Effect of looking backward on traffic flow in a cooperative driving car following model

Ge, Hongxia; Zhu, Huibing; Dai, Shun‐Dong

doi:10.1140/epjb/e2007-00014-x

Cited by 89 publications

(38 citation statements)

References 10 publications

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“…As R V ≤ 0, γ 2 contributes positively to the left hand side of equation (28) which consequently destabilizes traffic flow, which is in contradiction to the findings in literature (Nakayama, Sugiyama, and Hasebe 2001;Ge, Zhu, and Dai 2006b;Sun, Liao, and Peng 2011;Yang et al 2013), where it was found that backward looking strategy improves the linear stability condition. These results will be illustrated numerically in Section 5 for different sets of model parameters.…”

Section: Linear Stability Conditionscontrasting

confidence: 91%

“…In literature, the backward looking effect has been considered using the OVM or FVDM (Nakayama, Sugiyama, and Hasebe 2001;Ge, Zhu, and Dai 2006b;Sun, Liao, and Peng 2011;Yang et al 2013). Based on the bidirectional looking framework of Jin et al (2014) which considered the acceleration to consist of two tasks: backward looking and forward looking strategy, Zheng, Jin, and Huang (2015) proposed a bidirectional microscopic model using the Helly car-following model (Helly 1959):…”

Section: Multi-anticipative Bidirectional Microscopic Modelsmentioning

confidence: 99%

“…Such multi anticipative driving strategy generally leads to a better traffic operation such as enhanced capacity and more stable traffic flow with respect to perturbations caused by, for example, sudden deceleration or lane-changes of vehicles (Treiber, Kesting, and Helbing 2006;Kesting, Treiber, and Helbing 2010;Sau et al 2014;Monteil et al 2014;Ngoduy 2013b;Ngoduy and Wilson 2014;Ngoduy 2015). A few other research has been conducted to investigate how the information from the following vehicles affects the dynamics of traffic flow (Nakayama, Sugiyama, and Hasebe 2001;Hasebe, Nakayama, and Sugiyama 2003;Ge, Zhu, and Dai 2006b;Sun, Liao, and Peng 2011;Yang et al 2013;Zheng, Zhong, and Ma 2013;Jin et al 2014). The effect of the information from the following vehicles on traffic flow dynamics has also been introduced via the honk effect (Zheng, Ma, and Zhong 2011).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi anticipative bidirectional macroscopic traffic model considering cooperative driving strategy

Ngoduy

Jia

2016

Transportmetrica B: Transport Dynamics

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Recent development of information and communication technologies (ICT) has enabled vehicles to timely communicate with others through wireless technologies, which will form future (intelligent) traffic systems (ITS) consisting of so-called connected vehicles. Cooperative driving with the connected vehicles is regarded as a promising driving pattern to significantly improve transportation efficiency and traffic safety. Such cooperative driving has motivated many studies to model the dynamics of traffic flow under multi anticipative driving strategy (i.e. drivers react to many leading vehicles) or bidirectional strategy (i.e. drivers react to both (single) leading and following vehicles). In the vast literature of traffic flow theory, there are continuum models considering multiple forward anticipative strategy, where the driver reacts to many leaders. To the best of our knowledge, few study effort has been undertaken to include bidirectional driving strategy, where the driver reacts to both direct leader and direct follower, in the continuum traffic flow models. Moreover, the current bidirectional continuum traffic flow model still suffers some drawbacks: considers the behaviour of only a single leading vehicle in the forward looking strategy and neglects the impact of the forward space headway (e.g. the distance between two consecutive vehicles) sensitivity parameter on the (linear) stability of the model. This paper aims to derive a continuum traffic model considering both multiple forward and backward driving strategy. It is shown that the derived model is a generalized version of a current continuum model for ITS and can improve important properties of such bidirectional (continuum) model.

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Section: Linear Stability Conditionscontrasting

confidence: 91%

Section: Multi-anticipative Bidirectional Microscopic Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi anticipative bidirectional macroscopic traffic model considering cooperative driving strategy

Ngoduy

Jia

2016

Transportmetrica B: Transport Dynamics

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“…To determine the selected value of the amplitude A for the soliton solution (17), it is necessary to consider the solvability condition [14] :…”

Section: Thus (15) Turns Intomentioning

confidence: 99%

Application of singular perturbation method in analyzing traffic density waves

Shen

2009

J. Shanghai Univ.(Engl. Ed.)

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Car following model is one of microscopic models for describing traffic flow. Through linear stability analysis, the neutral stability lines and the critical points are obtained for the different types of car following models and two modified models. The singular perturbation method has been used to derive various nonlinear wave equations, such as the Kortewegde-Vries (KdV) equation and the modified Korteweg-de-Vries (mKdV) equation, which could describe different density waves occurring in traffic flows under certain conditions. These density waves are mainly employed to depict the formation of traffic jams in the congested traffic flow. The general soliton solutions are given for the different types of car following models, and the results have been used to the modified models efficiently.

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“…Among them, the most well-known one is the optimal velocity (OV) model [1], which has successfully revealed the dynamical evolution process of traffic congestion in a simple way. Thereafter, inspired by the OV model, some new car-following models [2][3][4][5][6][7][8][9][10][11] were successively put forward to describe the nature of traffic more realistically. Some were extended by introducing multiple information of headway [2][3][4] or relative velocity [5][6][7], whereas others considered both factors at the same time [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A new car-following model considering driver’s individual anticipation behavior

2015

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In this paper, a new car-following model of traffic flow is proposed by considering the driver's individual anticipation behavior (forecast behavior and response delay behavior). The effect of anticipation parameters corresponding to the individual differences upon the stability of traffic flow is examined through linear stability analysis. The simulation results are in good agreement with the theoretical analysis and confirm that traffic flow can be stabilized efficiently by drivers' forecast effect (positive anticipation parameter) and, nevertheless, be destabilized by drivers' physical delay effect (negative anticipation parameter). Thus, the two aspects of drivers' individual anticipation behavior play an important role in stabilizing or destabilizing the traffic system, respectively.

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Effect of looking backward on traffic flow in a cooperative driving car following model

Cited by 89 publications

References 10 publications

Multi anticipative bidirectional macroscopic traffic model considering cooperative driving strategy

Multi anticipative bidirectional macroscopic traffic model considering cooperative driving strategy

Application of singular perturbation method in analyzing traffic density waves

A new car-following model considering driver’s individual anticipation behavior

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