Capturing Dependency Among Link Boundaries in a Stochastic Dynamic Network Loading Model
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 t...
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Institute for Operations Research and the Management Sciences (INFORMS)
2016
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| Online Access: | http://hdl.handle.net/1721.1/101683 https://orcid.org/0000-0003-0979-6052 |
| _version_ | 1811093983246417920 |
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| author | Osorio Pizano, Carolina Flotterod, Gunnar |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Osorio Pizano, Carolina Flotterod, Gunnar |
| author_sort | Osorio Pizano, Carolina |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T15:53:44Z |
| format | Article |
| id | mit-1721.1/101683 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:53:44Z |
| publishDate | 2016 |
| publisher | Institute for Operations Research and the Management Sciences (INFORMS) |
| record_format | dspace |
| spelling | mit-1721.1/1016832022-10-02T04:54:50Z Capturing Dependency Among Link Boundaries in a Stochastic Dynamic Network Loading Model Osorio Pizano, Carolina Flotterod, Gunnar Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Osorio Pizano, Carolina 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. 2016-03-11T15:44:23Z 2016-03-11T15:44:23Z 2014-06 2013-04 Article http://purl.org/eprint/type/JournalArticle 0041-1655 1526-5447 http://hdl.handle.net/1721.1/101683 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. https://orcid.org/0000-0003-0979-6052 en_US http://dx.doi.org/10.1287/trsc.2013.0504 Transportation Science Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute for Operations Research and the Management Sciences (INFORMS) MIT web domain |
| spellingShingle | Osorio Pizano, Carolina Flotterod, Gunnar Capturing Dependency Among Link Boundaries in a Stochastic Dynamic Network Loading Model |
| title | Capturing Dependency Among Link Boundaries in a Stochastic Dynamic Network Loading Model |
| title_full | Capturing Dependency Among Link Boundaries in a Stochastic Dynamic Network Loading Model |
| title_fullStr | Capturing Dependency Among Link Boundaries in a Stochastic Dynamic Network Loading Model |
| title_full_unstemmed | Capturing Dependency Among Link Boundaries in a Stochastic Dynamic Network Loading Model |
| title_short | Capturing Dependency Among Link Boundaries in a Stochastic Dynamic Network Loading Model |
| title_sort | capturing dependency among link boundaries in a stochastic dynamic network loading model |
| url | http://hdl.handle.net/1721.1/101683 https://orcid.org/0000-0003-0979-6052 |
| work_keys_str_mv | AT osoriopizanocarolina capturingdependencyamonglinkboundariesinastochasticdynamicnetworkloadingmodel AT flotterodgunnar capturingdependencyamonglinkboundariesinastochasticdynamicnetworkloadingmodel |