A new lattice model of traffic flow considering driver’s anticipation effect of the traffic interruption probability

2018 ◽  
Vol 507 ◽  
pp. 374-380 ◽  
Author(s):  
Guanghan Peng ◽  
Hua Kuang ◽  
Li Qing
Keyword(s):  
Traffic Flow ◽  
Lattice Model ◽  
Driver’S Anticipation Effect ◽  
Anticipation Effect

A NEW LATTICE MODEL OF TWO-LANE TRAFFIC FLOW WITH THE CONSIDERATION OF MULTI-ANTICIPATION EFFECT

2013 ◽  
Vol 24 (07) ◽  
pp. 1350048 ◽  
Author(s):  
GUANGHAN PENG
Keyword(s):  
Numerical Simulation ◽  
Stability Analysis ◽  
Traffic Flow ◽  
Linear Stability ◽  
Lattice Model ◽  
Mkdv Equation ◽  
Stable Region ◽  
Lane Changing ◽  
Traffic Jam ◽  
Anticipation Effect

In this paper, a new two-lane lattice model of traffic flow is proposed with the consideration of multi-anticipation effect. The linear stability condition of two-lane traffic is derived with the multi-anticipation effect term by linear stability analysis, which shows that the stable region enlarges with the number of multi-anticipation sites increasing. Nonlinear analysis near the critical point is carried out to obtain kink–antikink soliton solution of the mKdV equation with the multi-anticipation effect term. Numerical simulation also shows that the multi-anticipation effect can suppress the traffic jam efficiently with lane changing in two-lane system.

The driver’s anticipation effect with passing in lattice model for two-lane freeway

2015 ◽  
Vol 29 (28) ◽  
pp. 1550174 ◽  
Author(s):  
Guanghan Peng
Keyword(s):  
Numerical Simulation ◽  
Linear Stability ◽  
Lattice Model ◽  
Mkdv Equation ◽  
Lane Changing ◽  
Traffic System ◽  
Driver’S Anticipation Effect ◽  
The Stability ◽  
Anticipation Effect ◽  
Anticipation Effects

In this paper, a new lattice model is proposed with the consideration of the driver’s anticipation effect with passing for two-lane traffic system. The linear stability condition and the mKdV equation which are correlative to the driver’s anticipation effect with passing are derived from linear stability analysis and nonlinear analysis, respectively. Numerical simulation shows that the driver’s anticipation effects with passing can efficiently enhance the stability of traffic flow under lane changing on two-lane highway.

A new lattice model of traffic flow with the anticipation effect of potential lane changing

2012 ◽  
Vol 376 (4) ◽  
pp. 447-451 ◽  
Author(s):  
G.H. Peng ◽  
X.H. Cai ◽  
C.Q. Liu ◽  
M.X. Tuo
Keyword(s):  
Traffic Flow ◽  
Lattice Model ◽  
Lane Changing ◽  
Anticipation Effect

A two-lane lattice model considering taillight effect and man–machine hybrid driving

2020 ◽  
Vol 34 (32) ◽  
pp. 2050365
Author(s):  
Siyuan Chen ◽  
Changxi Ma ◽  
Jinchou Gong
Keyword(s):  
Traffic Flow ◽  
Lattice Model ◽  
Critical Density ◽  
Mkdv Equation ◽  
Flow Stability ◽  
Stability Conditions ◽  
Neutral Stability ◽  
Nonlinear Stability Analysis ◽  
Traffic Flow Stability ◽  
The Stability

At present, drivers can rely on road communication technology to obtain the current traffic status information, and the development of intelligent transportation makes self-driving possible. In this paper, considering the mixed traffic flow with self-driving vehicles and the taillight effect, a new macro-two-lane lattice model is established. Combined with the concept of critical density, the judgment conditions for vehicles to take braking measures are given. Based on the linear analysis, the stability conditions of the new model are obtained, and the mKdV equation describing the evolution mechanism of density waves is derived through the nonlinear stability analysis. Finally, with the help of numerical simulation, the phase diagram and kink–anti-kink waveform of neutral stability conditions are obtained, and the effects of different parameters of the model on traffic flow stability are analyzed. The results show that the braking probability, the proportion of self-driving vehicles and the critical density have significant effects on the traffic flow stability. Considering taillight effect and increasing the mixing ratio of self-driving vehicles can effectively enhance the stability of traffic flow, but a larger critical density will destroy the stability of traffic flow.

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