Analysis of mixed traffic flow with human-driving and autonomous cars based on car-following model

2018 ◽  
Vol 496 ◽  
pp. 274-285 ◽  
Author(s):  
Wen-Xing Zhu ◽  
H.M. Zhang
Keyword(s):  
Traffic Flow ◽  
Mixed Traffic ◽  
Car Following ◽  
Mixed Traffic Flow ◽  
Autonomous Cars ◽  
Car Following Model

scholarly journals Risk Modeling and Quantification of a Platoon in Mixed Traffic Based on the Mass-Spring-Damper Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Luo Jiang ◽  
Jie Ji ◽  
Yue Ren ◽  
Hong Wang ◽  
Yanjun Huang
Keyword(s):  
Total Energy ◽  
Traffic Flow ◽  
Dominant Factor ◽  
Mixed Traffic ◽  
Quantitative Index ◽  
Car Following ◽  
Mixed Traffic Flow ◽  
Emergency Braking ◽  
Car Following Model ◽  
Mass Spring

Connected and automated vehicle (CAV) technologies have great potential to improve road safety. However, an emerging type of mixed traffic flow with human-driven vehicles (HDVs) and CAVs has also arisen in recent years. To improve the overall safety of this mixed traffic flow, a novel car-following model is proposed to control the driving behaviors of the above two types of vehicles in a platoon from the perspective of a mechanical system, mass-spring-damper (MSD) system. Furthermore, a quantitative index is proposed by incorporating the psychological field theory into the MSD model. The errors of spacing and speed in the car-following processes can be expressed as the accumulation of the virtual total energy, and the magnitude of the energy is used to reflect the danger level of vehicles in the mixed platoon. At the same time, the optimization model of minimum total energy is solved under the constraints of vehicle dynamics and the mechanical characteristics of the MSD system, and the optimal solutions are used as the parameters of the MSD car-following model. Finally, a mixed platoon composed of 3 CAVs and 2 HDVs without performing lane changing is tested using the driver-in-the-loop test platform. The test results show that, in the mixed platoon, CAVs can optimally adjust the intervehicle spacing by making full use of the braking distance, which also provides sufficient reaction time for the driver of HDV to avoid rear-end collisions. Furthermore, in the early stage of the emergency braking, the spacing error is the dominant factor influencing the car-following behaviors, but in the later stage of emergency braking, the speed error becomes the decisive factor of the car-following behaviors. These results indicate that the proposed car-following model and quantitative index are of great significance for improving the overall safety of the mixed traffic flow with CAVs and HDVs.

Analytical studies on an extended car following model for mixed traffic flow with slow and fast vehicles

2016 ◽  
Vol 27 (01) ◽  
pp. 1650004 ◽  
Author(s):  
Zhipeng Li ◽  
Xun Xu ◽  
Shangzhi Xu ◽  
Yeqing Qian ◽  
Juan Xu
Keyword(s):  
Standard Deviation ◽  
Traffic Flow ◽  
Maximum Velocity ◽  
Traffic Density ◽  
Extended Model ◽  
Mixed Traffic ◽  
Car Following ◽  
Mixed Traffic Flow ◽  
Average Maximum ◽  
Car Following Model

The car-following model is extended to take into account the characteristics of mixed traffic flow containing fast and slow vehicles. We conduct the linear stability analysis to the extended model with finding that the traffic flow can be stabilized with the increase of the percentage of the slow vehicle. It also can be concluded that the stabilization of the traffic flow closely depends on not only the average value of two maximum velocities characterizing two vehicle types, but also the standard deviation of the maximum velocities among all vehicles, when the percentage of the slow vehicles is the same as that of the fast ones. With increase of the average maximum velocity, the traffic flow becomes more and more unstable, while the increase of the standard deviation takes negative effect in stabilizing the traffic system. The direct numerical results are in good agreement with those of theoretical analysis. Moreover, the relation between the flux and the traffic density is investigated to simulate the effects of the percentage of slow vehicles on traffic flux in the whole density regions.

A control method applied to mixed traffic flow for the coupled-map car-following model

2014 ◽  
Vol 23 (3) ◽  
pp. 030507 ◽  
Author(s):  
Rong-Jun Cheng ◽  
Xiang-Lin Han ◽  
Siu-Ming Lo ◽  
Hong-Xia Ge
Keyword(s):  
Traffic Flow ◽  
Control Method ◽  
Mixed Traffic ◽  
Car Following ◽  
Mixed Traffic Flow ◽  
Car Following Model

CAV Nested Car-Following Model Based on Characteristics of Mixed Traffic Flow

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Haoshun Luo ◽  
Liangjie Xu ◽  
Xiaohan Wang ◽  
Shen Li ◽  
Pengyun Zhao
Keyword(s):  
Traffic Flow ◽  
Mixed Traffic ◽  
Car Following ◽  
Mixed Traffic Flow ◽  
Model Based ◽  
Car Following Model

scholarly journals An Asymmetric-Anticipation Car-following Model in the Era of Autonomous-Connected and Human-Driving Vehicles

2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Ammar Jafaripournimchahi ◽  
Wusheng Hu ◽  
Lu Sun
Keyword(s):  
Energy Consumption ◽  
Traffic Flow ◽  
Exhaust Emissions ◽  
Driving Behavior ◽  
Arrival Process ◽  
Motion Information ◽  
Mixed Traffic ◽  
Car Following ◽  
Mixed Traffic Flow ◽  
Car Following Model

Herein, we explored the impact of anticipation and asymmetric driving behavior on vehicle’s position, velocity, acceleration, energy consumption, and exhaust emissions of CO, HC, and NOx in mixed traffic flow. We present an asymmetric-anticipation car-following model (AAFVD) considering the motion information from two direct preceding vehicles (i.e., human-driving (HD) and autonomous and connected (AC) vehicles platoon) via wireless data transmission. The linear stability approach was used to evaluate the properties of the AAFVD model. Our simulations revealed that the drivers’ anticipation factor using the motion information from two direct preceding vehicles in connected vehicles environment can effectively improve traffic flow stability. The vehicle’s departure and arrival process while passing through a signal lane with a traffic light considering the anticipation and asymmetric driving behavior, and the motion information from two direct preceding vehicles was explored. Our numerical results demonstrated that the AAFVD model can decrease the velocity fluctuations, energy consumption, and exhaust emissions of vehicles in mixed traffic flow system.

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