Effect of front two adjacent vehicles’ velocity information on car-following model construction and stability analysis

Wang, Xiaoning; Liu, Minzhuang; Ci, Yusheng; Wu, Ling

doi:10.1016/j.physa.2022.128196

Cited by 8 publications

(3 citation statements)

References 51 publications

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“…Due to development of intelligent transportation technology and vehicle to vehicle (V2V) environment technology, the vehicles are equipped with intelligent devices while moving [35,36] and drivers can obtain information regarding surrounding vehicles more accurately and widely (see figure 1). This has lead to the development of various car-following models based on information interaction between vehicles to obtain information regarding multi-vehicle average velocity, average anticipative velocity, average headway, etc [37,38]. Sometimes the driver of target vehicle may change his or her mind suddenly due to unpredictable situation is created by immediate preceding driver leading to difficulty for following vehicle in handling this critical moment within short notice.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the above mentioned studies assume that the effects of surrounding vehicles on the target vehicle are the same, and that the introduced information is the average value of the anticipative velocity, velocity, and headway of the vehicle group. Although some studies pertaining to the extended car-following model in V2V environments mention that the surrounding vehicles impose different effects on the objective vehicle [37]. This motivated us to build another microscopic model based on the assumption that the effect of motion state of different preceding vehicles is different on the motion state of objective vehicle.…”

Section: Introductionmentioning

confidence: 99%

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A new car following model based on weighted average velocity field

Singh,

Kumar

2024

Phys. Scr.

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In real traffic, the motion status of forward moving vehicles play animportant role in influencing the following vehicle’s motion behaviour. Nowadays,autonomous and electric vehicles have been incorporated with car-following modewhen the control process is more complicated and more meaningful. As aresult, researchers are studying car-following control strategies for vehicles toattain stability in traffic. Based on this, we propose a new car following modelconsidering weighted average velocity field to describe the average effect of multivehicleinteraction on the whole road. The weighted average velocity field canbe regarded as the weighted average of velocities of finite number of vehiclespreceding the objective vehicle with more weightage to more preceding vehicle.The stability condition is derived through stability analysis. Subsequently, themodified Korteweg-de Vries (mKdV) equation is formulated to unveil trafficcongestion in the form of the kink-antikink density wave. Theoretical findingsindicate that the weighted average velocity field yields larger stability regionscompared to the average velocity field. Numerical simulations were conducted,and the results obtained demonstrate that the weighted average effect of multivehicleinteractions play a crucial role in suppressing traffic jams faster than asimple average.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new car following model based on weighted average velocity field

Singh,

Kumar

2024

Phys. Scr.

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“…By simulating different positions of the CACC platoon in traffic flow, the influence of the CACC platoon on traffic flow is analysed, and the results show that when the CACC platoon drives at the front of the flow, the oscillation will be reduced (Sharma et al 2021). In order to improve the vehicle stability, the speed adjustment strategy that is formulated by analysing and predicting the preceding vehicle (Tian et al 2021;Gregurić et al 2022) and an extended car-following model of CVs by considering the driving state and expected speed of 2 preceding vehicles (Wang et al 2022c), are used to reduce the CVs' reaction time and braking frequency, which are verified by simulation. A dynamic route choice model for CVs can also be established to simulate the running conditions of CVs at various headway gaps, so as to analyse the stability of the platoons (Lee et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Stability Analysis for Heterogeneous Traffic Flow With Lane-Change Disturbance

Li,

Pu,

Chen

et al. 2024

Transport

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Stability analysis and benefit estimation have substantial implications for lane-change decision-making to reduce delay and variation. Connected platoons drive with minor headway to increase capacity, whereas dividing or reforming platoons significantly impacts traveling efficiency. Therefore, this article focuses on the instability of the platoon caused by an en-route lane-change. Construction of platoon forming, combination rules, and car-following models for various vehicle types are presented to describe driving behaviours. Then, a velocity adjustment and a model for lane-change preparation and recovery are proposed. In addition, a group of stability recognition indexes and related stability evaluation factors are presented. Experiments involving numerical comparisons of the proposed factors are conducted to demonstrate the propagation properties of the instability and reveal the fluctuation degree. The variation duration, velocity variation range, and total delay are the primary indicators for evaluating lane-change feasibility. The models and findings can be applied effectively in practice to determine the optimal time and location for en-route lane-change and to assist with traffic management and lane selection at the entrance.

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