OPTIMIZING TRANSPORTATION INFRASTRUCTURE PLANNING WITH A MULTIOBJECTIVE GENETIC ALGORITHM MODEL

Most transportation infrastructure planning studies are conducted with only a few alternative networks and land use scenarios. Generally, these studies analyze only a few transportation-related measures of effectiveness, such as vehicle-miles traveled, congestion, and air-quality emissions. When such a small subset of possible alternatives and variables is analyzed, it is probable that optimal alternative designs are not included. Ideally, all combinations of land use, infrastructure, and social variables would be examined; however, even a small city of 200 traffic zones with an average of 10 land uses will have more than 10 to the 200th power possible zoning alternatives. A more efficient way to examine an extremely large search set of feasible designs is to employ artificial intelligence techniques to quickly narrow the number of alternatives to be considered. The use of a multiobjective genetic algorithm model to optimize land use, infrastructure, social, and fiscal variables is demonstrated. The model considers three primary objective functions: minimizing travel time, minimizing per capita cost (as related to property taxes), and minimizing land use change. A large number of constraints are used. A Pareto fitness function is used to develop a small set of optimal solutions. The model was applied to Provo, Utah, which is a fast-growing community. More than 1.9 million alternative designs were evaluated, and 195 optimal Pareto plans were found. The Pareto set of optimal solutions indicated that solutions clustering higher-density development along existing arterials were most likely to meet the objectives.

• Record URL:
  • https://doi.org/10.3141/1685-07
• Summary URL:
  • http://scholar.google.com/scholar_lookup?title=OPTIMIZING+TRANSPORTATION+INFRASTRUCTURE+PLANNING+WITH+A+MULTIOBJECTIVE+GENETIC+ALGORITHM+MODEL&author=J.+Taber&author=R.+Balling&author=M.+Brown&author=K.+Day&author=G.+Meyer&publication_year=1999
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/0309071119
• Supplemental Notes:
  • This paper appears in Transportation Research Record No. 1685, Transportation Planning, Programming, Public Participation, and Land Use.
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Taber, J T
  • Balling, R
  • Brown, M R
  • Day, K
  • Meyer, G A
• Publication Date: 1999

Language

• English

Media Info

• Features: Figures; References; Tables;
• Pagination: p. 51-56
• Serial:
  • Transportation Research Record
  • Issue Number: 1685
  • Publisher: Transportation Research Board
  • ISSN: 0361-1981

Subject/Index Terms

• TRT Terms: Alternatives analysis; Artificial intelligence; Constraints; Genetic algorithms; Infrastructure; Land use; Minimization; Optimization; Property taxes; Transportation planning; Travel time; Zoning
• Uncontrolled Terms: High density development
• Geographic Terms: Provo (Utah)
• Subject Areas: Highways; Planning and Forecasting; Public Transportation; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 00781429
• Record Type: Publication
• ISBN: 0309071119
• Files: TRIS, TRB, ATRI
• Created Date: Jan 3 2000 12:00AM