The effects of lane-changing on the immediate follower: Anticipation, relaxation, and change in driver characteristics

Zheng, Zuduo; Ahn, Soyoung; Chen, Danjue; Laval, Jorge A.

doi:10.1016/j.trc.2012.10.007

Cited by 171 publications

(95 citation statements)

References 23 publications

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“…At present, the research result for lane changing [1] on curve section of road is very little. In reference [2], Hatipoglu et al researched the control method for lane changing based on the yaw rate tracking, by using the curvature of the circular road, designed the mathematical model of desired yaw angle and yaw rate for lane changing, but it ignored the curvature difference between the inside lane and the outside lane of the road, so the vehicle position can't be guaranteed at the centerline of the target lane at the end of the lane changing process, namely lane changing trajectory exists a deviation.…”

Section: Introductionmentioning

confidence: 93%

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Modeling for Lane Changing Trajectory with Odd Order Polynomial

Ren¹,

Fangde²,

Yang³

2018

dtetr

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Abstract. In this paper, we study the virtual trajectory planning method for lane changing of vehicle in automated highway system, derivate the trajectory model for lane changing on circular road by using odd-order polynomial constraints. Based on the trajectory model for lane changing on straight road, the motion for lane changing of vehicle on circular road can be decomposed into the linear centripetal motion and the circular motion around the instantaneous centre of the circle road. The simulation results verify the feasibility of trajectory planning method for lane changing proposed in this paper on circular road.

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Section: Introductionmentioning

confidence: 93%

“…In the following, we should extend the trajectory model for lane changing on straight road to Take the start point S as origin point to establish coordinate system, X-axis is along the tangential direction of arc 1 l at point S, and Y-axis towards the instantaneous centre of road, as shown in Figure 2 …”

Section: Constraint Conditionsmentioning

confidence: 99%

Modeling for Lane Changing Trajectory with Odd Order Polynomial

Ren¹,

Fangde²,

Yang³

2018

dtetr

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“…This is not only because merging behavior might lead to traffic breakdown, but also because a last minute merging decision will affect subsequent driving behavior and the behaviors of the following drivers [21]. Hidas [22,23] developed a merging model including cooperative and forced merge movement components.…”

Section: Literature Reviewmentioning

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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“…In their study, they have considered varying wave speed within driver due to stochasticity, and presented methods to identify wave paths by matching points with similar speed. Zheng et al (2011Zheng et al ( , 2013 further examined the changes in the speed-spacing parameters under major disturbances such as lane changes and traffic flow oscillations.…”

Section: Intra-driver Heterogeneitymentioning

confidence: 99%