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.
Language
- English
Project
- Status: Active
- Funding: $275541
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Contract Numbers:
69-A3552348338
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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
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Performing Organizations:
University of Tennessee, Knoxville
Department of Civil and Environmental Engineering
John D. Tickle Building
Knoxville, TN United States 37886Texas A&M University, College Station
Zachry Department of Civil Engineering
3136 TAMU
College Station, TX United States 77843-3136University 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
- TRT Terms: Decision making; Freight traffic; Machine learning; Ship lines; Supply chain management; Surveys
- Subject Areas: Freight Transportation; Planning and Forecasting;
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