A cellular automation model accounting for bicycle’s group behavior

Tang, Tie-Qiao; Rui, Ying-Xu; Zhang, Jian; Shang, Hongyan

doi:10.1016/j.physa.2017.11.097

Cited by 89 publications

(22 citation statements)

References 12 publications

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“…In order to simulate this behavior, this study will establish a microscopic simulation model. e most popular microscopic simulation methods used in previous studies are car-following models and cellular automaton (CA) models [20][21][22]. Because the car-following model primarily simulates the interaction between a front and rear vehicle, the model is only applicable to one-dimensional movement and cannot represent the full mixed traffic flow.…”

Section: Literature Reviewmentioning

confidence: 99%

A Mixed-Flow Cellular Automaton Model for Vehicle Nonstrict Priority Give-Way Behavior at Crosswalks

Cheng

Lü

et al. 2020

Journal of Advanced Transportation

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The vehicle nonstrict priority give-way behavior (VNPGWB) is a common part of traffic interaction between motorized and nonmotorized vehicles in many countries. This study proposes a mixed-flow cellular automaton model to simulate the passing of vehicles in front of bicycles at crosswalks. The mixed-flow model combines a vehicle model with a bicycle model, using nonstrict priority give-way and strict give-way two driving behaviors defined as relating to the decision point rule and the launching rule, respectively. Simulation results showed that as the vehicle and bicycle inflow rates increased, a critical inflow rate divided vehicle and bicycle traffic flow into free flow and saturated flow conditions. The values of vehicle saturation flow decreased from 0.34 to 0.05, and the values of bicycle saturation flow decreased from 0.54 to 0.44, indicating that the mixed traffic flow has a negative effect on vehicle and bicycle saturated flow. Results also showed that VNPGWB effectively improves vehicle saturation flow over that of the strict give way. The advantage of VNPGWB is more signiﬁcant when vehicles and bicycles are in saturation traffic flow.

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Section: Literature Reviewmentioning

confidence: 99%

A Mixed-Flow Cellular Automaton Model for Vehicle Nonstrict Priority Give-Way Behavior at Crosswalks

Cheng

Lü

et al. 2020

Journal of Advanced Transportation

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“…This is not to say that lane formation behaviour of cyclists only occur when overtaking. Group behaviour where cyclists ride alongside each other -as implemented in Tang et al (2018) -is also likely to occur. However, such abreast riding can arguably be assumed not to influence other cyclists as long as the grouping behaviour stops once a faster cyclist rings his/her bell.…”

Section: Lane Behaviourmentioning

confidence: 99%

“…The second main category of bicycle traffic modelling is homogeneous simulation of bicycle traffic. This has also been explored with CA approaches (Jiang et al, 2004;Liu et al, 2008;Jiang et al, 2017;Tang et al, 2018), but has more interestingly also received some attention with alternative methodologies. Zhang et al (2013b) combined CA with gas dynamics models for high densities to model speed-density relationships for bicycle traffic.…”

Section: Introductionmentioning

confidence: 99%

Fast or forced to follow: A speed heterogeneous approach to congested multi-lane bicycle traffic simulation

Paulsen

Rasmussen

Nielsen

2019

Transportation Research Part B: Methodological

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“…In the past few decades, lots of models based on the actual traffic conditions have been built to study traffic flow. Normally, traffic flow models can be categorized into microscopic models [1,[6][7][8][9][10][11][12], lattice models [3,4,[13][14][15][16][17][18][19][20][21][22], and macroscopic hydrodynamic models [23][24][25][26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

An Extended Car-Following Model considering the Driver’s Desire for Smooth Driving and Self-Stabilizing Control with Velocity Uncertainty

Wang

Cheng

et al. 2020

Mathematical Problems in Engineering

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In this paper, an extended car-following model with consideration of the driver’s desire for smooth driving and the self-stabilizing control in historical velocity data is constructed. Moreover, for better reflecting the reality, we also integrate the velocity uncertainty into the new model to analyze the internal characteristics of traffic flow in situation where the historical velocity data are uncertain. Then, the model’s linear stability condition is inferred by utilizing linear stability analysis, and the modified Korteweg-de Vries (mKdV) equation is also obtained to depict the evolution properties of traffic congestion. According to the theoretical analysis, we observe that the degree of traffic congestion is alleviated when the control signal is considered, and the historical time gap and the velocity uncertainty also play a role in affecting the stability of traffic flow. Finally, some numerical simulation experiments are implemented and the experiments’ results demonstrate that the control signals including the self-stabilizing control, the driver’s desire for smooth driving, the historical time gap, and the velocity uncertainty are of avail to improve the traffic jam, which are consistent with the theoretical analytical results.

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A cellular automation model accounting for bicycle’s group behavior

Cited by 89 publications

References 12 publications

A Mixed-Flow Cellular Automaton Model for Vehicle Nonstrict Priority Give-Way Behavior at Crosswalks

A Mixed-Flow Cellular Automaton Model for Vehicle Nonstrict Priority Give-Way Behavior at Crosswalks

Fast or forced to follow: A speed heterogeneous approach to congested multi-lane bicycle traffic simulation

An Extended Car-Following Model considering the Driver’s Desire for Smooth Driving and Self-Stabilizing Control with Velocity Uncertainty

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