New full velocity difference model considering the driver’s heterogeneity of the disturbance risk preference for car-following theory

2016 ◽  
Vol 30 (01) ◽  
pp. 1550241 ◽  
Author(s):  
You-Zhi Zeng ◽  
Ning Zhang
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Flow Instability ◽  
Risk Preference ◽  
Difference Model ◽  
Velocity Difference ◽  
Absolute Size ◽  
Car Following ◽  
Preceding Vehicle ◽  
Preference Coefficient

This paper proposes a new full velocity difference model considering the driver’s heterogeneity of the disturbance risk preference for car-following theory to investigate the effects of the driver’s heterogeneity of the disturbance risk preference on traffic flow instability when the driver reacts to the relative velocity. We obtain traffic flow instability condition and the calculation method of the unstable region headway range and the probability of traffic congestion caused by a small disturbance. The analysis shows that has important effects the driver’s heterogeneity of the disturbance risk preference on traffic flow instability: (1) traffic flow instability is independent of the absolute size of the driver’s disturbance risk preference coefficient and depends on the ratio of the preceding vehicle driver’s disturbance risk preference coefficient to the following vehicle driver’s disturbance risk preference coefficient; (2) the smaller the ratio of the preceding vehicle driver’s disturbance risk preference coefficient to the following vehicle driver’s disturbance risk preference coefficient, the smaller traffic flow instability and vice versa. It provides some viable ideas to suppress traffic congestion.

scholarly journals A Modified Car-following Model Considering Traffic Density and Acceleration of Leading Vehicle

2020 ◽  
Vol 10 (4) ◽  
pp. 1268
Author(s):  
Xudong Cao ◽  
Jianjun Wang ◽  
Chenchen Chen
Keyword(s):  
Traffic Flow ◽  
Linear Stability Theory ◽  
Traffic Density ◽  
Neutral Stability ◽  
Difference Model ◽  
Velocity Difference ◽  
Car Following ◽  
Car Following Model ◽  
The Difference ◽  
Improved Model

Although the difference between the velocity of two successive vehicles is considered in the full velocity difference model (FVDM), more status information from preceding vehicles affecting the behavior of car-following has not been effectively utilized. For improving the performance of the FVDM, an extended modified car-following model taking into account traffic density and the acceleration of a leading vehicle (DAVD, density and acceleration velocity difference model) is presented under the condition of vehicle-to-vehicle (V2V) communications. Stability in the developed model is derived through applying linear stability theory. The curves of neutral stability for the improved model indicate that when the driver pays more attention to the traffic status in front, the traffic flow stability region is larger. Numerical simulation illustrates that traffic flow disturbance could be suppressed by gaining more information on preceding vehicles.

A New Car-Following Model with the Consideration of the Leader’s Acceleration

2015 ◽  
Vol 738-739 ◽  
pp. 489-492
Author(s):  
Tong Zhou ◽  
Yu Xuan Li ◽  
Zhan Wei Bai
Keyword(s):  
Numerical Simulation ◽  
Traffic Flow ◽  
Linear Stability ◽  
Linear Stability Theory ◽  
Difference Model ◽  
Velocity Difference ◽  
Optimal Velocity ◽  
New Model ◽  
Car Following ◽  
Car Following Model

Based on the optimal velocity difference model (for short, OVDM) proposed by Peng et al., a new car-following model is presented by considering the leading cars’ acceleration. The linear stability condition of the new model is obtained by using the linear stability theory. Numerical simulation shows that the new model can avoid the disadvantage of negative velocity occurred in the OVDM by adjusting the coefficient of the leaders acceleration and can stabilize traffic flow more effectively.

scholarly journals A New Car-Following Model considering Driving Characteristics and Preceding Vehicle’s Acceleration

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Yong Zhang ◽  
Ping Ni ◽  
Minwei Li ◽  
Hao Liu ◽  
Baocai Yin
Keyword(s):  
Traffic Flow ◽  
Clustering Algorithm ◽  
Traffic Data ◽  
Velocity Difference ◽  
Car Following ◽  
The Past ◽  
Preceding Vehicle ◽  
Car Following Model ◽  
The Stability ◽  
Anticipation Effects

In the past decades, many improved car-following models based on the full velocity difference (FVD) model have been developed. But these models do not consider the acceleration of leading vehicle. Some of them consider individual anticipation behavior of drivers, but they either do not quantitatively determine the types of driving or artificially divide the driving types rather than deriving them from actual traffic data. In this paper, driver’s driving styles are firstly categorized based on actual traffic data via data mining and clustering algorithm. Secondly, a new car-following model based on FVD model is developed, taking into account individual anticipation effects and the acceleration of leading vehicle. The effect of driving characteristics and leading vehicle’s acceleration on car-following behavior is further analyzed via numerical simulation. The results show that considering the acceleration of preceding vehicle in the model improves the stability of traffic flow and different driving characteristics have different influence on the stability of traffic flow.

A Control Method for Car Following Model Considering Changing Behaviors

2012 ◽  
Vol 198-199 ◽  
pp. 954-957
Author(s):  
Xiang Pei Meng ◽  
Rong Jun Cheng ◽  
Hong Xia Ge
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Control Method ◽  
Velocity Difference ◽  
Lane Changing ◽  
Traffic System ◽  
Car Following ◽  
Congested Traffic ◽  
Car Following Model ◽  
The Stability

We propose a simple control method to suppress two-lane traffic congestion for full velocity difference (for short, FVD) car-following model. The influence of lane changing behaviors is also studied in the stability of two-lane traffic flow under the boundary condition, and the friction interference which is from the neighbor lane has been taken into account. We derive the stability conditions by the control method. The feedback signals, which include vehicular information from both lanes, acting on the two-lane traffic system have been extended to the FVD car-following model. Theoretically, lane changing behaviors can break the stability of two-lane traffic flow and aggravate traffic perturbation, but it is proven that the congested traffic in two-lane traffic flow could be suppressed by using this control method.

Anticipation Driving Behavior in Car Following Theory

2014 ◽  
Vol 505-506 ◽  
pp. 1133-1136
Author(s):  
Feng Lv ◽  
Cai Hong Ye ◽  
Hong Xia Ge
Keyword(s):  
Traffic Flow ◽  
Critical Phenomenon ◽  
Driving Behavior ◽  
Thermodynamic Theory ◽  
Analysis Method ◽  
Difference Model ◽  
Velocity Difference ◽  
Ginzburg Landau ◽  
Car Following ◽  
Car Following Model

In this paper, a new anticipation driving car-following model (AD-TVDM) is presented based on the two velocity difference model (TVDM)[1], taking into the effect of anticipation driving behavior in real world. The nature of the model is investigated by using linear and nonlinear analysis method. A thermodynamic theory is formulated to describe the phase transition and critical phenomenon in traffic flow and the time-dependent Ginzburg-Landau (TDGL) equation is derived to describe the traffic flow near the critical point.

Surrounding Vehicles’ Contribution to Car-Following Models: Deep-Learning-Based Analysis

2021 ◽  
pp. 036119812110186
Author(s):  
Saeed Vasebi ◽  
Yeganeh M. Hayeri ◽  
Peter J. Jin
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Short Term Memory ◽  
Data Availability ◽  
Car Following ◽  
Preceding Vehicle ◽  
Long Short Term Memory ◽  
The Right ◽  
Mean Square Errors ◽  
Deep Learning Model

Relatively recent increased computational power and extensive traffic data availability have provided a unique opportunity to re-investigate drivers’ car-following (CF) behavior. Classic CF models assume drivers’ behavior is only influenced by their preceding vehicle. Recent studies have indicated that considering surrounding vehicles’ information (e.g., multiple preceding vehicles) could affect CF models’ performance. An in-depth investigation of surrounding vehicles’ contribution to CF modeling performance has not been reported in the literature. This study uses a deep-learning model with long short-term memory (LSTM) to investigate to what extent considering surrounding vehicles could improve CF models’ performance. This investigation helps to select the right inputs for traffic flow modeling. Five CF models are compared in this study (i.e., classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models). Performance of the CF models is compared in relation to accuracy, stability, and smoothness of traffic flow. The CF models are trained, validated, and tested by a large publicly available dataset. The average mean square errors (MSEs) for the classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models are 1.58 × 10−3, 1.54 × 10−3, 1.56 × 10−3, 1.61 × 10−3, and 1.73 × 10−3, respectively. However, the results show insignificant performance differences between the classic CF model and multi-anticipative model or adjacent-lanes model in relation to accuracy, stability, or smoothness. The following-vehicle CF model shows similar performance to the multi-anticipative model. The all-surrounding-vehicles CF model has underperformed all the other models.

An improved car-following model considering the impact of safety messages

2018 ◽  
Vol 32 (32) ◽  
pp. 1850398 ◽  
Author(s):  
Tenglong Li ◽  
Fei Hui ◽  
Xiangmo Zhao
Keyword(s):  
Traffic Congestion ◽  
Dominant Factor ◽  
Velocity Difference ◽  
Car Following ◽  
Safety Message ◽  
Warning Messages ◽  
Gray Correlation ◽  
Car Following Model ◽  
The Stability ◽  
The Impact

The existing car-following models of connected vehicles commonly lack experimental data as evidence. In this paper, a Gray correlation analysis is conducted to explore the change in driving behavior with safety messages. The data mining analysis shows that the dominant factor of car-following behavior is headway with no safety message, whereas the velocity difference between the leading and following vehicle becomes the dominant factor when warning messages are received. According to this result, an extended car-following model considering the impact of safety messages (IOSM) is proposed based on the full velocity difference (FVD) model. The stability criterion of this new model is then obtained through a linear stability analysis. Finally, numerical simulations are performed to verify the theoretical analysis results. Both analytical and simulation results show that traffic congestion can be suppressed by safety messages. However, the IOSM model is slightly less stable than the FVD model if the average headway in traffic flow is approximately 14–20 m.

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

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Shihao Li ◽  
Ting Wang ◽  
Rongjun Cheng ◽  
Hongxia Ge
Keyword(s):  
Traffic Flow ◽  
Linear Stability ◽  
Traffic Congestion ◽  
The Self ◽  
Historical Time ◽  
Velocity Data ◽  
Car Following ◽  
Stabilizing Control ◽  
Car Following Model ◽  
Time Gap

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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