A New Extended Multiple Car-Following Model Considering the Backward-Looking Effect on Traffic Flow

Yang, Shuhong; Liu, Lu; Sun, Dihua; Li, Chungui

doi:10.1115/1.4007310

Cited by 11 publications

(12 citation statements)

References 19 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: 55%

“…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: 55%

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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“…Several other studies have not only reported enhanced traffic flow efficiency because of cooperation and exchange of information between vehicles (e.g. Monteil, Nantes, Billot, Sau, & El Faouzi, 2014;Wang, Daamen, Hoogendoorn, & van Arem, 2016b;Xie, Zhang, Gartner, & Arsava, 2016;Yang, Liu, Sun, & Li, 2013;Zhou, Qu, & Jin, 2016) but also between vehicles and infrastructure (e.g. variable speed limits, see Grumert, Ma, & Tapani, 2015;Wang, Daamen, Hoogendoorn, & van Arem, 2016a).…”

Section: Literature Resultsmentioning

confidence: 99%

Policy and society related implications of automated driving: A review of literature and directions for future research

Milakis

Arem

Wee

2017

Journal of Intelligent Transportation Systems

726

383

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KEYWORDSautomated driving; first, second, and third order impacts; policy and societal implications; ripple effect ABSTRACTIn this paper, the potential effects of automated driving that are relevant to policy and society are explored, findings discussed in literature about those effects are reviewed and areas for future research are identified. The structure of our review is based on the ripple effect concept, which represents the implications of automated vehicles at three different stages: first-order (traffic, travel cost, and travel choices), second-order (vehicle ownership and sharing, location choices and land use, and transport infrastructure), and third-order (energy consumption, air pollution, safety, social equity, economy, and public health). Our review shows that first-order impacts on road capacity, fuel efficiency, emissions, and accidents risk are expected to be beneficial. The magnitude of these benefits will likely increase with the level of automation and cooperation and with the penetration rate of these systems. The synergistic effects between vehicle automation, sharing, and electrification can multiply these benefits. However, studies confirm that automated vehicles can induce additional travel demand because of more and longer vehicle trips. Potential land use changes have not been included in these estimations about excessive travel demand. Other third-order benefits on safety, economy, public health and social equity still remain unclear. Therefore, the balance between the short-term benefits and long-term impacts of vehicle automation remains an open question.

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“…In multianticipative models [8][9][10], the relative current is the current difference of the two adjacent sites ahead. This is similar to the idea in the multianticipative car-following models [11][12][13][14].…”

Section: Introductionmentioning

confidence: 54%

Nonlinear Analysis of a New Extended Lattice Model With Consideration of Multi-Anticipation and Driver Reaction Delays

Chen

Shi

et al. 2014

Journal of Computational and Nonlinear Dynamics

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A new extended lattice model of traffic flow is presented by taking into account both multianticipative behavior and the reaction-time delay of drivers. The linear stability theory and the nonlinear analysis method are applied to the model. The linear stability condition is obtained. The Korteweg-de Vries (KdV) equation near the neutral stability line and the modified Korteweg-de Vries (mKdV) equation near the critical point are derived. The numerical results show that the stability of traffic flow will be enhanced by multianticipative consideration and will be weakened with the increase of the reaction-time delay. The unfavorable effect induced by driver reaction delays can be partly compensated by considering multianticipative behavior.

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A New Extended Multiple Car-Following Model Considering the Backward-Looking Effect on Traffic Flow

Cited by 11 publications

References 19 publications

Multi anticipative bidirectional macroscopic traffic model considering cooperative driving strategy

Multi anticipative bidirectional macroscopic traffic model considering cooperative driving strategy

Policy and society related implications of automated driving: A review of literature and directions for future research

Nonlinear Analysis of a New Extended Lattice Model With Consideration of Multi-Anticipation and Driver Reaction Delays

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