Merging behaviour: Empirical comparison between two sites and new theory development

Marczak, Florian; Daamen, Winnie; Buisson, Christine

doi:10.1016/j.trc.2013.07.007

Cited by 71 publications

(53 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…332 and 609 merging samples were collected from SH and LA, respectively. Similar to Sun et al [27] and Marczak et al [7], variables considered in the analysis are depicted in Figure 2 (detailed trajectory is show in Figure 3) and summarized in Table 1. For clarity, the meanings of variables are described in Figure 2 taking Vehicle 1618 in LA as an example.…”

Section: Study Sites and Datamentioning

confidence: 98%

“…Daamen et al [12] note that drivers prefer to choose an optimum gap that might reject several acceptable gaps before merging. Marczak et al [7] first define the rejected gap as the gap a merger could have chosen (but chooses instead to drive ahead and merge into a gap downstream) and analyze the impact of one rejected gap. Based on the evidence of these studies, therefore, we conclude that previous studies either lack a clear description of rejected gaps or simply consider one rejected gap.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Sun and Elefteriadou [26] modeled this behavior considering driver characteristics. Recently, Marczak et al [7] take the once rejected gap before merging into consideration to model the merging behavior.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this study, we used the concept of rejected gap as Marczak et al [7] mentioned that the rejected gap is the gap a merger could have chosen. However, the selection and calculation of rejected samples and their gaps are not clearly explained in all the previous studies.…”

Section: Defining and Measuring The Rejected Gapsmentioning

confidence: 99%

“…Lots of these models are based on the gap acceptance theory; that is, when meeting a gap, a driver will compare it to the critical gap. If the gap is larger than the critical gap, the driver will accept it and merge; if not, he/she will reject it and move on to find another gap [7,8]. Intuitively, there are two types of gap: the accepted gap and the rejected gap.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Sun

Zuo

Jiang

et al. 2018

Journal of Advanced Transportation

View full text Add to dashboard Cite

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.

show abstract

Section: Study Sites and Datamentioning

confidence: 98%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Defining and Measuring The Rejected Gapsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Sun

Zuo

Jiang

et al. 2018

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

Empirical Analysis of Lane Changing Behavior at a Freeway Weaving Section

Marczak¹,

Daamen²,

Buisson³

2016

Traffic Management

View full text Add to dashboard Cite

Measuring and Describing Cooperation Between Road Users—Results from CoMove

Quante,

Stoll,

Baumann

et al. 2024

Cooperatively Interacting Vehicles

View full text Add to dashboard Cite

Safe and efficient traffic requires that road users interact and cooperate with each other. Especially in situations which are not explicitly regulated, and the right of way is not clearly defined, it is of great importance that road users are able to communicate their own intentions and understand the communication and cooperation behaviour of the other involved road users. When automated vehicles enter the current traffic system, their ability to fit into the system, that is their ability to communicate and cooperate, will determine their success. Therefore, the development of cooperatively interacting, automated vehicles requires detailed knowledge about human cooperation behaviour in traffic, which can only be obtained using appropriate methods and measures. By focusing on road narrowings and lane changing, this chapter gives an overview on how to measure cooperation between road users, considering methods for data collection, subjective and objective measures of cooperation as well as behaviour modeling, to support the systematic research on cooperation in road traffic. This overview is extended by findings from studies conducted within CoInCar, including results on factors influencing human behaviour in cooperative situations, either in a manual or an automated setting, and initial findings from modeling the cognitive processes underlying cooperative driving behaviour.

show abstract

Merging behaviour: Empirical comparison between two sites and new theory development

Cited by 71 publications

References 16 publications

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

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

Empirical Analysis of Lane Changing Behavior at a Freeway Weaving Section

Measuring and Describing Cooperation Between Road Users—Results from CoMove

Contact Info

Product

Resources

About