Agent-Based Approaches in Freight Systems: Towards A Door-To-Door (D2D) Freight Optimization

This project focuses on developing an agent-based model to optimize door-to-door freight movement by simulating container flows across multimodal networks. The study includes constructing an open-source simulator that integrates freight energy efficiency models for trains and ships, utilizing in-house simulators NeTrainSim and ShipNetSim. Through scenario analysis, the model will evaluate energy use, policy impacts, and logistical efficiencies in container movements. This project anticipates that its results will guide sustainable practices in freight logistics, reduce greenhouse gas emissions, and improve decision-making for supply chain operations.

Language

  • English

Project

  • Status: Active
  • Contract Numbers:

    69A3552348303

  • Sponsor Organizations:

    Sustainable Mobility and Accessibility Regional Transportation Equity Research Center

    Morgan State University
    Baltimore, MD  United States 

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Sustainable Mobility and Accessibility Regional Transportation Equity Research Center

    Morgan State University
    Baltimore, MD  United States 
  • Performing Organizations:

    Virginia Polytechnic Institute and State University, Blacksburg

    Virginia Tech Transportation Institute
    3500 Transportation Research Plaza
    Blacksburg, VA  United States  24061
  • Principal Investigators:

    Rakha, Hesham

    Aredah, Ahmed

  • Start Date: 20240901
  • Expected Completion Date: 20251001
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01935539
  • Record Type: Research project
  • Source Agency: Sustainable Mobility and Accessibility Regional Transportation Equity Research Center
  • Contract Numbers: 69A3552348303
  • Files: UTC, RIP
  • Created Date: Oct 30 2024 2:46PM