Nonlinear density wave and energy consumption investigation of traffic flow on a curved road

Jin, Zhizhan; Cheng, Rongjun; Ge, Hongxia

doi:10.1088/1674-1056/26/11/110504

Cited by 21 publications

(10 citation statements)

References 47 publications

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“…In [23], while the extended curve FVD model was proposed, the analysis of nonlinear characteristics of traffic flow near the critical point was absent. To address this, Jin et al [128] derived the time-dependent Ginzburg-Landau (TDGL) equation, mKdV equation, and the correlation expression of these two equations. After this, the feedback control item based on the velocity difference between the subject vehicle and its preceding vehicle was designed and introduced into the extended curve OV model by Zheng et al [29], and the impacts of this control item on the stability of car-following were discussed based on control theory.…”

Section: Curvementioning

confidence: 99%

“…The results reveal that the negative feedback control of the preceding vehicle's velocity can enhance the stability of traffic flow. In [23,128], the linear and nonlinear characteristics of traffic flow based on the micro behavior model were discussed. To comprehensively describe the characteristics of traffic flow on a curve, Liu et al [130] derived a macro flow model corresponding to the aforementioned micro behavior model.…”

Section: Curvementioning

confidence: 99%

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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: Curvementioning

confidence: 99%

Section: Curvementioning

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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“…For what concerns the effect of road curvature, Liang and Su [14] introduce the centrifugal force acting on the vehicle into the modified cellular automaton (CA) model to investigate the traffic flow at curved sections. Some studies extend the OVM [15], [16] or FVDM [17], [18] model to describe the impacts of side friction factor and turning radius on acceleration behaviour. Sun et al [19] propose another extended FVDM to explore the impacts of curved roads, considering not only the side friction factor and the turning radius but the driver's desire for smooth driving.…”

Section: Introductionmentioning

confidence: 99%

Introducing the Effects of Road Geometry Into Microscopic Traffic Models for Automated Vehicles

Mattas

Donà³

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Road geometry (e.g. slope and curvature) has significant impacts on driving behaviours of low-level automated vehicles (AVs), but it has been largely ignored in microscopic traffic models. To capture these effects, this study proposes a generic approach to extend any (free-flow or car-following) microscopic models characterized by acceleration functions. To this end, three model extensions are developed, each of which can use different submodels for comparison. Their effectiveness is demonstrated with a microscopic free-flow model that represents the AVs' control logic. Finally, all possible combinations of the microscopic model and the model extensions are calibrated and cross-validated against data sets, which contain empirical trajectories of commercial adaptive cruise control (ACC) systems on different test tracks. Results suggest that two submodels, i.e., the nonlinear vehicle dynamics (NVD) and the radius difference method (RDM), can extend the microscopic model to effectively capture the effects of road slope and road curvature, respectively, on automated driving. Specifically, the NVD is the dominant factor contributing to increasing (by 34.9% on average) model accuracy. In addition, when simulating reckless turning behaviours (i.e. the vehicle turns at high speeds), the inclusion of the developed RDM is significant for model performance, and the models extended with both the NVD and the RDM can achieve the largest accuracy gains (39.6%).

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“…Jiang et al (2001) presented the full velocity difference model (FVDM) by adding the positive velocity difference into the GF model. On the basis of FVDM, a series of improved factors have been considered, such as memory (Cao, 2020;Wu et al, 2019;Sun et al, 2019a;Ma et al, 2019;Wang et al, 2019c;Huang et al, 2018;Liu et al, 2017;Peng et al, 2016), driver's anticipation (Cheng et al, 2017c(Cheng et al, , 2018Sun et al, 2018a;Peng and Cheng, 2013), intelligent transportation system (Sun et al, 2015(Sun et al, , 2018bOu and Tang, 2018;Li et al, 2015a;Ge et al, 2004;Chen et al, 2019), backward looking (Hou et al, 2017), curved road (Zhu and Zhang, 2012;Sun et al, 2018cSun et al, , 2019bCheng and Wang, 2019;Liu et al, 2019;Jin et al, 2017;Zheng et al, 2017;Zhu, 2013) and lateral gap (Li et al, 2015b(Li et al, , 2018. These new models have contributed to the solution of traffic problems and the progress of traffic flow theory.…”

Section: Introductionmentioning

confidence: 99%

“…It is impossible for a vehicle to keep running on the straight road and driving on only one road in the actual traffic. Some models have been established to study the traffic flow characteristics on curved road (Zhu and Zhang, 2012;Sun et al, 2018cSun et al, , 2019bCheng and Wang, 2019;Liu et al, 2019;Jin et al, 2017;Zheng et al, 2017;Zhu, 2013). In 2012, Zhu et al (Zhu and Zhang, 2012) earlier studied the curve factors in the traffic flow theory.…”

Section: Introductionmentioning

confidence: 99%

An extended car-following model considering the effect of two-sided lateral gap with uncertain velocity on curved road

Cheng

2021

Self Cite

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PurposeThe goal of this study is to explore the effect of two-sided lateral gap with uncertain velocity on the stability of traffic flow on a curved road.Design/methodology/approachIn this paper, an extended car-following model considering the effect of two-sided lateral gap with uncertain velocity on a curved road is proposed. The effects of different lateral positions and radius of different sizes can be considered as control signals. The stability condition of the new model is obtained by the control theory. The numerical simulations are carried out to analyze how the control signal and lateral positions and radius of curved road affect traffic flow stability. The results show that driving between two lanes and inaccurate speed estimates both have a negative effect on traffic flow stability, and the stability also decreases with the increase in the radius of curved road.Findings(1) Simulation of influencing factors of vehicle lateral position indicates that if the driver drives between two lanes, it would have a negative impact on traffic flow. (2) When the speed is fixed, the traffic flow becomes more and more unstable with the increase in the radius of the curve. (3) The stability of traffic flow will be affected when the driver estimates the speed of the vehicle ahead. Therefore, whether it is manual driving or future intelligent vehicle driving, it is necessary to accurately judge the speed of the front vehicle.Originality/valueThere is little research on two-sided lateral gap with uncertain velocity for the stability of traffic flow on a curved road. The enhanced model constructed in this study can better reflect the real traffic, which can also give some theoretical reference for the development of connected and autonomous vehicles (CAVs).

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Nonlinear density wave and energy consumption investigation of traffic flow on a curved road

Cited by 21 publications

References 47 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

Introducing the Effects of Road Geometry Into Microscopic Traffic Models for Automated Vehicles

An extended car-following model considering the effect of two-sided lateral gap with uncertain velocity on curved road

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