Capturing Dependency Among Link Boundaries in a Stochastic Dynamic Network Loading Model
Name
Osorio_Capturing dependency.pdf
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2.09 MB
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Adobe PDF
Checksum (MD5)
3d4ec75028225c9017e4842b5dd9877a
Author(s)
Osorio Pizano, Carolina
Flotterod, Gunnar
Date Issued
June 2014
Journal
Transportation Science
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Citation
Osorio, Carolina, and Gunnar Flotterod. “Capturing Dependency Among Link Boundaries in a Stochastic Dynamic Network Loading Model.” Transportation Science 49, no. 2 (May 2015): 420–431.
Version
Author's final manuscript
Abstract
This work adds realistic dependency structure to a previously developed analytical stochastic network loading model. The model is a stochastic formulation of the link-transmission model, which is an operational instance of Newell’s simplified theory of kinematic waves. Stochasticity is captured in the source terms, the flows, and, consequently, in the cumulative flows. The previous approach captured dependency between the upstream and downstream boundary conditions within a link (i.e., the respective cumulative flows) only in terms of time-dependent expectations without capturing higher-order dependency. The model proposed in this paper adds an approximation of full distributional stochastic dependency to the link model. The model is validated versus stochastic microsimulation in both stationary and transient regimes. The experiments reveal that the proposed model provides a very accurate approximation of the stochastic dependency between the link’s upstream and downstream boundary conditions. The model also yields detailed and accurate link state probability distributions.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1287/trsc.2013.0504
