Linkage problem in mathematical optimization of transportation networks

Methods for identifying optimal decisions for various problems in transportation networks have been extensively studied in the literature. Depending on the size of the problem, these studies often use metaheuristic methods to find solutions for various optimization problems. However, basic metaheuristic methods, such as genetic algorithms, do not perform well for the problems with linkages between decision variables. This paper investigates the linkage problem in the optimization of capacity changing network modifications. First, the linkage problem in the location optimization of dedicated bus lanes is investigated by enumerating all possible bus lane locations in a small grid network. The results suggest that the impact of implementing a bus lane at a given location depends on where existing bus lanes are located on a network. Thus, an optimization algorithm that is capable of learning linkages between decision variables is needed for the problem. Then, the optimization performance of two genetic algorithms, a Bayesian optimization algorithm, and a population-based incremental learning algorithm is compared to each other in terms of consistency and quality of the solutions, and exploration capability. Results show that algorithms that can learn linkages between decision variables perform better than the genetic algorithms.

100th Annual Meeting of the Transportation Research Board

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Work Title Linkage problem in mathematical optimization of transportation networks
Access
Open Access
Creators
  1. Murat Bayrak
  2. S. Ilgin Guler
Keyword
  1. Heuristic algorithms
  2. Bus lanes
  3. Location optimization
License In Copyright (Rights Reserved)
Work Type Conference Proceeding
Publication Date 2021
Related URLs
Deposited February 26, 2024

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Version 1
published

  • Created
  • Added 2023_BayrakGuler_Linkage_problem_in_location_optimization_of_dedicated_bus_lanes.pdf
  • Added Creator Murat Bayrak
  • Added Creator Sukran Ilgin Guler
  • Published
  • Updated

Version 2
published

  • Created
  • Deleted 2023_BayrakGuler_Linkage_problem_in_location_optimization_of_dedicated_bus_lanes.pdf
  • Added TRBAM-S-20-01805.pdf
  • Updated
  • Published
  • Updated Keyword, Subtitle, Description, and 2 more Show Changes
    Keyword
    • Heuristic algorithms, Bus lanes, Location optimization
    Subtitle
    • 100th Annual Meeting of the Transportation Research Board
    Description
    • Linkage problem in mathematical optimization of transportation networks
    • Methods for identifying optimal decisions for various problems in transportation networks have been extensively studied in the literature. Depending on the size of the problem, these studies often use metaheuristic methods to find solutions for various optimization problems. However, basic metaheuristic methods, such as genetic algorithms, do not perform well for the problems with linkages between decision variables. This paper investigates the linkage problem in the optimization of capacity changing network modifications. First, the linkage problem in the location optimization of dedicated bus lanes is investigated by enumerating all possible bus lane locations in a small grid network. The results suggest that the impact of implementing a bus lane at a given location depends on where existing bus lanes are located on a network. Thus, an optimization algorithm that is capable of learning linkages between decision variables is needed for the problem. Then, the optimization performance of two genetic algorithms, a Bayesian optimization algorithm, and a population-based incremental learning algorithm is compared to each other in terms of consistency and quality of the solutions, and exploration capability. Results show that algorithms that can learn linkages between decision variables perform better than the genetic algorithms.
    • 100th Annual Meeting of the Transportation Research Board
    Related URLs
    • https://annualmeeting.mytrb.org/OnlineProgramArchive/Details/15715
    Publication Date
    • 2021-01-01
    • 2021
  • Renamed Creator S. Ilgin Guler Show Changes
    • Sukran Ilgin Guler
    • S. Ilgin Guler
  • Updated