Lane-changing in traffic streams

Laval, Jorge A.; Daganzo, Carlos F.

doi:10.1016/j.trb.2005.04.003

Cited by 570 publications

(300 citation statements)

References 24 publications

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“…The main physical explanations for such a phenomenon are lower speeds for merging vehicles combined with bounded acceleration, e.g. (Cassidy and Rudjanakanoknad, 2005;Laval et al, 2005;Treiber et al, 2006;Laval and Daganzo, 2006), and the impacts of driver behaviors, e.g. (Cassidy and Ahn, 2005;Coifman and Kim, 2011;Chen et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Capacity drops at merges: New analytical investigations

Leclercq

Knoop

Marczak

et al. 2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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This paper focuses on the derivation of analytical formulae to estimate the effective capacity at freeway merges. It extends previous works by proposing a generic framework able to account for (i) heterogeneous vehicle characteristics and (ii) refined description of the physical interactions between upstream waves and downstream voids created by inserting vehicles within the merge area. The provided analytical formulae permit to directly compute the capacity values when the merge is self-active, i.e. when both upstream roads are congested while downstream traffic conditions are free-flow. They show that accounting for vehicle heterogeneity is not necessary when only the mean capacity is targeted. Calculations with the proper mean value for all parameters provide accurate estimates. This result is appealing because the shape of the parameter distributions does not need to be calibrated. However, this paper also shows that vehicle heterogeneity plays a major role in the flow dynamics just upstream of the merge.

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Section: Introductionmentioning

confidence: 99%

“…(Laval and Daganzo, 2006;Srivastava and Geroliminis, 2013). This requires running a simulation for every new set of parameters and is not really convenient when looking for a first and quick approximation of how a merge behaves or to determine which parameters are the most influential, e.g.…”

Section: Introductionmentioning

confidence: 99%

Capacity drops at merges: New analytical investigations

Leclercq

Knoop

Marczak

et al. 2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

show abstract

“…Yousif & Hunt (1995), Sparmann (1979), Brackstone et al (1998), Hidas (2005) describe lane change rates as a function of flow, and these observations can be used to calibrate a lane-change model. Further efforts use other approaches, such as kinematic wave theory (Laval et al 2006) and a gas-kinetic (Boltzmann) model (Helbing & Greiner (1997)). Hidas (2002 and2005) outlines some recent work in the development of microscopic models for lane changing.…”

Section: Department Of Civil and Structural Engineering Dublin Institumentioning

confidence: 99%

Lane changing control to reduce traffic load effect on long-span bridges

Caprani¹,

Enright²,

Carey³

2012

Bridge Maintenance, Safety, Management, Resilience and Sustainability

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“…This model can replicate traffic phenomenon with higher accuracy. There are also other types of second-order traffic prediction models and combinations, such as variation kinematic waves [14], second-order traffic flow model with Kalman filter [15], and CTM-based second-order traffic flow model with particle filtering [16].…”

Section: Introductionmentioning

confidence: 99%

Improving Traffic State Prediction Model for Variable Speed Limit Control by Introducing Stochastic Supply and Demand

Bie

Seraj

Zhang

et al. 2018

Journal of Advanced Transportation

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Variable speed limit (VSL) is becoming recognized as an effective way to improve traffic throughput and road safety. In particular, methods based on traffic state prediction exhibit promising potential to prevent future traffic congestion and collisions. However, field observations indicate that the traffic state prediction model results in nonnegligible error that impacts the next step decision making of VSL. Thus, this paper investigates how to eliminate this prediction error within a VSL environment. In this study, the traffic state prediction model is a second-order traffic flow model named METANET, while the VSL control is model predictive control (MPC) based, and the VSL decision is discrete optimized choice. A simplified version of the switching mode stochastic cell transmission model (SCTM) is integrated with the METANET model to eliminate the prediction error. The performance of the proposed method is assessed using field data from a VSL pilot test in Edmonton, Canada, and is compared with the prediction results of the baseline METANET model during the road test. The results show that during the most congested period the proposed SCTM-METANET model significantly improves the prediction accuracy of regular METANET model.

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Lane-changing in traffic streams

Cited by 570 publications

References 24 publications

Capacity drops at merges: New analytical investigations

Capacity drops at merges: New analytical investigations

Lane changing control to reduce traffic load effect on long-span bridges

Improving Traffic State Prediction Model for Variable Speed Limit Control by Introducing Stochastic Supply and Demand

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