Capacity drops at merges: New analytical investigations

Leclercq, Ludovic; Knoop, Victor L.; Marczak, Florian; Hoogendoorn, Serge P.

doi:10.1109/itsc.2014.6957839

Cited by 4 publications

(10 citation statements)

References 25 publications

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“…Chen et al (2014) 2010, Leclercq et al 2011Leclercq et al , 2015 Inter-driver/vehicle spread indicates the spread of drivers and vehicles. Papageorgiou et al (2008) state that the capacity drop is due to the acceleration difference between two successive vehicles.…”

Section: Overview Of Assumptions On Mechanisms Of the Capacity Dropmentioning

confidence: 99%

A Microscopic Investigation Into the Capacity Drop: Impacts of Longitudinal Behavior on the Queue Discharge Rate

Yuan¹,

Knoop²,

Hoogendoorn³

2017

Transportation Science

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The capacity drop indicates that the queue discharge rate is lower than the free-flow capacity. Studies show that the queue discharge rate varies under different traffic conditions. Empirical data show that the queue discharge rate increases as the speed in congestion increases. Insights into the underlying behavioral mechanisms that cause these variable queue discharge rates can help minimize traffic delays and eliminate congestion. However, to the best of the authors knowledge, few efforts have been devoted to testing impacts of traffic behaviors on the queue discharge rate. This paper tries to fill this gap. We investigate to what extent the acceleration spread and reaction time can influence the queue discharge rate. It is found that the (inter-driver) acceleration spread does not reduce the queue discharge rates as much as found empirically.Modelling reaction time might be more important than modeling acceleration for capacity drop in carfollowing models. A speed-dependent reaction time mechanism for giving variable queue discharge rates is proposed. That is, decreasing reaction time as the speed in congestion increases can give the same queue discharge rate as found empirically. This research suggests that motivating drivers to speed up earlier could increase the queue discharge rate and thereby minimize delays.

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Section: Overview Of Assumptions On Mechanisms Of the Capacity Dropmentioning

confidence: 99%

A Microscopic Investigation Into the Capacity Drop: Impacts of Longitudinal Behavior on the Queue Discharge Rate

Yuan¹,

Knoop²,

Hoogendoorn³

2017

Transportation Science

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“…Each merge has only one targeted lane for the insertions. The analytical expressions for the effective capacity, C 1 and C 2 for the related inserting flows q 0 and q 12 , respectively, can then be derived directly from the analytical formulas provided elsewhere (24,29). It is beyond the scope of this paper to explain how these formulas were derived.…”

Section: Analytical Expression Of Effective Capacity For a Given Inserting Flowmentioning

confidence: 99%

“…The authors recall only the main hypothesis and provide the formulation when vehicle characteristics are homogeneous. The readers are referred in particular to the work of Leclercq et al for details and extension to the heterogeneous case (29).…”

Section: Analytical Expression Of Effective Capacity For a Given Inserting Flowmentioning

confidence: 99%

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Capacity Drops at Merges: Analytical Expressions for Multilane Freeways

Leclercq

Marczak

Knoop

et al. 2016

Transportation Research Record: Journal of the Transportation R

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This paper deals with the derivation of analytical formulae to estimate the effective capacity at freeway merges in a multilane context. It extends two previous papers that are based on the same modeling framework but that are restricted to a single lane on the freeway (or to the analysis of the right lane only). The analytical expression for the one-lane capacity is recursively applied for all lanes. Lane-changing maneuvers (mandatory for the on-ramp vehicles and discretionary for others) are partitioned into two non-overlapping local merging areas. Discretionary lane-changes are transformed into a lane-changing flow using an appropriate analytical formula. This defines a system of equations whose unknowns are the capacity on all lanes and the inserting flow coming from the on-ramp. A sensitivity analysis shows that vehicle acceleration and the truck ratio are the most influential parameters for the total capacity. The analytical formulae are proven to match with numerical results from a traffic simulator that fully describes vehicle dynamics. Finally, they provide very good estimates when compared to experimental data for an active merge on the M6 freeway in UK.

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“…Numerous studies have shown that merging from the acceleration lane has a significant impact on traffic operations at freeway on-ramp bottlenecks [1,2] and can also trigger traffic breakdown [3][4][5]. Merging behavior is a complex task, because, typically, the driver has to focus on three or more vehicles in their current and target lanes in a limited timeframe [5].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Predicting Stochastic Merging Behaviors at Freeway On-Ramp Bottlenecks

Sun

Zuo

Jiang

et al. 2018

Journal of Advanced Transportation

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Merging behavior is inevitable at on-ramp bottlenecks and is a significant factor in triggering traffic breakdown. In modeling merging behaviors, the gap acceptance theory is generally used. Gap acceptance theory holds that when a gap is larger than the critical gap, the vehicle will merge into the mainline. In this study, however, analyses not only focus on the accepted gaps, but also take the rejected gaps into account, and the impact on merging behavior with multi-rejected (more than once rejecting behavior) gaps was investigated; it shows that the multi-rejected gaps have a great influence on the estimation of critical gap and merging prediction. Two empirical trajectory data sets were collected and analyzed: one at Yan'an Expressway in Shanghai, China, and the other at Highway 101 in Los Angeles, USA. The study made three main contributions. First, it gives the quantitative measurement of the rejected gap which is also a detailed description of non-merging event and investigated the characteristics of the multi-rejected gaps; second, taking the multi-rejected gaps into consideration, it further expanded the concept of the "critical gap" which can be a statistic one and the distribution function of merging probability with respect to such gaps was analyzed by means of survival analysis. This way could make the full use of multi-rejected gaps and accepted gaps and reduce the sample bias, thus estimating the critical gap accurately; finally, considering multi-rejected gaps, it created logistic regression models to predict merging behavior. These models were tested using field data, and satisfactory performances were obtained.

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Capacity drops at merges: New analytical investigations

Cited by 4 publications

References 25 publications

A Microscopic Investigation Into the Capacity Drop: Impacts of Longitudinal Behavior on the Queue Discharge Rate

A Microscopic Investigation Into the Capacity Drop: Impacts of Longitudinal Behavior on the Queue Discharge Rate

Capacity Drops at Merges: Analytical Expressions for Multilane Freeways

Modeling and Predicting Stochastic Merging Behaviors at Freeway On-Ramp Bottlenecks

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