A Modified Car-following Model Considering Traffic Density and Acceleration of Leading Vehicle

Cao, Xudong; Wang, Jianjun; Chen, Chenchen

doi:10.3390/app10041268

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…However, the individual motion state of the vehicles close to the object vehicle was not considered comprehensively. According to this, Cao et al further extended Kuang's model [71] by introducing the acceleration of the preceding vehicle [75].…”

Section: Information Of Vehicles Platoonmentioning

confidence: 99%

The Car-Following Model and Its Applications in the V2X Environment: A Historical Review

et al. 2021

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The application of vehicle-to-everything (V2X) technology has resulted in the traffic environment being different from how it was in the past. In the V2X environment, the information perception ability of the driver–vehicle unit is greatly enhanced. With V2X technology, the driver–vehicle unit can obtain a massive amount of traffic information and is able to form a connection and interaction relationship between multiple vehicles and themselves. In the traditional car-following models, only the dual-vehicle interaction relationship between the object vehicle and its preceding vehicle was considered, making these models unable to be employed to describe the car-following behavior in the V2X environment. As one of the core components of traffic flow theory, research on car-following behavior needs to be further developed. First, the development process of the traditional car-following models is briefly reviewed. Second, previous research on the impacts of V2X technology, car-following models in the V2X environment, and the applications of these models, such as the calibration of the model parameters, the analysis of traffic flow characteristics, and the methods that are used to estimate a vehicle’s energy consumption and emissions, are comprehensively reviewed. Finally, the achievements and shortcomings of these studies along with trends that require further exploration are discussed. The results that were determined here can provide a reference for the further development of traffic flow theory, personalized advanced driving assistance systems, and anthropopathic autonomous-driving vehicles.

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Section: Information Of Vehicles Platoonmentioning

confidence: 99%

The Car-Following Model and Its Applications in the V2X Environment: A Historical Review

et al. 2021

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Car-following model of connected and autonomous vehicles considering both average headway and electronic throttle angle

Chen

Zhang

et al. 2021

Mod. Phys. Lett. B

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The connected and automated vehicle (CAV) is regarded as an effective way to improve traffic efficiency and safety, which can utilize vehicle-to-vehicle (V2V) communication technology to obtain real-time status information from multiple preceding vehicles. In view of the car-following characteristic of CAV in a V2V communications environment, an extended car-following model AHT-FVD is proposed which takes both average headway and electronic throttle angle difference into account. The stability of this model is examined via linear stability analysis. It is found that the proposed model has a larger stability region than both the full velocity difference (FVD) model and throttle-based FVD (T-FVD) model. Namely, this AHT-FVD model can effectively stabilize traffic flow and alleviate traffic congestion in theory. Moreover, a series of numerical simulations are carried out to explore how average headway together with electronic throttle angle difference influences the stability of traffic flow. Simulation results show that increasing either the average headway weight or the electronic throttle angle difference control signal coefficients can yield higher traffic flow stability. Simulation result is highly consistent with theoretical analysis.

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An extended car-following model accounting for average optimal velocity difference and backward-looking effect based on the Internet of Vehicles environment

Xiao

Liang

et al. 2022

Mod. Phys. Lett. B

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Communication technology has achieved unprecedented development in recent years, and its applications in transportation systems and automobiles are also increasing. During driving, the driver obtains not only traffic information through observation but also more traffic information beyond the visual range through the Internet of Vehicles (IoV). Therefore, to better describe the evolution law of traffic flow in the IoV environment and to provide some theoretical and strategic support for the coming of the autonomous driving era in the future, an extended car-following model accounting for average optimal velocity difference and backward-looking effect based on IoV environment is proposed. The linear analysis and nonlinear analysis of the model are calculated separately, and the neutral stability curve and the coexistence curve together prove the validity of the model. Subsequently, the numerical simulation verified the accuracy of the theoretical analysis and also proved that the extended model can enhance the stability of traffic flow.

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A Modified Car-following Model Considering Traffic Density and Acceleration of Leading Vehicle

Cited by 3 publications

References 38 publications

The Car-Following Model and Its Applications in the V2X Environment: A Historical Review

The Car-Following Model and Its Applications in the V2X Environment: A Historical Review

Car-following model of connected and autonomous vehicles considering both average headway and electronic throttle angle

An extended car-following model accounting for average optimal velocity difference and backward-looking effect based on the Internet of Vehicles environment

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