Variability of observed drivers’ car-following behavior on expressway basic segment

Yang, Yupei; Wada, Koh; Oguchi, Takashi; Iryo-Asano, Miho

doi:10.1016/j.trpro.2017.05.179

Cited by 5 publications

(7 citation statements)

References 8 publications

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“…The present paper-the first step in a larger workconsiders this inter-vehicle variability as independent of traffic conditions. Some papers mention reaction time and minimal distance distributions as inter-vehicle variability (31,32,34,35,49). From empirical trajectories, other studies obtained different vehicle-driver parameters for every specific model (34,50).…”

Section: Car-following (Cf) Approachesmentioning

confidence: 99%

“…Some papers mention reaction time and minimal distance distributions as inter-vehicle variability ( 31 , 32 , 34 , 35 , 49 ). From empirical trajectories, other studies obtained different vehicle-driver parameters for every specific model ( 34 , 50 ).…”

Section: Underlying Microscopic Reasons For Macroscopic Phenomenamentioning

confidence: 99%

“…Similarly, the inter-drivers difference of responses to the flow disturbance can reveal the mechanism of the probabilistic breakdown occurrence at bottlenecks ( 32 , 50 ). Yang et al conjecture that some following drivers are not affected by the unnoticed driving changes of their leaders as much as others at sags ( 32 ). To model this phenomenon, they estimate different CF parameters distributions and propose a microscopic simulation.…”

Section: Underlying Microscopic Reasons For Macroscopic Phenomenamentioning

confidence: 99%

“…The uniform distribution is chosen for its simplicity, as well as the truncated Gaussian. The Gamma distribution was also selected because, in the literature, some authors found this shape is adequate for the reaction-time distribution ( 32 ).…”

Section: Microscopic Modeling Frameworkmentioning

confidence: 99%

See 3 more Smart Citations

Impact of Variability of Car-Following Parameters on Road Capacity: The Simple Case of Newell’s Model

Patiño

Buisson

Keyvan‐Ekbatani

2022

Transportation Research Record: Journal of the Transportation R

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This paper contributes to the vehicle-level analysis of two macroscopic features of the road traffic: capacity variability and capacity drop. This paper focuses only on car-following (CF) behavior and leaves the part related to lane-change maneuvers for future research. In particular, a simplistic CF model (Newell’s with bounded acceleration) for a single-lane scenario is studied. In this work, by introducing a speed limitation across a zone, a bottleneck with variable nominal capacity has been created. A continuous event-based numerical resolution method is used. Consequently, it is possible to vary the three Newell’s model parameters: maximal acceleration, minimal distance, and reaction time. It has been shown that the variability of those CF parameters (e.g., reaction time, minimal distance, and maximal acceleration) has a strong impact on pre-breakdown capacity variation and also on queue discharge flow. It has been concluded that this parameters variability does affect the drop (provided that the maximal acceleration has a relatively high mean value). Various distribution shapes (uniform, truncated Gaussian, and Gamma) have been explored. It has been realized that this does not have any significant impact on the capacity distribution. Concerning the amplitude of the capacity distribution, it is demonstrated that reaction time is the parameter with the highest impact followed by minimal distance. If all parameters vary with an amplitude of 30%, it is shown that the capacity standard deviation, in this scenario without lane changes, is about half the experimental values reported in the literature.

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Section: Car-following (Cf) Approachesmentioning

confidence: 99%

Section: Underlying Microscopic Reasons For Macroscopic Phenomenamentioning

confidence: 99%

Section: Underlying Microscopic Reasons For Macroscopic Phenomenamentioning

confidence: 99%

Section: Microscopic Modeling Frameworkmentioning

confidence: 99%

See 2 more Smart Citations

Impact of Variability of Car-Following Parameters on Road Capacity: The Simple Case of Newell’s Model

Patiño

Buisson

Keyvan‐Ekbatani

2022

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…Zhou et al provided a more stable carfollowing model by considering the moving features of the car ahead [13]. Tang et al found that the headway is an important parameter that affects the risk assessment of the driver [14][15][16]. Furthermore, based on the time headway (THW) and driving speed, Simons-Morton et al provided a method that can identify the age and gender of the driver [17].…”

Section: Introductionmentioning

confidence: 99%

A driver’s car-following behavior prediction model based on multi-sensors data

Wang

et al. 2020

J Wireless Com Network

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The prerequisite for the effective operation of vehicle collision warning system is that the necessary operation is not implemented. Therefore, the behavior prediction that the driver should perform when the preceding vehicle braking is the key to improve the effectiveness of the warning system. This study was conducted to acquire characteristics in the car-following behavior when confronted by the braking of the preceding vehicle, including the reaction time and operation behavior, and establish a behavior prediction model. A driving experiment on the expressway was conducted using devices, such as millimeter-wave radars and controller area network (CAN) bus data, to acquire 845 segments of car following when the brake lamps of the car ahead are on. Data analysis demonstrates that the mean of time distance of car following, mean of car-following distance, and time-to-collision (TTC) mean are closely related with whether or not the driver slowed the car down. The operation states of the driver were divided into keeping the unchanged state of the degree of accelerator pedal opening, loosening of accelerator pedal without braking, braking, and other special situations with the input variables of car-following distance, speed of driver's car, relative speed, time distance, and TTC using the support vector machine (SVM) method to build a prediction model for the operation behavior of the driver. The verification result showed that the model predicts driving behavior with an accuracy rate of 80%. It reflects the actual decision-making process of the driver, especially the normal operation of the driver, to loosen the accelerator pedal without braking. This model can help to optimize the algorithm of the rear-end accident warning system and improve intelligent system acceptance.

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Modelling the effect of aggressive driver behavior on longitudinal performance measures during car-following

Adavikottu¹,

Velaga²,

Mishra³

2023

Transportation Research Part F: Traffic Psychology and Behaviou

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Variability of observed drivers’ car-following behavior on expressway basic segment

Cited by 5 publications

References 8 publications

Impact of Variability of Car-Following Parameters on Road Capacity: The Simple Case of Newell’s Model

Impact of Variability of Car-Following Parameters on Road Capacity: The Simple Case of Newell’s Model

A driver’s car-following behavior prediction model based on multi-sensors data

Modelling the effect of aggressive driver behavior on longitudinal performance measures during car-following

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