THE INFLUENCE OF INDIVIDUAL DRIVER CHARACTERISTICS ON CONGESTION FORMATION

2011 ◽  
Vol 22 (03) ◽  
pp. 305-318 ◽  
Author(s):  
LANJUN WANG ◽  
HAO ZHANG ◽  
HUADONG MENG ◽  
XIQIN WANG
Keyword(s):  
Stability Analysis ◽  
Traffic Flow ◽  
Linear Stability Analysis ◽  
Traffic Congestion ◽  
Velocity Model ◽  
Model Parameters ◽  
Unstable Flow ◽  
Optimal Velocity ◽  
Optimal Velocity Model ◽  
Langevin Approach

Previous works have pointed out that one of the reasons for the formation of traffic congestion is instability in traffic flow. In this study, we investigate theoretically how the characteristics of individual drivers influence the instability of traffic flow. The discussions are based on the optimal velocity model, which has three parameters related to individual driver characteristics. We specify the mappings between the model parameters and driver characteristics in this study. With linear stability analysis, we obtain a condition for when instability occurs and a constraint about how the model parameters influence the unstable traffic flow. Meanwhile, we also determine how the region of unstable flow densities depends on these parameters. Additionally, the Langevin approach theoretically validates that under the constraint, the macroscopic characteristics of the unstable traffic flow becomes a mixture of free flows and congestions. All of these results imply that both overly aggressive and overly conservative drivers are capable of triggering traffic congestion.

Heterogeneous Drivers Car-Following Model and Stability Analysis

2012 ◽  
Vol 253-255 ◽  
pp. 1631-1636
Author(s):  
Jing Shan Pan ◽  
Li Dong Zhang
Keyword(s):  
Stability Analysis ◽  
Traffic Flow ◽  
Flow Characteristics ◽  
Velocity Model ◽  
Sensitivity Factor ◽  
Past Study ◽  
Optimal Velocity ◽  
Flow Models ◽  
Car Following ◽  
Optimal Velocity Model

Optimal Velocity Model (OVM) is one of the typical car-following traffic flow models. The driver’s sensitivity factor in OVM is always constant in the past study, which does not fully comply with practical traffic flow characteristics. To gain a more actual and objective model, we propose a kind of heterogeneous drivers car-following optimal velocity model, i.e. HDOVM. In this model, the constant driver’s parameter is substituted with driver type function, and every car in the queue has a corresponding value. After stability analysis with Laplace transform state space method, we make many types cars in the traffic queue numerical simulation to prove our supposition , the simulation results after many times show that the HDOVM model is more practical than traditional ones. Considering the diversity of traffic flow composition should be one of the major factors to find out the reason of traffic jam.

scholarly journals A New Car-Following Model with Consideration of Dynamic Safety Distance

2018 ◽  
Vol 2018 ◽  
pp. 1-5
Author(s):  
Tao Wang ◽  
Jing Zhang ◽  
Guangyao Li ◽  
Keyu Xu ◽  
Shubin Li
Keyword(s):  
Traffic Flow ◽  
Velocity Model ◽  
Safe Distance ◽  
Optimal Velocity ◽  
New Model ◽  
Car Following ◽  
Optimal Velocity Model ◽  
Safety Distance ◽  
Car Following Model ◽  
The Impact

In the traditional optimal velocity model, safe distance is usually a constant, which, however, is not representative of actual traffic conditions. This paper attempts to study the impact of dynamic safety distance on vehicular stream through a car-following model. Firstly, a new car-following model is proposed, in which the traditional safety distance is replaced by a dynamic term. Then, the phase diagram in the headway, speed, and sensitivity spaces is given to illustrate the impact of a variable safe distance on traffic flow. Finally, numerical methods are conducted to examine the performance of the proposed model with regard to two aspects: compared with the optimal velocity model, the new model can suppress traffic congestion effectively and, for different safety distances, the dynamic safety distance can improve the stability of vehicular stream. Simulation results suggest that the new model is able to enhance traffic flow stability.

Bifurcation phenomena in the optimal velocity model for traffic flow

2001 ◽  
Vol 64 (4) ◽  
Author(s):  
Yuji Igarashi ◽  
Katsumi Itoh ◽  
Ken Nakanishi ◽  
Kazuhiro Ogura ◽  
Ken Yokokawa
Keyword(s):  
Traffic Flow ◽  
Velocity Model ◽  
Optimal Velocity ◽  
Optimal Velocity Model ◽  
Bifurcation Phenomena

scholarly journals An Improved Lattice Hydrodynamic Model by considering the Effect of “Backward-Looking” and Anticipation Behavior

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jin Wan ◽  
Xin Huang ◽  
Wenzhi Qin ◽  
Xiuge Gu ◽  
Min Zhao
Keyword(s):  
Stability Analysis ◽  
Traffic Flow ◽  
Linear Stability ◽  
Linear Stability Analysis ◽  
Hydrodynamic Model ◽  
Traffic Congestion ◽  
Traffic Accidents ◽  
Flow Stability ◽  
Lattice Hydrodynamic Model ◽  
Traffic Flow Stability

In order to prevent the occurrence of traffic accidents, drivers always focus on the running conditions of the preceding and rear vehicles to change their driving behavior. By taking into the “backward-looking” effect and the driver’s anticipation effect of flux difference consideration at the same time, a novel two-lane lattice hydrodynamic model is proposed to reveal driving characteristics. The corresponding stability conditions are derived through a linear stability analysis. Then, the nonlinear theory is also applied to derive the mKdV equation describing traffic congestion near the critical point. Linear and nonlinear analyses of the proposed model show that how the “backward-looking” effect and the driver’s anticipation behavior comprehensively affect the traffic flow stability. The results show that the positive constant γ , the driver’s anticipation time τ , and the sensitivity coefficient p play significant roles in the improvement of traffic flow stability and the alleviation of the traffic congestion. Furthermore, the effectiveness of linear stability analysis and nonlinear analysis results is demonstrated by numerical simulations.

Sign in / Sign up

Export Citation Format

Share Document