A link-based elastic demand equilibrium model with capacity constraints and queueing delays

Smith, Mike

doi:10.1016/j.trc.2012.04.011

Cited by 23 publications

(3 citation statements)

References 32 publications

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“…Although BPR functions are widely used as travel cost functions, they have several disadvantages, as Horowitz () pointed out, including the use in urban areas (e.g., traffic controlled intersections) and the inability to represent dynamic traffic phenomena like queues, spillbacks, wave propagation, capacity drops, and so forth. To improve this, more sophisticated traffic assignment models (e.g., dynamic traffic assignment) could be used (e.g., Nagurney and Dong, ; Smith, ; Bliemer et al., ; Bliemer and Raadsen, ). However, in this case the assumption of a bilevel optimization problem has to be relaxed.…”

Section: Discussionmentioning

confidence: 99%

Determination of Near‐Optimal Restoration Programs for Transportation Networks Following Natural Hazard Events Using Simulated Annealing

Hackl

Adey

Lethanh³

2018

Computer aided Civil Eng

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Disruptive events, such as earthquakes, floods, and landslides, may disrupt the service provided by transportation networks on a vast scale, as their occurrence is likely to cause multiple objects to fail simultaneously. The restoration program following a disruptive event should restore service as much, and as fast, as possible. The estimation of risk due to natural hazards must take into consideration the resilience of the network, which requires estimating the restoration program as accurately as possible. In this article, a restoration model using simulated annealing is formulated to determine near‐optimal restoration programs following the occurrence of hazard events. The objective function of the model is to minimize the costs, taking into consideration the direct costs of executing the physical interventions, and the indirect costs that are being incurred due to the inadequate service being provided by the network. The constraints of the model are annual and total budget constraints, annual and total resource constraints, and the specification of the number and type of interventions to be executed within a given time period. The restoration model is demonstrated by using it to determine the near‐optimal restoration program for an example road network in Switzerland following the occurrence of an extreme flood event. The strengths and weaknesses of the restoration model are discussed, and an outlook for future work is given.

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Section: Discussionmentioning

confidence: 99%

Determination of Near‐Optimal Restoration Programs for Transportation Networks Following Natural Hazard Events Using Simulated Annealing

Hackl

Adey

Lethanh³

2018

Computer aided Civil Eng

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“…the flow that could potentially flow out if there were no constraints). Similar models that explicitly describe residual queues have been proposed by Lam and Zhang (2000) and Smith (2013) in which deterministic route choice (FP) is assumed. None of these models consider specific turn flow restrictions (N).…”

Section: Capacity Constrained Modelsmentioning

confidence: 99%

Genetics of traffic assignment models for strategic transport planning

et al. 2016

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This paper presents a review and classification of traffic assignment models for strategic transport planning purposes by using concepts analogous to genetics in biology. Traffic assignment models share the same theoretical framework (DNA), but differ in functionality (genes). We argue that all traffic assignment models can be described by two genes. The first gene determines the spatial functionality (unrestricted, capacity restrained, capacity constrained, capacity and storage constrained) described by five spatial interaction assumptions, while the second gene determines the temporal functionality (static, semi-dynamic, dynamic) described by two temporal interaction assumptions. This classification provides a deeper understanding of the often implicit assumptions made in traffic assignment models described in the literature, particularly with respect to networking loading where the largest differences occur. It further allows for comparing different models in terms of functionality, and opens the way for developing novel traffic assignment models.

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“…Many have followed, e.g. Smith (1984a,b;1987), Larsson and Patriksson (1995), Lam and Zhang (2000), Marcotte et al (2003), Nie et al (2004), Gentile et al (2005) and Bundschuh et al (2006), andSmith (2013). Regarding queue storage capacities and queue spillback, examples of capacity-constrained equilibrium models with queueing that explicitly consider the impact of blocking back are very much less numerous.…”

Section: Introductionmentioning

confidence: 99%

Quasi-dynamic traffic assignment with spatial queueing, control and blocking back

Smith

Wei

Viti

et al. 2019

Transportation Research Part B: Methodological

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A link-based elastic demand equilibrium model with capacity constraints and queueing delays

Cited by 23 publications

References 32 publications

Determination of Near‐Optimal Restoration Programs for Transportation Networks Following Natural Hazard Events Using Simulated Annealing

Determination of Near‐Optimal Restoration Programs for Transportation Networks Following Natural Hazard Events Using Simulated Annealing

Genetics of traffic assignment models for strategic transport planning

Quasi-dynamic traffic assignment with spatial queueing, control and blocking back

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