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

2. 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?

3. 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?

4. Before:

5. After:

6. The rest of the story…

7. 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

8. 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

9. Map route to data:

10. Geographic Information Systems: Mines data from various sources to provide a concise, easily understood representation. Compute costs based on: Right of way. Environmental concerns.

11. 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

12. 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

13. 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!!

14. 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!

15. Optimization Classic technique - derivatives: COSTCOST INITIAL ESTIMATE

16. Optimization Classic technique – Find local, not global, minimum COSTCOST Solution

17. 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

18. 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.

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

21. Drape of orthophoto onto terrain:

22. Photo-realistic rendering:

23. Photo-Simulation:

24. Visualization techniques: Animation:

25. 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)

26. Evaluation:

27. Questions: