Dynamic network loading: a stochastic differentiable model that derives link state distributions
Name
Osorio-2011-Dynamic network load.pdf
Size
331.44 KB
Format
Adobe PDF
Checksum (MD5)
2569ed3cec36f5c97b7e9d37a1141508
Author(s)
Bierlaire, Michel
Osorio Pizano, Carolina
Flotterod, Gunnar
Date Issued
May 2011
Journal
Procedia - Social and Behavioral Sciences
Publisher
Elsevier
Citation
Osorio, Carolina, Gunnar Flotterod, and Michel Bierlaire. “Dynamic Network Loading: a Stochastic Differentiable Model That Derives Link State Distributions.” Procedia - Social and Behavioral Sciences 17 (2011): 364–381.
Version
Final published version
Abstract
We present a dynamic network loading model that yields queue length distributions, accounts for spillbacks, and maintains a differentiable mapping from the dynamic demand on the dynamic queue lengths. The model also captures the spatial correlation of all queues adjacent to a node, and derives their joint distribution. The approach builds upon an existing stationary queueing network model that is based on finite capacity queueing theory. The original model is specified in terms of a set of differentiable equations, which in the new model are carried over to a set of equally smooth difference equations. The physical correctness of the new model is experimentally confirmed in several congestion regimes. A comparison with results predicted by the kinematic wave model (KWM) shows that the new model correctly represents the dynamic build-up, spillback, and dissipation of queues. It goes beyond the KWM in that it captures queue lengths and spillbacks probabilistically, which allows for a richer analysis than the deterministic predictions of the KWM. The new model also generates a plausible fundamental diagram, which demonstrates that it captures well the stationary flow/density relationships in both congested and uncongested conditions.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.sbspro.2011.04.522
