A simulation-based mult-objective genetic algorithm (SMOGA) for transportation network design problem

Anthony Chen, K. Subprasom, E. Z. Ji

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)


In the conventional transportation network design problem, travel demand is assumed to be known exactly in the future. However, there is no guarantee that the travel demand forecast would be precisely materialized under uncertainty. This is because travel demand forecast is affected by many factors such as economic growth, land use pattern, socioeconomic characteristics, etc. All these factors cannot be measured accurately, but can only be roughly estimated. Another issue in many existing transportation network design problems considers only one objective or a composite objective with a priori weights. It may be more realistic to explicitly consider multiple objectives in the transportation network design problem. We incorporate both travel demand uncertainty and multiple objectives into the transportation network design problem. It is formulated as a stochastic bi-level programming problem (SBLPP) where the upper level represents the traffic manager and the lower level represents the road users. To solve this SBLPP, a simulation-based multiobjective genetic algorithm (SMOGA) is developed. Numerical results are provided to demonstrate the feasibility of SMOGA.
Original languageEnglish
Title of host publication4th International Symposium on Uncertainty Modeling and Analysis, ISUMA 2003
Number of pages6
ISBN (Electronic)0769519970, 9780769519975
Publication statusPublished - 1 Jan 2003
Externally publishedYes
Event4th International Symposium on Uncertainty Modeling and Analysis, ISUMA 2003 - College Park, United States
Duration: 21 Sep 200324 Sep 2003


Conference4th International Symposium on Uncertainty Modeling and Analysis, ISUMA 2003
Country/TerritoryUnited States
CityCollege Park


  • Algorithm design and analysis
  • Demand forecasting
  • Economic forecasting
  • Genetic algorithms
  • Roads
  • Stochastic processes
  • Telecommunication traffic
  • Traffic control
  • Transportation
  • Uncertainty

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Control and Optimization
  • Modelling and Simulation

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