Multi-anticipative car-following model

Lenz, H.; Wagner, Christoph; Sollacher, Rudolf

doi:10.1007/s100510050618

Cited by 274 publications

(135 citation statements)

References 2 publications

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“…Such 'spatial anticipation' or 'multi-anticipation' has been applied to the OVM [20] and to the Gipps model [21] as well as to some cellular automata model [16,22]. As expected, the resulting models show a higher stability than the original model.…”

Section: Introductionmentioning

confidence: 71%

Delays, inaccuracies and anticipation in microscopic traffic models

Treiber

Kesting

Helbing

2006

Physica A: Statistical Mechanics and its Applications

470

269

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We generalize a wide class of time-continuous microscopic traffic models to include essential aspects of driver behaviour not captured by these models. Specifically, we consider (i) finite reaction times, (ii) estimation errors, (iii) looking several vehicles ahead (spatial anticipation), and (iv) temporal anticipation. The estimation errors are modelled as stochastic Wiener processes and lead to time-correlated fluctuations of the acceleration.We show that the destabilizing effects of reaction times and estimation errors can essentially be compensated for by spatial and temporal anticipation, that is, the combination of stabilizing and destabilizing effects results in the same qualitative macroscopic dynamics as that of the respectively underlying simple car-following model. In many cases, this justifies the use of simplified, physics-oriented models with a few parameters only. Although the qualitative dynamics is unchanged, multianticipation increase both spatial and temporal scales of stop-and-go waves and other complex patterns of congested traffic in agreement with real traffic data. Remarkably, the anticipation allows accident-free smooth driving in complex traffic situations even if reaction times exceed typical time headways.

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Section: Introductionmentioning

confidence: 71%

Delays, inaccuracies and anticipation in microscopic traffic models

Treiber

Kesting

Helbing

2006

Physica A: Statistical Mechanics and its Applications

470

269

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“…An alternative possibility has been explored in recent works based on the carfollowing approach [89][90][91][92][93][94][95][96][97][98][99][100][101][102][103][104]. This formulation is based on the assumption that V desired n depends on the distance-headway of the n-th vehicle, i.e., V desired n (t) = V opt (∆x n (t)) so thatẍ…”

Section: Optimal Velocity Modelsmentioning

confidence: 99%

“…One possible way to generalize the OV models for taking into account such multi-vehicle interactions [102] is to write the dynamical equations as…”

Section: Optimal Velocity Modelsmentioning

confidence: 99%

Statistical physics of vehicular traffic and some related systems

2000

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In the so-called "microscopic" models of vehicular traffic, attention is paid explicitly to each individual vehicle each of which is represented by a "particle"; the nature of the "interactions" among these particles is determined by the way the vehicles influence each others' movement. Therefore, vehicular traffic, modeled as a system of interacting "particles" driven far from equilibrium, offers the possibility to study various fundamental aspects of truly nonequilibrium systems which are of current interest in statistical physics. Analytical as well as numerical techniques of statistical physics are being used to study these models to understand rich variety of physical phenomena exhibited by vehicular traffic. Some of these phenomena, observed in vehicular traffic under different circumstances, include transitions from one dynamical phase to another, criticality and self-organized criticality, metastability and hysteresis, phase-segregation, etc. In this critical review, written from the perspective of statistical physics, we explain the guiding principles behind all the main theoretical approaches. But we present detailed discussions on the results obtained mainly from the so-called "particle-hopping" models, particularly emphasizing those which have been formulated in recent years using the language of cellular automata.

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“…multiple anticipative driving strategy). Such multiple (forward) anticipations are important in both human driven cars (Lenz, Wagner, and Sollacher 1999;Treiber, Kesting, and Helbing 2006;Hoogendoorn, Ossen, and Schreuder 2006) or cooperative cruise control (van Arem, Van Driel, and Visser 2006;Arnaout and Bowling 2011;Ngoduy 2013b). This paper concerns the extension of the model of Zheng, Jin, and Huang (2015) to capture such multiple (forward) anticipations.…”

Section: Multi-anticipative Bidirectional Microscopic Modelsmentioning

confidence: 99%

“…To account for such changes in the driving strategy, a lot of research has been undertaken to understand how including the information of neighbouring vehicles (both followers and leaders) affects the dynamics of traffic flow. For example, many car-following models have been developed to account for the multiple anticipative driving strategy where the considered vehicle can react to the behaviour of many leading vehicles (Lenz, Wagner, and Sollacher 1999;Ge, Dai, and Dong 2006a;Treiber, Kesting, and Helbing 2006;Schreuder 2006, 2007;Kesting and Treiber 2008;Kesting, Treiber, and Helbing 2010). 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).…”

Section: Introductionmentioning

confidence: 99%

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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Multi-anticipative car-following model

Cited by 274 publications

References 2 publications

Delays, inaccuracies and anticipation in microscopic traffic models

Delays, inaccuracies and anticipation in microscopic traffic models

Statistical physics of vehicular traffic and some related systems

Multi anticipative bidirectional macroscopic traffic model considering cooperative driving strategy

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