An analytic finite capacity queueing network model capturing the propagation of congestion and blocking

Osorio, Carolina; Bierlaire, Michel

doi:10.1016/j.ejor.2008.04.035

Cited by 120 publications

(67 citation statements)

References 50 publications

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“…The form is that we use the mathematical model coming from the quantitative analysis to get the key, then based on which we make qualitative inference [7,8]. At the same time, according to the relationship structure set mapping which gets from qualitative inference, we establish mathematical model through all kinds of set mapping space hypothesis.…”

Section: B Simulation Model Of Traffic Congestion Degreementioning

confidence: 99%

Analysis Method of Traffic Congestion Degree Based on Spatio-Temporal Simulation

He¹

2012

IJACSA

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Abstract-The purpose of this research is to design and implement a road traffic congestion and traffic patterns simulation (TPS) model and integrate it with extension-information model (EIM). The problems of road traffic simulation and control are studied according to the method of extension information model, and from the spatio-temporal analysis point of view. The rules of the traffic simulation from existence to evolution are analyzed using theories. Based on this study, the concept of traffic system entropy is introduced, and resulted in the establishment of a fundamental frame work for the road traffic simulation system based on extension spatio-temporal information system. Moreover, a practicable methodology is presented.

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Section: B Simulation Model Of Traffic Congestion Degreementioning

confidence: 99%

Analysis Method of Traffic Congestion Degree Based on Spatio-Temporal Simulation

He¹

2012

IJACSA

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“…This formulation can be extended to allow for arbitrary link topologies as well as more general demand structures (where external arrivals and departures arise at arbitrary links). These extensions can be based, for instance, on the assumptions of the approach in [39].…”

Section: Wherementioning

confidence: 99%

“…A formulation for large-scale networks appears in [22]. Both of these models are derived from the analytical stationary queueing model in [39].…”

Section: Finite Capacity Queueing Modelmentioning

confidence: 99%

Dynamic network loading: A stochastic differentiable model that derives link state distributions

Osorio

Flötteröd

Bierlaire

2011

Transportation Research Part B: Methodological

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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.

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“…They include the Social Force Model (Helbing and Molnar, 1995;Lakoba et al, 2005), the Magnetic Force Model (Okazakia and Matsushitaa, 1993), the Benefit Cost Model (Ressel, 2004;Teknomo, 2002), the Cellular Automata Model (Yang et al, 2003;Weifang et al, 2003;Dijkstra et al, 2001), and the Queuing Network Model (MacGregor Smith, 1998;Osorio and Bierlaire, 1998).…”

Section: Introductionmentioning

confidence: 99%

A Novel Three Dimensional Movement Model for Pedestrian Navigation

2012

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In this paper, a Three Dimensional Pedestrian Movement Model (3D-MM) capable of probabilistically representing pedestrian movement in challenging indoor and outdoor localization environments is developed, implemented and evaluated. In the scope of this paper, the model is used to generate a 'movement' or a transition for dynamic positioning systems that are based on sequential Bayesian filtering techniques, such as particle filtering. It can also be used to assign weights for particles' movements proposed by sensors in Likelihood Particle Filters implementations. Alternatively, the developed model can be applied to other applications domains such as infrastructure design, evacuation planning, robot-human interaction and pervasive computing. The novelty of the model is in its ability to characterize both random and goals-oriented pedestrian motions and additionally use the a priori knowledge of maps and floor plans. It will be shown that an appropriate pedestrian movement model not only improves the positioning accuracy, but is also essential for a robust positioning estimator. Additionally, this work shows that maps and floor plans can improve pedestrian movement models but do not replace them, as several authors suggest. K E Y WO R D S 1. Mobility Models.2. Movement Models. 3. Indoor Positioning. 4. Multisensor Navigation.

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An analytic finite capacity queueing network model capturing the propagation of congestion and blocking

Cited by 120 publications

References 50 publications

Analysis Method of Traffic Congestion Degree Based on Spatio-Temporal Simulation

Analysis Method of Traffic Congestion Degree Based on Spatio-Temporal Simulation

Dynamic network loading: A stochastic differentiable model that derives link state distributions

A Novel Three Dimensional Movement Model for Pedestrian Navigation

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