Shippers’ Behavior Study Through Developing and Calibrating their Utility Functions

Freight flows on a multimodal network and through alternative routes. Each mode and route decision are determined by shipper behavior. There are multiple factors behind shipper behaviors such as time, distance, cost, reliability, etc., each of which is related to the characteristics of the commodity being shipped. To effectively promote multimodal transportation requires in depth understanding of the shipper behavior, which is also critical to the planning and operations of the multimodal transportation system. In the wake of supply chain volatility, shipper behavior study carries its additional significance. When a key infrastructure element is disrupted such as a port closeout, how would the O-D flow respond to the lockout of a major port? There may be multiple alternative routes with different set of modes of transportation. In this case, shipper behavior study would help predict the potential distribution of the network flow and assist planners and operational managers in developing proper policies and measure to serve the stakeholders better. In a broader term, shipper behavior study allows re-optimize the distribution routes and modes of major products/commodities after system disruptions due to either political or natural reasons. Re-distribution of shipments over the network is not uncommon to happen in the private sectors. For instance, when the Long Beach port is jammed with significant delay to vessels, shippers such as Walmart would need to decide whether to divert its shipments via the Panama Canal to the Gulf Coast ports. Good planning shall have prepared all the potential options and means for the private sector changes when needs arises. In summary, shipper behavior directly contributes to the performance of transportation logistics on the national network and is therefore imbedded in the supply chain resiliency and reliability. The objective is to understand shipper’s utility function and study how to calibrate the function using machine learning techniques and with available and planned survey data.


  • English


  • Status: Active
  • Funding: $275541
  • Contract Numbers:


  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

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

    Center for Freight Transportation for Efficient and Resilient Supply Chain

    University of Tennessee Knoxville
    Knoxville, TN  United States  37996
  • Project Managers:

    Bruner, Britain

    Kaplan, Marcella

  • Performing Organizations:

    University of Tennessee, Knoxville

    Department of Civil and Environmental Engineering
    John D. Tickle Building
    Knoxville, TN  United States  37886

    Texas A&M University, College Station

    Zachry Department of Civil Engineering
    3136 TAMU
    College Station, TX  United States  77843-3136

    University of Illinois at Chicago (UIC)

    842 W Taylor St
    Chicago, IL  United States  60607
  • Principal Investigators:

    Han, Lee

    Wang, Bruce

    Zhang, Yunlong

    Mohammadian, Kouros

  • Start Date: 20231001
  • Expected Completion Date: 20240930
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01895583
  • Record Type: Research project
  • Source Agency: Center for Freight Transportation for Efficient and Resilient Supply Chain
  • Contract Numbers: 69-A3552348338
  • Files: UTC, RIP
  • Created Date: Oct 6 2023 6:05PM