Stochastic network link transmission model
This article considers the stochastic modeling of vehicular network flows, including the analytical approximation of joint queue-length distributions. The article presents two main methodological contributions. First, it proposes a tractable network model for finite space capacity Markovian queueing...
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Article |
Language: | en_US |
Published: |
Elsevier Science
2017
|
Online Access: | http://hdl.handle.net/1721.1/110622 https://orcid.org/0000-0003-0979-6052 |
_version_ | 1811093984631586816 |
---|---|
author | Flotterod, Gunnar Osorio Pizano, Carolina |
author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Flotterod, Gunnar Osorio Pizano, Carolina |
author_sort | Flotterod, Gunnar |
collection | MIT |
description | This article considers the stochastic modeling of vehicular network flows, including the analytical approximation of joint queue-length distributions. The article presents two main methodological contributions. First, it proposes a tractable network model for finite space capacity Markovian queueing networks. This methodology decomposes a general topology queueing network into a set of overlapping subnetworks and approximates the transient joint queue-length distribution of each subnetwork. The subnetwork overlap allows to approximate stochastic dependencies across multiple subnetworks with a complexity that is linear in the number of subnetworks. Additionally, the network model maintains mutually consistent overlapping subnetwork distributions. Second, a stochastic network link transmission model (SLTM) is formulated that builds on the proposed queueing network decomposition and on the stochastic single-link model of Osorio and Flötteröd (2015). The SLTM represents each direction of a road and each road intersection as one queueing subnetwork. Three experiments are presented. First, the analytical approximations of the queueing-theoretical model are validated against simulation-based estimates. An experiment with intricate traffic dynamics and multi-modal joint distributions is studied. The analytical model captures most dependency structure and approximates well the simulated network dynamics and joint distributions. Even for the considered simple network, which consists of only eight links, the proposed subnetwork decomposition yields significant gains in computational efficiency: It uses less than 0.0025% of the memory that is required by the use of a full network model. Second and third, the proposed SLTM is illustrated with a linear test network adopted from the literature and a more general topology network containing a diverge node and a merge node. Time-dependent probabilistic performance measures (occupancy uncertainty bands, spillback probabilities) are presented and discussed. |
first_indexed | 2024-09-23T15:53:46Z |
format | Article |
id | mit-1721.1/110622 |
institution | Massachusetts Institute of Technology |
language | en_US |
last_indexed | 2024-09-23T15:53:46Z |
publishDate | 2017 |
publisher | Elsevier Science |
record_format | dspace |
spelling | mit-1721.1/1106222022-09-29T16:54:01Z Stochastic network link transmission model Flotterod, Gunnar Osorio Pizano, Carolina Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Flotterod, Gunnar Osorio Pizano, Carolina This article considers the stochastic modeling of vehicular network flows, including the analytical approximation of joint queue-length distributions. The article presents two main methodological contributions. First, it proposes a tractable network model for finite space capacity Markovian queueing networks. This methodology decomposes a general topology queueing network into a set of overlapping subnetworks and approximates the transient joint queue-length distribution of each subnetwork. The subnetwork overlap allows to approximate stochastic dependencies across multiple subnetworks with a complexity that is linear in the number of subnetworks. Additionally, the network model maintains mutually consistent overlapping subnetwork distributions. Second, a stochastic network link transmission model (SLTM) is formulated that builds on the proposed queueing network decomposition and on the stochastic single-link model of Osorio and Flötteröd (2015). The SLTM represents each direction of a road and each road intersection as one queueing subnetwork. Three experiments are presented. First, the analytical approximations of the queueing-theoretical model are validated against simulation-based estimates. An experiment with intricate traffic dynamics and multi-modal joint distributions is studied. The analytical model captures most dependency structure and approximates well the simulated network dynamics and joint distributions. Even for the considered simple network, which consists of only eight links, the proposed subnetwork decomposition yields significant gains in computational efficiency: It uses less than 0.0025% of the memory that is required by the use of a full network model. Second and third, the proposed SLTM is illustrated with a linear test network adopted from the literature and a more general topology network containing a diverge node and a merge node. Time-dependent probabilistic performance measures (occupancy uncertainty bands, spillback probabilities) are presented and discussed. National Science Foundation (U.S.). (Grant No. 1351512) 2017-07-11T14:19:34Z 2017-07-11T14:19:34Z 2017-05 2017-04 Article http://purl.org/eprint/type/JournalArticle 01912615 http://hdl.handle.net/1721.1/110622 Flötteröd, G., and C. Osorio. “Stochastic Network Link Transmission Model.” Transportation Research Part B: Methodological 102 (August 2017): 180–209. https://orcid.org/0000-0003-0979-6052 en_US http://dx.doi.org/10.1016/j.trb.2017.04.009 Transportation Research Part B: Methodological Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier Science Elsevier |
spellingShingle | Flotterod, Gunnar Osorio Pizano, Carolina Stochastic network link transmission model |
title | Stochastic network link transmission model |
title_full | Stochastic network link transmission model |
title_fullStr | Stochastic network link transmission model |
title_full_unstemmed | Stochastic network link transmission model |
title_short | Stochastic network link transmission model |
title_sort | stochastic network link transmission model |
url | http://hdl.handle.net/1721.1/110622 https://orcid.org/0000-0003-0979-6052 |
work_keys_str_mv | AT flotterodgunnar stochasticnetworklinktransmissionmodel AT osoriopizanocarolina stochasticnetworklinktransmissionmodel |