Analytical and Scalable Analysis of Transient Tandem Markovian Finite Capacity Queueing Networks

Osorio, Carolina; Yamani, Jana H.

doi:10.1287/trsc.2015.0629

Cited by 13 publications

(9 citation statements)

References 41 publications

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“…We are also investigating the use of aggregation-disaggregation techniques to approximate the full joint network distribution in a scalable and tractable way. Aggregation-disaggregation ideas address the curse of dimensionality by providing an aggregate description of network states (Osorio and Yamani, 2017;Osorio and Wang, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…Table 5 displays the values of the main exogenous model parameters. Note that there are two sets of demand scenarios in Section 4 of Osorio and Yamani (2017), high demand and medium demand, we are going to carry out the optimization for both demand scenarios. The optimization problems are solved using the Active-set algorithm of the fmincon routine of MATLAB (2016).…”

Section: Network Configuration and Problem Formulationmentioning

confidence: 99%

“…The model of Osorio et al (2011) includes a stochastic node model for a simple two queue network. Osorio and Yamani (2017) proposes an analytical model to approximate the transient aggregate joint queue-length distribution of tandem links, the aggregation-disaggregation technique and network decomposition methods are used to reduce the model complexity. Osorio and Wang (2017) propose an analytical approximation of the aggregate stationary joint queue-length distributions for arbitrary size and it can be extended to general topology by coupling with the method introduced in Osorio and Bierlaire (2009).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the approximation of queue-length distributions in transportation networks

Lü¹,

Osorio²

2021

Preprint

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This paper focuses on the analytical probabilistic modeling of vehicular traffic. It formulates a stochastic node model. It then formulates a network model by coupling the node model with the link model of Lu and Osorio (2018), which is a stochastic formulation of the traffictheoretic link transmission model. The proposed network model is scalable and computationally efficient, making it suitable for urban network optimization. For a network with r links, each of space capacity , the model has a complexity of O(r ). The network model yields the marginal distribution of link states. The model is validated versus a simulation-based network implementation of the stochastic link transmission model. The validation experiments consider a set of small network with intricate traffic dynamics. For all scenarios, the proposed model accurately captures the traffic dynamics. The network model is used to address a signal control problem. Compared to the probabilistic link model of Lu and Osorio (2018) with an exogenous node model and a benchmark deterministic network loading model, the proposed network model derives signal plans with better performance. The case study highlights the added value of using between-link (i.e., across-node) interaction information for traffic management and accounting for stochasticity in the network.

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Section: Discussionmentioning

confidence: 99%

Section: Network Configuration and Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the approximation of queue-length distributions in transportation networks

Lü¹,

Osorio²

2021

Preprint

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“…Nonetheless, recall that the main role of the physical component is to provide a tractable approximation of the objective function. Given the difficulty of accurately modeling between-link dependencies while preserving tractability (Osorio and Yamani 2014, Osorio and Wang 2017, this independence approximation ensures tractability. By definition:…”

Section: Total Travel Time Standard Deviationmentioning

confidence: 99%

Simulation-Based Travel Time Reliable Signal Control

Xiao

Osorio

Santos

2019

Transportation Science

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Most urban transportation optimization problems are formulated based on first-order moments of network performance (e.g. expected trip travel times, expected throughput). Formulations based on the use of higher-order information can lead, for instance, to enhanced network reliability and enhanced network robustness. Problem formulations that account for higher-order distributional information are rare and are usually based on the use of low-resolution analytical traffic models. This paper proposes the use of high-resolution stochastic traffic simulators to approximate and optimize higher-order information. This paper proposes a simulation-based optimization technique that enables problems formulated with higher-order information to be addressed efficiently. The paper addresses a simulation-based travel time reliable signal control problem. Travel time distributional estimates are obtained from a stochastic microscopic urban traffic simulator and are embedded within a simulation-based optimization algorithm. Analytical approximations of the simulated metrics are formulated and combined with the simulated data in order to enhance the computational efficiency of the algorithm. A large-scale control problem is tackled in a computationally efficient manner, when compared with a traditional simulation-based optimization method. The proposed approach enables the efficient 1 use of higher-order distributional estimates obtained from stochastic traffic simulators to address simulation-based reliable and robust transportation problems.

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“…The model of Osorio et al ( 8 ) includes a stochastic node model for a simple two queue network. Osorio and Yamani ( 32 ) proposed an analytical model to approximate the transient aggregate joint queue-length distribution of tandem links in which the aggregation-disaggregation technique and network decomposition methods are used to reduce the model complexity. Osorio and Wang ( 33 ) proposed an analytical approximation of the aggregate stationary joint queue-length distributions for arbitrary size which can be extended to general topology by coupling with the method introduced in Osorio and Bierlaire ( 34 ).…”

mentioning

confidence: 99%

On the Analytical Probabilistic Modeling of Flow Transmission Across Nodes in Transportation Networks

Lü

Osorio

2022

Transportation Research Record: Journal of the Transportation R

Self Cite

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This paper focuses on the analytical probabilistic modeling of vehicular traffic. It formulates a stochastic node model. It then formulates a network model by coupling the node model with the link model of Lu and Osorio (2018), which is a stochastic formulation of the traffic-theoretic link transmission model. The proposed network model is scalable and computationally efficient, making it suitable for urban network optimization. For a network with [Formula: see text] links, each with a space capacity of one, the model has a complexity of [Formula: see text]. The network model yields the marginal distribution of link states. The model is validated versus a simulation-based network implementation of the stochastic link transmission model. The validation experiments consider a set of small networks with intricate traffic dynamics. For all scenarios, the proposed model accurately captures the traffic dynamics. The network model is used to address a signal control problem. Compared with the probabilistic link model of Lu and Osorio (2018) with an exogenous node model and a benchmark deterministic network loading model, the proposed network model derives signal plans with better performance. The case study highlights the added value of using between-link (i.e., across-node) interaction information for traffic management and accounting for stochasticity in the network.

show abstract

Analytical and Scalable Analysis of Transient Tandem Markovian Finite Capacity Queueing Networks

Cited by 13 publications

References 41 publications

On the approximation of queue-length distributions in transportation networks

On the approximation of queue-length distributions in transportation networks

Simulation-Based Travel Time Reliable Signal Control

On the Analytical Probabilistic Modeling of Flow Transmission Across Nodes in Transportation Networks

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