A New Continuum Model considering Driving Behaviors and Electronic Throttle Effect on a Gradient Highway

Jiao, Yu; Cheng, Rongjun; Ge, Hongxia

doi:10.1155/2020/2172156

Cited by 12 publications

(4 citation statements)

References 56 publications

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“…In [118], the traffic flow characteristics in the non-lane-discipline-environment with a slope were analyzed. Based on the extended FVD model and considering the driver's offensive characteristics [119], the slope FVD model [116], and the equation of vehicle movement and ETOA information [86], Jiao et al [120] proposed a new extended slope FVD model considering a driver's offensive characteristics and ETOA information and derived the corresponding macro flow model.…”

Section: Slopementioning

confidence: 99%

Modeling the Car-Following Behavior with Consideration of Driver, Vehicle, and Environment Factors: A Historical Review

Han

Wang

2022

Sustainability

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Car-following behavior is the result of the interaction of various elements in the specific driver-vehicle-environment aggregation. Under the intelligent and connected condition, the information perception ability of vehicles has been significantly enhanced, and abundant information about the driver-vehicle-environment factors can be obtained and utilized to study car-following behavior. Therefore, it is necessary to comprehensively take into account the driver-vehicle-environment factors when modeling car-following behavior under intelligent and connected conditions. While there are a considerable number of achievements in research on car-following behavior, a car-following model with comprehensive consideration of driver-vehicle-environment factors is still absent. To address this gap, the literature with a focus on car-following behavior research with consideration of the driver, vehicle, or environment were reviewed, the contributions and limitations of the previous studies were analyzed, and the future exploration needs and prospects were discussed in this paper. The results can help understand car-following behavior and the traffic flow characteristics affected by various factors and provide a reference for the development of traffic flow theory towards smart transportation systems and intelligent and connected driving.

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Section: Slopementioning

confidence: 99%

Modeling the Car-Following Behavior with Consideration of Driver, Vehicle, and Environment Factors: A Historical Review

Han

Wang

2022

Sustainability

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“…Kang et al derived a macro model utilizing the micro-macro relation based on Sun's model [137] to explore the traffic flow characteristic affected by information about the average velocity of multiple preceding vehicles in the current lane [70]. Jiao et al derived a macro model for describing characteristics of traffic flow with slope based on the extended car-following model with consideration of the ETOA information [138]. Sun et al derived a macro model to analyze how the traffic flow affected the information of two preceding vehicles based on the extended model incorporating the same information [139].…”

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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“…Meanwhile, it is also very important to analyze the overall evolution of traffic flow from a macro perspective. [13][14][15][16][17][18][19] Qin et al [13] discussed the effect of electronic throttle on the stability of mixed traffic flow. Jiao et al [14] proposed a new macro model, which contains electronic throttle (ET) and ramp factors.…”

Section: Introductionmentioning

confidence: 99%

“…[13][14][15][16][17][18][19] Qin et al [13] discussed the effect of electronic throttle on the stability of mixed traffic flow. Jiao et al [14] proposed a new macro model, which contains electronic throttle (ET) and ramp factors. The results indicated that the ET effect play an important role in alleviating traffic congestion to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

An extended smart driver model considering electronic throttle angle changes with memory

Wu¹,

Ge²,

Cheng³

2022

Chinese Phys. B

Self Cite

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Based on the fact that the electronic throttle angle effect performs well in the traditional car following model, this paper attempts to introduce the electronic throttle angle into the smart driver model (SDM) as an acceleration feedback control term, and establish an extended smart driver model considering electronic throttle angle changes with memory (ETSDM). In order to show the practicability of the extended model, the next generation simulation (NGSIM) data was used to calibrate and evaluate the extended model and the smart driver model. The calibration results show that, compared with SDM, the simulation value based on the ETSDM is better fitted with the measured data, that is, the extended model can describe the actual traffic situation more accurately. Then, the linear stability analysis of ETSDM was carried out theoretically, and the stability condition was derived. In addition, numerical simulations were explored to show the influence of the electronic throttle angle changes with memory and the driver sensitivity on the stability of traffic flow. The numerical results show that the feedback control term of electronic throttle angle changes with memory can enhance the stability of traffic flow, which shows the feasibility and superiority of the proposed model to a certain extent.

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A New Continuum Model considering Driving Behaviors and Electronic Throttle Effect on a Gradient Highway

Cited by 12 publications

References 56 publications

Modeling the Car-Following Behavior with Consideration of Driver, Vehicle, and Environment Factors: A Historical Review

Modeling the Car-Following Behavior with Consideration of Driver, Vehicle, and Environment Factors: A Historical Review

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

An extended smart driver model considering electronic throttle angle changes with memory

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