The potential of parsimonious models for understanding large scale transportation systems and answering big picture questions

A model with few variables is said to be parsimonious. If it is also analytically tractable, physically realistic, and conceptually insightful, it is said to be effective. Effective parsimonious models have long been used in fields such as economics and applied physics to describe the aggregate beha...

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Bibliographic Details
Published inEURO Journal on Transportation and Logistics Vol. 1; no. 1-2; pp. 47 - 65
Main Authors Daganzo, Carlos F., Gayah, Vikash V., Gonzales, Eric J.
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Elsevier B.V 01.06.2012
Springer-Verlag
Subjects
41A99
Business and Management
Continuum approximations
Effective parsimonious models
Logistics
Macroscopic modeling
Management Science
Mathematics Subject Classification
Operations Research
Operations Research/Decision Theory
Optimization
Research Paper
Urban mobility
41A99
Urban mobility
Continuum approximations
Macroscopic modeling
Effective parsimonious models
Mathematics Subject Classification
Logistics
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Summary:A model with few variables is said to be parsimonious. If it is also analytically tractable, physically realistic, and conceptually insightful, it is said to be effective. Effective parsimonious models have long been used in fields such as economics and applied physics to describe the aggregate behavior of systems as opposed to the behavior of their individual parts. In transportation, these models are particularly well suited to address big picture questions because they provide insights that might be lost when focusing on details. This paper presents an abbreviated history of effective parsimonious models in the transportation field, classified by sub-area: regional and urban economics, traffic flow, queuing theory, network dynamics, town planning, public transportation, logistics, and infrastructure management. The paper also discusses the benefits of these models—fewer data requirements, reduced computational complexity, improved system representation, insightfulness—and ways of constructing them. Two examples, one from logistics and one from urban transportation, are used to illustrate these points. Finally, the paper discusses ways of expanding the application of effective parsimonious models in the transportation field.
ISSN:2192-4376
2192-4384
DOI:10.1007/s13676-012-0003-z