Research on the Impacts of Generalized Preceding Vehicle Information on Traffic Flow in V2X Environment

Wang, Xiaoyuan; Han, Junyan; Chen, Bai; Shi, Huili; Zhang, Jinglei; Wang, Gang

doi:10.3390/fi13040088

Cited by 10 publications

(7 citation statements)

References 74 publications

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“…Yu constructed a continuous medium macro model based on the car-following model with consideration of the drivers' reaction delay, using the headway and relative velocity information [140]. Different from the above works, Wang et al proposed a simulation framework to combine the carfollowing model into the cellular automata model, and analyzed the traffic flow affected by information about the average velocity of the GPV based on the framework [141].…”

Section: Analyzing the Characteristics Of Traffic Flowmentioning

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: Analyzing the Characteristics Of Traffic Flowmentioning

confidence: 99%

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

et al. 2021

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“…For ensemble learning, the stronger the independence of the basic learners it contains, the better the performance of the assembled learner. It is almost impossible to construct completely independent basic learners, and the random extraction principle of the Bagging theory guarantees the relative independence of the basic learners to the greatest extent, referring to the Equation ( 1) and (2).…”

Section: Modelmentioning

confidence: 99%

A Data-Driven Car-Following Model Based on the Random Forest

Shi¹,

Wang²,

Zhong³

et al. 2021

WJET

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The car-following models are the research basis of traffic flow theory and microscopic traffic simulation. Among the previous work, the theory-driven models are dominant, while the data-driven ones are relatively rare. In recent years, the related technologies of Intelligent Transportation System (ITS) represented by the Vehicles to Everything (V2X) technology have been developing rapidly. Utilizing the related technologies of ITS, the large-scale vehicle microscopic trajectory data with high quality can be acquired, which provides the research foundation for modeling the car-following behavior based on the data-driven methods. According to this point, a data-driven car-following model based on the Random Forest (RF) method was constructed in this work, and the Next Generation Simulation (NGSIM) dataset was used to calibrate and train the constructed model. The Artificial Neural Network (ANN) model, GM model, and Full Velocity Difference (FVD) model are employed to comparatively verify the proposed model. The research results suggest that the model proposed in this work can accurately describe the carfollowing behavior with better performance under multiple performance indicators.

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“…In recent years, the emergence of the Internet of Vehicles (IoVs) enables huge potential in the area of intelligent transportation [23][24][25][26][27][28][29][30][31]. It mainly includes vehicle-to-vehicle (V2V), vehicle-to-road (V2R), vehicle-to-infrastructure (V2I) and so on.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the work in [23] builds a smart traffic management platform to collect available real-time traffic data based on IoVs, and successfully demonstrates this system on real roads. The research in [27] builds a car-following model to propose a car-following model using vehicles to everything (V2X) technique. In [28], a way of transmitting warning messages by V2V and V2I are designed to avoid road accidents.…”

Section: Introductionmentioning

confidence: 99%

Predictive Intelligent Transportation: Alleviating Traffic Congestion in the Internet of Vehicles

Zhang

Khalgui

2021

Sensors

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Due to the limitations of data transfer technologies, existing studies on urban traffic control mainly focused on isolated dimension control such as traffic signal control or vehicle route guidance to alleviate traffic congestion. However, in real traffic, the distribution of traffic flow is the result of multiple dimensions whose future state is influenced by each dimension’s decisions. Presently, the development of the Internet of Vehicles enables an integrated intelligent transportation system. This paper proposes an integrated intelligent transportation model that can optimize predictive traffic signal control and predictive vehicle route guidance simultaneously to alleviate traffic congestion based on their feedback regulation relationship. The challenges of this model lie in that the formulation of the nonlinear feedback relationship between various dimensions is hard to describe and the design of a corresponding solving algorithm that can obtain Pareto optimality for multi-dimension control is complex. In the integrated model, we introduce two medium variables—predictive traffic flow and the predictive waiting time—to two-way link the traffic signal control and vehicle route guidance. Inspired by game theory, an asymmetric information exchange framework-based updating distributed algorithm is designed to solve the integrated model. Finally, an experimental study in two typical traffic scenarios shows that more than 73.33% of the considered cases adopting the integrated model achieve Pareto optimality.

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Research on the Impacts of Generalized Preceding Vehicle Information on Traffic Flow in V2X Environment

Cited by 10 publications

References 74 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

A Data-Driven Car-Following Model Based on the Random Forest

Predictive Intelligent Transportation: Alleviating Traffic Congestion in the Internet of Vehicles

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