presenter: Mike Jones

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?

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?

Before:

After:

The rest of the story…

Approach:

A blend of GIS, GA, and CV.GIS: Geographic Information Systems

Data WarehouseGA: Genetic Algorithms

OptimizationCV: Computer Visualization

Feedback and based in intangiblesSecure stakeholder support

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

Map route to data:

Geographic Information Systems:

Mines data from various sources to provide a concise, easily understood representation.

Compute costs based on:Right of way.Environmental concerns.

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

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

Accounting note:

One time cost per unit length: construction, maintenance & pavement.

5-year period cost: accidentsNPV in base year: fuel, travel time

NO CONSISTENT METHOD FOR COMPARISON!!

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!

Optimization

Classic technique - derivatives:

COST

INITIAL

ESTIMATE

Optimization

Classic technique – Find local, not global, minimum

COST

Solution

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 <terminating condition>

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.

Visualization:

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

Drape of orthophoto onto terrain:

Photo-realistic rendering:

Photo-Simulation:

Visualization techniques:

Animation:

Stakeholder commitment

Once the optimal solution is reached, visualization will be used to educate and earn the commitment of key stakeholders:ManagementCitizensLegislators (funding)

Evaluation:

Questions: