On the role of speed adaptation and spacing indifference in traffic instability: Evidence from car-following experiments and its stochastic model

Tian, Junfang; Zhang, H.M.; Treiber, Martin; Jiang, Rui; Gao, Ziyou; Jia, Bin

doi:10.1016/j.trb.2019.09.014

Cited by 55 publications

(19 citation statements)

References 30 publications

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“…In actual traffic flow, driving behavior is closely related to time difference, random behavior, etc. [30]. Therefore, a reasonable modeling model should be able to consider the relationship between distance and speed, and have resistance to the impact of random interference fluctuations on traffic flow.…”

Section: Flock's Model For Platoonmentioning

confidence: 99%

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Spacing Allocation Method for Vehicular Platoon: A Cooperative Game Theory Approach

et al. 2020

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Recently, spacing policies of the vehicular platoon have been widely developed to enhance safety, traffic efficiency, and fuel consumption. However, the integrated spacing policies aim to maximum overall benefit, and the distributed spacing policies intense to get optimal monomer benefit. Ignoring the fairness of the benefit allocation of each vehicle will reduce the motivation to constitute the platoon. To fill this critical gap, this study proposes a spacing allocation method by treating spacing decisions as cooperative games. A flock’s model which is used to be the payoff function is introduced based on bionic motion principles. We present a characteristic function of the platoon for the cooperative game model considering the specific structure of the platoon. The τ value, Shapley value, and average lexicographic value are introduced and applied to allocate the spacing fairly. Proposed methods are compared with constant distance policy in some typical situations. The simulation results demonstrate that the spacing policy based on cooperative game theory improved the stable time for consistency control and the convergence of longitudinal following error.

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Section: Flock's Model For Platoonmentioning

confidence: 99%

“…τ value is defined within the quasi-balanced game, which can be obtain from the upper vector M and lower vector m [30].…”

Section: τ Value and Shapley Value For The Cooperative Gamementioning

confidence: 99%

Spacing Allocation Method for Vehicular Platoon: A Cooperative Game Theory Approach

et al. 2020

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“…Variable ξ i ∈ R n−1 represents the remaining dynamics of the vehicle, such as actuators dynamics. Since the reaction delay has only slight quantitative influence on the oscillation growth pattern (see [18]), no delays are considered here either for the reaction time or the communication time.…”

Section: A Platoon Modelingmentioning

confidence: 99%

“…where γ ∆p , γ ∆v > 0 are constant parameters, and µ ∆p,i , µ ∆v,i , σ 2 ∆p,i and σ 2 ∆v,i are defined in (17) and (18). The sign function is defined as…”

Section: B Macroscopic Informationmentioning

confidence: 99%

On the utilization of Macroscopic Information for String Stability of a Vehicular Platoon

Mirabilio¹,

Iovine²,

Santis³

et al. 2020

Preprint

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The use of macroscopic information to improve control performance of a vehicular platoon composed of autonomous vehicles is investigated. A mesoscopic control law is provided, and a String Stability analysis is performed using Lyapunov functions and Input-to-State Stability (ISS) concepts. Simulations are implemented in order to validate the controller and to show the efficacy of the proposed approach for mitigating traffic oscillations.

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“…Fu et al [13] proposed a human-like car-following model for autonomous vehicles and connected vehicles. In addition, Tian et al [14] proposed a new car-following model based on the revised Intelligent Driver Model [15] and explored the effect of speed adaptation and spacing indifference on traffic instability. In summary, the car-following model can describe complex microscopic traffic flow phenomena such as traffic congestion, stop-and-go waves, and traffic phase transitions.…”

mentioning

confidence: 99%

An Extended Car-Following Model Considering the Drivers’ Characteristics under a V2V Communication Environment

et al. 2020

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In intelligent transportation systems, vehicles can obtain more information, and the interactivity between vehicles can be improved. Therefore, it is necessary to study car-following behavior during the introduction of intelligent traffic information technology. To study the impacts of drivers' characteristics on the dynamic characteristics of car-following behavior in a vehicle-to-vehicle (V2V) communication environment, we first analyzed the relationship between drivers' characteristics and the following car's optimal velocity using vehicle trajectory data via the grey relational analysis method and then presented a new optimal velocity function (OVF). The boundary conditions of the new OVF were analyzed theoretically, and the results showed that the new OVF can better describe drivers' characteristics than the traditional OVF. Subsequently, we proposed an extended car-following model by combining V2V communication based on the new OVF and previous car-following models. Finally, numerical simulations were carried out to explore the effect of drivers' characteristics on car-following behavior and fuel economy of vehicles, and the results indicated that the proposed model can improve vehicles' mobility, safety, fuel consumption, and emissions in different traffic scenarios. In conclusion, the performance of traffic flow was improved by taking drivers' characteristics into account under the V2V communication situation for car-following theory.Sustainability 2020, 12, 1552 2 of 18 by Nagel and Schreckenberg [7]. Since then, different scholars have used the cellular automata model to explore traffic phase transitions, spatiotemporal patterns of traffic flow, and mixed traffic flow (i.e., mixed bicycle traffic flow) [8][9][10]. Additionally, because the car-following model can describe complex traffic phenomena from the micro perspective, it has attracted considerable attention from scholars. Jiang et al. [11] studied the relationship between the vehicle speed and space headway under various traffic conditions using car-following experimental data and proposed a car-following model based on the following mechanism. Zhao et al. [12] explored the car-following behavior of automated vehicles on the basis of an accelerated evaluation method, and the simulation results showed that the proposed method can reduce the evaluation time of crashes, injuries, or conflict events. Fu et al. [13] proposed a human-like car-following model for autonomous vehicles and connected vehicles. In addition, Tian et al. [14] proposed a new car-following model based on the revised Intelligent Driver Model [15] and explored the effect of speed adaptation and spacing indifference on traffic instability. In summary, the car-following model can describe complex microscopic traffic flow phenomena such as traffic congestion, stop-and-go waves, and traffic phase transitions. Therefore, systematic research on modeling car-following behavior has been rapidly developed.However, there are few studies on the impact of drivers' characteristics on the car-f...

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On the role of speed adaptation and spacing indifference in traffic instability: Evidence from car-following experiments and its stochastic model

Cited by 55 publications

References 30 publications

Spacing Allocation Method for Vehicular Platoon: A Cooperative Game Theory Approach

Spacing Allocation Method for Vehicular Platoon: A Cooperative Game Theory Approach

On the utilization of Macroscopic Information for String Stability of a Vehicular Platoon

An Extended Car-Following Model Considering the Drivers’ Characteristics under a V2V Communication Environment

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