Analyzing the impact of automated vehicles on uncertainty and stability of the mixed traffic flow

2020 ◽  
Vol 112 ◽  
pp. 203-219 ◽  
Author(s):  
Fangfang Zheng ◽  
Can Liu ◽  
Xiaobo Liu ◽  
Saif Eddin Jabari ◽  
Liang Lu
Keyword(s):  
Traffic Flow ◽  
Mixed Traffic ◽  
Automated Vehicles ◽  
Mixed Traffic Flow ◽  
The Impact

Cellular Automata–Based Modeling and Simulation of a Mixed Traffic Flow of Manual and Automated Vehicles

2017 ◽  
Vol 2622 (1) ◽  
pp. 105-116 ◽  
Author(s):  
Da Yang ◽  
Xiaoping Qiu ◽  
Lina Ma ◽  
Danhong Wu ◽  
Liling Zhu ◽  
...  
Keyword(s):  
Reaction Time ◽  
Traffic Flow ◽  
Road Traffic ◽  
Flow Characteristics ◽  
Lane Change ◽  
Mixed Traffic ◽  
Automated Vehicles ◽  
Obvious Effect ◽  
Mixed Traffic Flow ◽  
Automated Vehicle

In recent years, automated vehicles have been developing rapidly, and some automated vehicles have begun to drive on highways. The market share of automated vehicles is expected to increase and will greatly affect traffic flow characteristics. This paper focuses on the mixed traffic flow of manual and automated vehicles. The study improves the existing cellular automaton model to capture the differences between manual vehicles and automated vehicles. Computer simulations are employed to analyze the characteristic variations in the mixed traffic flow under different automated vehicle proportions, lane change probabilities, and reaction times. Several new conclusions are drawn in the paper. First, with the increment of the proportion of automated vehicles, freeway capacity increases; the capacity increment is more significant for single-lane traffic than for two-lane traffic. Second, for single-lane traffic flow, reducing the reaction time of the automated vehicle can significantly improve road traffic capacity—as much as doubling it—and reaction time reduction has no obvious effect on the capacity of the two-lane traffic. Third, with the proportion increment of automated vehicles, lane change frequency reduces significantly. Fourth, when the density is 15 < ρ < 55 vehicles/km, the addition of 20% automated vehicles to a traffic flow that consisted of only manual vehicles can decrease congestion by up to 16.7%.

scholarly journals Potential Pollutant Emission Effects of Connected and Automated Vehicles in a Mixed Traffic Flow Context for Different Road Types

2021 ◽  
Vol 2 ◽  
pp. 364-383
Author(s):  
Jorge M. Bandeira ◽  
Eloisa Macedo ◽  
Paulo Fernandes ◽  
Monica Rodrigues ◽  
Mario Andrade ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Pollutant Emission ◽  
Mixed Traffic ◽  
Automated Vehicles ◽  
Mixed Traffic Flow

Simulation study of car accidents at the intersection of two roads in the mixed traffic flow

2015 ◽  
Vol 26 (01) ◽  
pp. 1550007 ◽  
Author(s):  
R. Marzoug ◽  
H. Ez-Zahraouy ◽  
A. Benyoussef
Keyword(s):  
Cellular Automata ◽  
Traffic Flow ◽  
Critical Density ◽  
Mixed Traffic ◽  
Priority Rules ◽  
Mixed Traffic Flow ◽  
Car Accidents ◽  
First Case ◽  
Accident Probability ◽  
The Impact

Using cellular automata (CA) Nagel–Schreckenberg (NaSch) model, we numerically study the probability P ac of the occurrence of car accidents at nonsignalized intersection when drivers do not respect the priority rules. We also investigated the impact of mixture lengths and velocities of vehicles on this probability. It is found that in the first case, where vehicles distinguished only by their lengths, the car accidents start to occur above a critical density ρc. Furthermore, the increase of the fraction of long vehicles (FL) delays the occurrence of car accidents (increasing ρc) and increases the risk of collisions when ρ > ρc. In other side, the mixture of maximum velocities (with same length for all vehicles) leads to the appearance of accidents at the intersection even in the free flow regime. Moreover, the increase of the fraction of fast vehicles (Ff) reduces the accident probability (P ac ). The influence of roads length is also studied. We found that the decrease of the roads length enhance the risk of collision.

Traffic Oscillation using Stochastic Lagrangian Dynamics: Simulation and Mitigation via Control of Autonomous Vehicles

2019 ◽  
Vol 2673 (7) ◽  
pp. 1-11
Author(s):  
Fangfang Zheng ◽  
Liang Lu ◽  
Ruijie Li ◽  
Xiaobo Liu ◽  
Youhua Tang
Keyword(s):  
Traffic Flow ◽  
Autonomous Vehicles ◽  
Control Strategies ◽  
Dynamics Simulation ◽  
Lagrangian Model ◽  
Mixed Traffic ◽  
Mixed Traffic Flow ◽  
Sample Paths ◽  
Stop And Go ◽  
The Impact

The phenomenon of stop-and-go waves is frequently observed in congested traffic. With the development of connected and autonomous vehicle (CAV) technologies, it is possible to reduce traffic oscillation via control of CAVs in a mixed traffic flow with both human drivers and autonomous vehicles (AVs). This paper introduces a stochastic Lagrangian model which is capable of simulating stop-and-go traffic considering the heterogeneity of drivers. The sample paths of the stochastic process are smooth without aggressive oscillation. The model is further extended to the mixed traffic flow condition, considering stochastic human driving behavior and deterministic behavior of AVs. With the proposed model, the variation of performance of AV control strategies can be quantified in addition to the average performance. A numerical example with a single lane circular road is used to investigate the impact of the AV control strategy on mitigating stop-and-go waves. Both qualitative and quantitative results show that the phenomenon of stop-and-go waves can be reduced significantly with only one AV, while the increase of AVs from 10% (two AVs) to 50% (10 AVs) offers just marginal improvement in relation to the ensemble-averaged performance and 95% confidence interval of the ensemble-averaged performance. The proposed simulation approach based on the stochastic Lagrangian model can effectively investigate the impact of AV control strategies on traffic oscillation, considering in particular the uncertainty of human driver behavior.

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