Behavioral/Agent-Based Supply Chain Modeling Research Synthesis: Model Approaches

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Behavioral/Agent-Based Supply Chain Modeling Research Synthesis: Model Approaches

2018-02-13
By United States. Department of Transportation. Federal Highway Administration

File Language:

English

Details

Corporate Creators:

United States. Department of Transportation. Federal Highway Administration
Contributor:

Shabani, Kaveh ; Outwater, Maren L. ; Smith, Colin ; Purdy, Jeffrey
Corporate Contributors:

Second Strategic Highway Research Program (SHRP2)
Subject/TRT Terms:
Resource Type:

Brief
Right Statement:

Public Domain
Geographical Coverage:

United States
Corporate Publisher:

United States. Department of Transportation. Federal Highway Administration
Description:

A handful of public agencies in the United States are in the process of developing-or have already developed-behavioral/agent-based models of supply chain decisions and freight movements. The uptick in development of these new models is largely attributable to the planning need for better freight forecasts. Federal Highway Administration (FHWA) Broad Agency Announcement awards and the Strategic Highway Research Program (SHRP2) C20 program provided funding for these new approaches to designing and implementing freight demand modeling. These include agent-based supply chain freight models currently in use in Baltimore Metropolitan Council (BMC), Chicago Metropolitan Agency for Planning (CMAP), Florida Department of Transportation (FDOT), Metro (Portland, Oregon), Phoenix's Maricopa Association of Governments (MAG), and Wisconsin Department of Transportation (WisDOT). A number of these agencies additionally funded their own efforts, including Metro who added funding for data collection, and CMAP who added funding beyond the initial research.
Format:

PDF
Download URL:

https://rosap.ntl.bts.gov/view/dot/94041/dot_94041_DS1.pdf
File Type:

[PDF - 1.54 MB]
Collection(s):

Federal Highway Administration
Main Document Checksum:

urn:sha-512:4fde2cc4260f340b7131a95f56501cff4571f9d27934ac51497233cf7212ca91ae6447d7370e61bf43c6d331b1538dfc1f576fa6e1ce411f0a784ddd19db79c0

File Language:

English

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