Experimental study and modeling of car-following behavior under high speed situation

Huang, Yong-Xian; Jiang, Rui; Zhang, HM; Hu, Mao-Bin; Tian, Junfang; Jia, Bin; Gao, Ziyou

doi:10.1016/j.trc.2018.10.022

Cited by 54 publications

(18 citation statements)

References 38 publications

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“…Recent car-following field experiments (12)(13)(14)(15) show that traffic states span a 2D region in the speed-spacing plane. It was found that traffic instability is caused by the competition between speed adaptation and the cumulative effect of stochastic factors.…”

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confidence: 99%

Impact of Stochasticity on Traffic Flow Dynamics in Macroscopic Continuum Models

Zheng

Jiang

Jia

et al. 2020

Transportation Research Record

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Stochasticity is an indispensable factor for describing real traffic situations. Recent experimental study has shown that a model spanning a two-dimensional speed–spacing (or speed–density) relationship has the potential to reproduce the characteristics of traffic flow in both experiments and empirical observations. This paper studies the impact of stochasticity on traffic flow in macroscopic models utilizing the stochastic flow–density relationship. Numerical analysis is conducted under the periodic boundary to study the impact of stochasticity on stability. Traffic flow upstream of a bottleneck is also investigated to study the impact of stochasticity on the oscillation growth feature. It is shown that there is only a quantitative difference for model stability after introducing stochasticity. In contrast, a qualitative change of the traffic oscillation growth feature can be clearly observed. With the introduction of stochasticity, traffic oscillations begin to grow in a concave way along the road. Sensitivity analysis is also performed. It is found that, under the stochastic flow–density relationship: (i) with the decrease of relaxation time, the second-order model becomes stable; (ii) the smaller the propagation speed of small disturbance, the much stronger the traffic oscillation; (iii) the larger the fluctuation range, the sooner the traffic oscillation fully develops; and (iv) the changing probability has trivial impact on the simulation results. Finally, model calibration and validation are conducted. It is shown that the experimental spatiotemporal patterns can be captured by macroscopic models under the stochastic flow–density relationship, especially the second-order model.

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confidence: 99%

Impact of Stochasticity on Traffic Flow Dynamics in Macroscopic Continuum Models

Zheng

Jiang

Jia

et al. 2020

Transportation Research Record

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“…Furthermore, in microsimulation studies regarding the impact assessment of connected and automated vehicles, the models used for simulation do not account for the reproduction of realistic vehicle dynamics [22,23,29]. In the literature, it has been mentioned that the absence of models capable to reproduce realistic vehicle power dynamics and driving behaviors in microsimulation, can lead to questionable results regarding both emissions-related studies and the study of traffic flow phenomena [2,13,15,21].…”

Section: Introductionmentioning

confidence: 99%

The impact of driving homogeneity due to automation and cooperation of vehicles on uphill freeway sections

Makridis

Leclercq

Mattas

et al. 2020

Eur. Transp. Res. Rev.

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Background: This work presents a microsimulation study on the topic on an uphill network, regarding the potential impact of AVs and Cooperative-AVs (Coop-AVs or CAVs), vehicles able to cooperate with the infrastructure. The novelty of the proposed approach is that the simulation of all vehicles is performed with a common hybrid car-following model that takes explicitly into account the variability in the vehicle dynamics and the driving behaviors. Methods: Simulation of longitudinal movement of the individual vehicles is performed with a common hybrid carfollowing model that takes explicitly into account the variability in the vehicle dynamics and the driving behaviors. Different homogeneity levels in the vehicles and drivers are tested, while the cooperation is explicitly assessed by proposing a realistic Vehicle to Infrastructure (V2I) logic. Possible reduction in the response times of the vehicles is also studied. Results: Results with more homogenous vehicle movements have more consistent performance in terms of traffic flow, that is independent of the order that the vehicles enter the network. Finally, the cooperation with the infrastructure can limit high variations in the vehicles' accelerations and thus potential traffic jams. Conclusions: Homogenized flows can mitigate or even solve traffic-related problems related to the variability in driving behaviors, such as bottlenecks and stop-and-go waves.

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“…In the past traffic flow studies, Ranjitkar et al [26] and Punzo and Simonelli [27] calibrated car-following models and compared the performances of different car-following models, via the trajectory of each tested vehicle which equipped with RTK GPS receivers. To investigate the car-following behavior under high speed driving conditions, Jiang et al [28] carried out car-platoon experiments measured by high-precision DGPS (Differential GPS) devices, and found that there is considerable heterogeneity for the same driver over different runs of the experiment. However, the disadvantage of GPS receivers is that they only provide vehicle-specific data.…”

Section: A Related Workmentioning

confidence: 99%

A Car-Following Data Collecting Method Based on Binocular Stereo Vision

et al. 2020

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This study proposes the design, implementation, and application of a new car-following data collecting method based on binocular stereo vision system. The car-following data consist of car-following distance, velocity, relative velocity and driving trajectory of a following vehicle. Specifically, triangulation principle is used to measure car-following distance. Robust locally weighted regression smoothing method is applied to automatically filter out the outliers caused by disturbance in real-world driving condition. Real-world experiments indicate that binocular cameras with 25mm focal length have advantages in traffic conditions with long car-following distance and high driving speed, and that binocular cameras with12mm focal length are suitable to measure on urban roads, where car-following distance is short and more driving conditions are needed to be observed. In 0-60m distance measuring range, it is verified that the mean absolute percentage error (MAPE) of binocular cameras with 25mm focal length is 3.2% and the MAPE of binocular cameras with 12mm focal length is 6.3%. The data collecting system helps to advance car following models due to its high accuracy and effective cost. INDEX TERMS Binocular stereo vision, car-following, data collecting method, robust locally weighted regression.

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Experimental study and modeling of car-following behavior under high speed situation

Cited by 54 publications

References 38 publications

Impact of Stochasticity on Traffic Flow Dynamics in Macroscopic Continuum Models

Impact of Stochasticity on Traffic Flow Dynamics in Macroscopic Continuum Models

The impact of driving homogeneity due to automation and cooperation of vehicles on uphill freeway sections

A Car-Following Data Collecting Method Based on Binocular Stereo Vision

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