Multilane first-order traffic flow model with endogenous representation of lane-flow equilibrium

Shiomi, Yasuhiro; Taniguchi, Takao; Uno, Nobuhiro; Shimamoto, Hiroshi; Nakamura, Toshiyuki

doi:10.1016/j.trc.2015.07.002

Cited by 22 publications

(17 citation statements)

References 19 publications

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“…Furthermore, developments have been made in describing traffic situations with multiple lanes, e.g. the '2-pipe regime' with slugs and rabbits (Daganzo 2002), modified speed-density relation based on lanechanging (Jin 2010) and utility-driven lane changes (Shiomi et al 2015). The disordered traffic situation, in which lane discipline is lacking, has been captured in continuum models using e.g.…”

Section: Background On Macroscopic Modellingmentioning

confidence: 99%

A macroscopic flow model for mixed bicycle–car traffic

Wierbos

Knoop

Hänseler

et al. 2020

Transportmetrica A: Transport Science

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Bicycles are gaining popularity as a mode of transport resulting in a mixed bicycle-car traffic situation on urban roads. Cyclists, however, are hardly included in traffic flow models which complicates the design of safe and congestion-free traffic situations. This work introduces class-specific speed functions based on two variables, being space headway for both cars and cyclists. This enables the macroscopic modelling of mixed bicycle-car traffic. The multi-class macroscopic flow model is successfully tested for different traffic situations that occur on urban roads where cyclists and cars share the same infrastructure, e.g. cyclists overtaking a queue of cars and cars overtaking cyclists with reduced speed. The mixed bicycle-car flow model allows travel time estimation of both classes, which in turn can be used to evaluate the overall performance of a mixed traffic road.

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Section: Background On Macroscopic Modellingmentioning

confidence: 99%

A macroscopic flow model for mixed bicycle–car traffic

Wierbos

Knoop

Hänseler

et al. 2020

Transportmetrica A: Transport Science

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show abstract

“…A major drawback of these models is that they consider the traffic flow variables to be aggregated across lanes which is not the case. Shiomi et al (2015) and Roncoli, Papageorgiou, and Papamichail (2015) presented first order multi-lane models considering the lane changing rates to be dependent upon speed difference and density difference of neighbouring lanes respectively. These models treated the lanes as independent entities and did not aggregate the variables.…”

Section: State-of-the-art On Multi-lane Trafficmentioning

confidence: 99%

“…Laval and Daganzo (2006) used speed difference as motivations for lane change but it was based on the assumption of similar operating speeds across lanes which is not the case. Shiomi et al (2015) also considered speed difference as motivation but took into account the different speeds across lanes (different FD for every lane). A single FD for all lanes implies that lane changes always happen among the lanes which is not the case (because of different operating speeds on each lane).…”

Section: State-of-the-art On Multi-lane Trafficmentioning

confidence: 99%

“…There may be cases where the model predicts a lane change towards a lane considering lower density but a lane change may not be realistic. This problem has been highlighted in Shiomi et al (2015) where drivers change lane from the middle lane to the median lane which is dropping. These kind of problems can be avoided by taking into account the downstream conditions.…”

Section: Computation Of Lateral Flowsmentioning

confidence: 99%

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First order multi-lane traffic flow model – an incentive based macroscopic model to represent lane change dynamics

Subraveti

Knoop

Arem

2019

Transportmetrica B: Transport Dynamics

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“…For the FIFO case, if complete congestion on one outgoing link completely impedes the outgoing flow from link 1, the node model at the diverging junction is said to be full FIFO, otherwise it is a partial FIFO model. FIFO models, [10] and [11] have suggested modeling lanes of an input link as separate links. The main drawback of this approach is that it greatly complicates the size and dimensionality of the model since every node in the network becomes a multi-input-multi-output junction.…”

Section: Introductionmentioning

confidence: 99%

Mixed monotonicity of partial first-in-first-out traffic flow models

Coogan

Arcak

Kurzhanskiy

2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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Abstract-In vehicle traffic networks, congestion on one outgoing link of a diverging junction often impedes flow to other outgoing links, a phenomenon known as the first-in-first-out (FIFO) property. Simplified traffic models that do not account for the FIFO property result in monotone dynamics for which powerful analysis techniques exist. FIFO models are in general not monotone, but have been shown to be mixed monotone-a generalization of monotonicity that enables similarly powerful analysis techniques. In this paper, we study traffic flow models for which the FIFO property is only partial, that is, flows at diverging junctions exhibit a combination of FIFO and non-FIFO phenomena. We show that mixed monotonicity extends to this wider class of models and establish conditions that guarantee convergence to an equilibrium.

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Multilane first-order traffic flow model with endogenous representation of lane-flow equilibrium

Cited by 22 publications

References 19 publications

A macroscopic flow model for mixed bicycle–car traffic

A macroscopic flow model for mixed bicycle–car traffic

First order multi-lane traffic flow model – an incentive based macroscopic model to represent lane change dynamics

Mixed monotonicity of partial first-in-first-out traffic flow models

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