Prioritizing road extension projects with interdependent benefits under time constraint

Saeed Asadi Bagloee, Mohsen Asadi

Research output: Contribution to journalArticleResearchpeer-review

19 Citations (Scopus)


Since transportation projects are costly and resources are limited, prioritizing or sequencing the projects is imperative. This study was inspired by a client who asked: I have tens of approved road extension projects, but my financial resources are limited. I cannot construct all the projects simultaneously, so can you help me prioritize my projects? To address this question, the benefits and costs of all the possible scenarios must be known. However, the impacts (or benefit) of road extension projects are highly interdependent, and in sizable cases cannot be specified thoroughly. We demonstrate that the problem is analogous to the Traveling Salesman Problem (TSP). Dynamic change in travel demand during construction is another aspect of the complexity of the problem. The literature is yet to provide efficient methods for large cases. To this end, we developed a heuristic methodology in which the variation of travel demand during the construction period is considered. We introduce a geometrical objective function for which a solution-finding policy based on gradient maximization is developed. To address the projects interdependency, a special neural network (NN) model was devised. We developed a search engine hybridized of Ant Colony and Genetic Algorithm to seek a solution to the TSP-like problem on the NN based on gradient maximization. The algorithm was calibrated and applied to real data from the city of Winnipeg, Canada, as well as two cases based on Sioux-Falls. The results were reliable and identification of the optimum solution was achievable within acceptable computational time.
Original languageEnglish
Pages (from-to)196 - 216
Number of pages21
JournalTransportation Research Part A: Policy and Practice
Publication statusPublished - 2015


  • Prioritization or sequencing
  • Traveling salesman problem (TSP)
  • Gradient maximization
  • Neural network
  • Ant colony
  • Genetic algorithm

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