Cross-Asset Resource Allocation and Multi-Objective Decision Analysis Literature Review

Sydoriak, Jason; Lamoureux, Katie

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Cross-Asset Resource Allocation and Multi-Objective Decision Analysis Literature Review

2022-09-01
By Sydoriak, Jason ; Lamoureux, Katie

File Language:

English

Details

Creators:

Sydoriak, Jason ; Lamoureux, Katie
Corporate Creators:

John A. Volpe National Transportation Systems Center (U.S.)
Contributors:

Davis, Corbin ; Call, Steven
Corporate Contributors:

United States. Department of Transportation. Federal Highway Administration. Office of Planning
Subject/TRT Terms:
Publication/ Report Number:

FHWA-HEP-22-028
Resource Type:

Tech Report
Right Statement:

Public Domain
Geographical Coverage:

United States
Edition:

Final Report
Corporate Publisher:

United States. Department of Transportation. Federal Highway Administration. Office of Planning
Abstract:

This report documents the results of a literature review on the topics of Cross-Asset Resource Allocation (CARA) and Multi-Objective Decision Analysis (MODA). This literature review is intended to be used as an educational resource for transportation agencies and practitioners, providing a general/fundamental understanding of CARA and MODA. This report also suggests additional research areas for further study, makes references to ongoing CARA and MODA efforts, and highlights tools and resources that are available to advance the state of the practice. The literature review does not document the universe of research on these topics; instead, this report aims to summarize a selection of relevant, noteworthy resources in order to provide an overview of CARA and MODA, and how they may be used by transportation agencies as part of their performance-driven programming processes.
Format:

PDF
Funding:

51HW2LA500 - UK201, VK201
Download URL:

https://rosap.ntl.bts.gov/view/dot/93511/dot_93511_DS1.pdf
File Type:

[PDF - 707.75 KB]
Collection(s):

Federal Highway Administration
Main Document Checksum:

urn:sha-512:db9eb0f94546979732b802480d4c0706595d9e7ca2f5852803c268c604568872aa26c1f39f243b58b1a683719e2225a15b94dddacd3a6729fa3f175f3b919d79

File Language:

English

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