A heuristic decomposition method for large-scale traffic assignment: Aburra Valley case study

Authors

  • Laura Lotero National University of Colombia
  • Patricia Jaramillo National University of Colombia
  • Claudia Cristina Rave Government of Antioquia

DOI:

https://doi.org/10.17533/udea.redin.17225

Keywords:

traffic assignment problem, large-scale model, decomposition methods, heuristics

Abstract

Traffic assignment is one of the most important stages in transportation planning; however, its application to real case studies in medium- to largesized cities makes the solution of the model difficult because of the scale and high computational complexity related to the combinatorial and non-linear nature of the problem. The aim of this paper is to present a decomposition method based on sub-region analysis, and a simple heuristic rule for solving large-scale traffic assignment problems. This reduces the total amount of variables and equations of the model and offers a practical solution in a reasonable computing time. The proposed traffic assignment model is applied to the multimodal main road network of the Aburra Valley, Colombia. Such an application of a great amount of variables and equations converts the model into a large-scale problem. The proposed method considerably reduces the computational complexity of the problem, and it reveals accurate solutions in an execution time which is reasonable for such a large-scale model.

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Author Biographies

Laura Lotero, National University of Colombia

Department of Computer and Decision Sciences, Faculty of Mines.

Patricia Jaramillo, National University of Colombia

Department of Computer and Decision Sciences, Faculty of Mines.

Claudia Cristina Rave, Government of Antioquia

Secretariat of Infrastructure.

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Published

2013-10-24

How to Cite

Lotero, L., Jaramillo, P., & Rave, C. C. (2013). A heuristic decomposition method for large-scale traffic assignment: Aburra Valley case study. Revista Facultad De Ingeniería Universidad De Antioquia, (68), 187–197. https://doi.org/10.17533/udea.redin.17225