Stochastic network link transmission model
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Osorio_Stochastic network.pdf
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1.81 MB
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Adobe PDF
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4dd240bf9e8df2c7269df7ae43552dc0
Author(s)
Flotterod, Gunnar
Osorio Pizano, Carolina
Date Issued
May 2017
Journal
Transportation Research Part B: Methodological
Publisher
Elsevier Science
Citation
Flötteröd, G., and C. Osorio. “Stochastic Network Link Transmission Model.” Transportation Research Part B: Methodological 102 (August 2017): 180–209.
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.trb.2017.04.009
