Asymptotic solutions for a multi-anticipative car-following model

Wagner, Christoph

doi:10.1016/s0378-4371(98)00306-9

Cited by 28 publications

(9 citation statements)

References 12 publications

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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%

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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Section: Optimal Velocity Modelsmentioning

confidence: 99%

Statistical physics of vehicular traffic and some related systems

2000

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“…In general, the multianticipative driving behavior results in stabilizing traffic flow because the driver reacts well in advance to the changing downstream traffic situations so that small disturbances are dissolved when propagating upstream. The stabilizing effects of the multianticipative driving behavior on traffic flow have been studied both analytically and numerically using either extended car-following or extended optimal speed models in Ge et al (5), Hoogendoorn et al, (6), and Wagner (7).…”

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confidence: 99%

Macroscopic Effects of Multianticipative Driving Behavior on Traffic Flow Characteristics

Ngoduy

2009

Transportation Research Record: Journal of the Transportation R

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Intelligent transportation systems have been considered an important solution in enhancing freeway capacity and suppressing traffic congestion. One recent automotive technology has enabled a vehicle to communicate with several vehicles ahead via a wireless system. Such a communication system constitutes so-called multianticipative driving behavior. In general, the effect of multianticipative interactions on traffic flow dynamics has been studied widely in the literature on the basis of a microscopic modeling approach. This paper presents an alternative approach to gain insights into the effect of multianticipative driving behavior on traffic flow characteristics from a macroscopic model. The model is obtained from a gas-kinetic theory in which the multianticipative interactions are taken into consideration. It is found that the analytical and numerical results of the model are consistent with those obtained from microscopic models. It is argued that the results provide a better understanding of the effect of multianticipative interactions on traffic flow characteristics.

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“…Car-following theory is one of the most important parts of modern traffic theory, which describes the dynamic behaviors of one vehicle following the other, and usually used in the study of microscopic traffic flow. e car-following theory has developed rapidly in recent decades, and an increasing number of models have been put forward, which include the early nonlinear models proposed by Pipes [1], Newell [2], the recent classic model presented by Bando et al [3], Helbing and Tilch [4] and Jiang et al [5], and some others in the literature [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. Bando et al [3] proposed a remarkable car-following model called the optimal velocity model (OVM) because it solves the problem of describing the motion of successive vehicles.…”

Section: Introductionmentioning

confidence: 99%

“…Based on OVM, Wagner [6] put forward a new model called the looking forward model because it contains the location information of multiple vehicles ahead. Hasebe et al [7] further expanded the OVM by introducing the headway information of K vehicles ahead and proved that the new model can improve the stability of traffic flow more than the OVM from the perspective of the linear response.…”

Section: Introductionmentioning

confidence: 99%

Modeling for Micro Traffic Flow with the Consideration of Lateral Vehicle’s Influence

Liu

Shi

2020

Journal of Advanced Transportation

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In order to make the car-following model describe the driving behavior of vehicle on urban road more accurately, existing car-following models are simulated using measured traffic data. According to the analysis of the simulation result, two new improved car-following models based on the optimal velocity model (OVM) are proposed in this paper. The lateral vehicle’s influence is introduced as the influence factor of driving behavior. By using of linear stability analysis, stability conditions of improved car-following models are obtained. Nonlinear analysis is carried out to investigate the traffic performances near the critical point. The result of numerical simulation indicates that stability of traffic flow is under the influence from lateral vehicle; the lesser the influence, the greater the stability. New cooperative car-following models are verified by the traffic flow data collected in Xi’an city. It is shown that compared with the optimal velocity model, the simulation result of the second cooperative model, respectively, gets 62.89% unbiased variance reduction, 66.39% maximum absolute error reduction, and 33.4% minimum absolute error reduction. Therefore, the second cooperative model is more suitable to describe the vehicle’s actual behavior in car-following state.

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Asymptotic solutions for a multi-anticipative car-following model

Cited by 28 publications

References 12 publications

Statistical physics of vehicular traffic and some related systems

Statistical physics of vehicular traffic and some related systems

Macroscopic Effects of Multianticipative Driving Behavior on Traffic Flow Characteristics

Modeling for Micro Traffic Flow with the Consideration of Lateral Vehicle’s Influence

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