Multiple-Factors Aware Car-Following Model for Connected and Autonomous Vehicles

2021 ◽  
pp. 036119812110452
Author(s):  
Huaqing Ma ◽  
Hao Wu ◽  
Yucong Hu ◽  
Zhiwei Chen ◽  
Jialing Luo
Keyword(s):  
Autonomous Vehicles ◽  
Transportation Systems ◽  
Mixed Traffic ◽  
Traffic Flows ◽  
Simulation Experiments ◽  
Velocity Difference ◽  
Car Following ◽  
Multiple Factors ◽  
Car Following Model ◽  
The Stability

The emergence of connected and autonomous vehicles (CAV) is of great significance to the development of transportation systems. This paper proposes a multiple-factors aware car-following (MACF) model for CAVs with the consideration of multiple factors including vehicle co-optimization velocity, velocity difference of multiple PVs, and space headway of multiple PVs. The Next Generation Simulation (NGSIM) dataset and the genetic algorithm are used to calibrate the parameters of the model. The stability of the MACF model is first theoretically proved and then empirically verified via numerical simulation experiments. In addition, the VISSIM software is partially redeveloped based on the MACF model to analyze mixed traffic flows consisting of human-driven vehicles and CAVs. Results show that the integration of CAVs based on the MACF model effectively improves the average velocity and throughput of the system.

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.

Full velocity difference car-following model considering desired inter-vehicle distance

2018 ◽  
Vol 29 (02) ◽  
pp. 1850018
Author(s):  
Tong Xin ◽  
Liu Yi ◽  
Cheng Rongjun ◽  
Ge Hongxia
Keyword(s):  
Theoretical Analysis ◽  
Numerical Simulations ◽  
Traffic Congestion ◽  
Control Method ◽  
Stability Conditions ◽  
Velocity Difference ◽  
Car Following ◽  
Car Following Model ◽  
The Stability

Based on the full velocity difference car-following model, an improved car-following model is put forward by considering the driver’s desired inter-vehicle distance. The stability conditions are obtained by applying the control method. The results of theoretical analysis are used to demonstrate the advantages of our model. Numerical simulations are used to show that traffic congestion can be improved as the desired inter-vehicle distance is considered in the full velocity difference car-following model.

An extended car-following model considering the acceleration derivative in some typical traffic environments

2018 ◽  
Vol 32 (08) ◽  
pp. 1850020 ◽  
Author(s):  
Tong Zhou ◽  
Dong Chen ◽  
Weining Liu
Keyword(s):  
Traffic Congestion ◽  
Extended Model ◽  
Velocity Difference ◽  
Car Following ◽  
Jamming Transition ◽  
Traffic Jams ◽  
Car Following Model ◽  
Starting Process ◽  
The Stability ◽  
Space Time Evolution

Based on the full velocity difference and acceleration car-following model, an extended car-following model is proposed by considering the vehicle’s acceleration derivative. The stability condition is given by applying the control theory. Considering some typical traffic environments, the results of theoretical analysis and numerical simulation show the extended model has a more actual acceleration of string vehicles than that of the previous models in starting process, stopping process and sudden brake. Meanwhile, the traffic jams more easily occur when the coefficient of vehicle’s acceleration derivative increases, which is presented by space-time evolution. The results confirm that the vehicle’s acceleration derivative plays an important role in the traffic jamming transition and the evolution of traffic congestion.

A Control Method Considering Two Velocity Difference Effect in the Car Following Model

2012 ◽  
Vol 198-199 ◽  
pp. 962-965
Author(s):  
Jian Yu ◽  
Rong Jun Cheng ◽  
Hong Xia Ge
Keyword(s):  
Traffic Congestion ◽  
Control Method ◽  
Velocity Difference ◽  
Traffic System ◽  
Car Following ◽  
Traffic State ◽  
Control Signals ◽  
Car Following Model ◽  
The Stability ◽  
Difference Effect

A modified car following model is put forward considering the headway distance of two successive vehicles in front. A control method to suppress traffic congestion is proposed for car following model. According to the control theory, the stability conditions are derived. The feedback signals, which act on our traffic system, consider two velocity difference effect. The control signals will play an effect only if the traffic state is in congestion. The corresponding numerical simulation results are agree well with our theoretical analysis.

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.

Nonlinear analysis of a new car-following model accounting for the global average optimal velocity difference

2016 ◽  
Vol 30 (27) ◽  
pp. 1650327 ◽  
Author(s):  
Guanghan Peng ◽  
Weizhen Lu ◽  
Hongdi He
Keyword(s):  
Nonlinear Analysis ◽  
Traffic Flow ◽  
Stable Region ◽  
Velocity Difference ◽  
Optimal Velocity ◽  
Car Following ◽  
Global Average ◽  
Car Following Model ◽  
The Stability ◽  
Difference Effect

In this paper, a new car-following model is proposed by considering the global average optimal velocity difference effect on the basis of the full velocity difference (FVD) model. We investigate the influence of the global average optimal velocity difference on the stability of traffic flow by making use of linear stability analysis. It indicates that the stable region will be enlarged by taking the global average optimal velocity difference effect into account. Subsequently, the mKdV equation near the critical point and its kink–antikink soliton solution, which can describe the traffic jam transition, is derived from nonlinear analysis. Furthermore, numerical simulations confirm that the effect of the global average optimal velocity difference can efficiently improve the stability of traffic flow, which show that our new consideration should be taken into account to suppress the traffic congestion for car-following theory.

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