Reinforcement learning(RL)can free automated vehicles(AVs)from the car-following constraints and provide more possible explorations for mixed behavior.This study uses deep RL as AVs’longitudinal control and designs a...Reinforcement learning(RL)can free automated vehicles(AVs)from the car-following constraints and provide more possible explorations for mixed behavior.This study uses deep RL as AVs’longitudinal control and designs a multi-level objectives framework for AVs’trajectory decision-making based on multi-agent DRL.The saturated signalized intersection is taken as the research object to seek the upper limit of traffic efficiency and realize the specific target control.The simulation results demonstrate the convergence of the proposed framework in complex scenarios.When prioritizing throughputs as the primary objective and emissions as the secondary objective,both indicators exhibit a linear growth pattern with increasing market penetration rate(MPR).Compared with MPR is 0%,the throughputs can be increased by 69.2%when MPR is 100%.Compared with linear adaptive cruise control(LACC)under the same MPR,the emissions can also be reduced by up to 78.8%.Under the control of the fixed throughputs,compared with LACC,the emission benefits grow nearly linearly as MPR increases,it can reach 79.4%at 80%MPR.This study employs experimental results to analyze the behavioral changes of mixed flow and the mechanism of mixed autonomy to improve traffic efficiency.The proposed method is flexible and serves as a valuable tool for exploring and studying the behavior of mixed flow behavior and the patterns of mixed autonomy.展开更多
This paper introduces an information point factor and attempts to identify how it affects saturation flow and their relationship at signalized intersections. An information point is defined as any object, structure, o...This paper introduces an information point factor and attempts to identify how it affects saturation flow and their relationship at signalized intersections. An information point is defined as any object, structure, or activity located outside of a traveling vehicle that can potentially attract the visual attention of the driver. Saturation flow rates are studied at three pairs of signalized intersections in Toledo, Ohio, USA. Each pair of intersections consists of one intersection with a high number of information points and one intersection with a low number of information points. Study results reveal that, for each pair of intersections, the one with high information points has a lower saturation flow rate than the one with low information points. A statistical analysis shows that the differences are significant. Based on the saturation flow data of the paired intersections, information point effect models are developed and presented in this paper.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.52272332 and 51578199)Heilongjiang Provincial Natural Science Foundation(Grant No.YQ2021E031)the Fundamental Research Funds for the Central Universities(Grant No.HIT.OCEF.2022026).
文摘Reinforcement learning(RL)can free automated vehicles(AVs)from the car-following constraints and provide more possible explorations for mixed behavior.This study uses deep RL as AVs’longitudinal control and designs a multi-level objectives framework for AVs’trajectory decision-making based on multi-agent DRL.The saturated signalized intersection is taken as the research object to seek the upper limit of traffic efficiency and realize the specific target control.The simulation results demonstrate the convergence of the proposed framework in complex scenarios.When prioritizing throughputs as the primary objective and emissions as the secondary objective,both indicators exhibit a linear growth pattern with increasing market penetration rate(MPR).Compared with MPR is 0%,the throughputs can be increased by 69.2%when MPR is 100%.Compared with linear adaptive cruise control(LACC)under the same MPR,the emissions can also be reduced by up to 78.8%.Under the control of the fixed throughputs,compared with LACC,the emission benefits grow nearly linearly as MPR increases,it can reach 79.4%at 80%MPR.This study employs experimental results to analyze the behavioral changes of mixed flow and the mechanism of mixed autonomy to improve traffic efficiency.The proposed method is flexible and serves as a valuable tool for exploring and studying the behavior of mixed flow behavior and the patterns of mixed autonomy.
文摘This paper introduces an information point factor and attempts to identify how it affects saturation flow and their relationship at signalized intersections. An information point is defined as any object, structure, or activity located outside of a traveling vehicle that can potentially attract the visual attention of the driver. Saturation flow rates are studied at three pairs of signalized intersections in Toledo, Ohio, USA. Each pair of intersections consists of one intersection with a high number of information points and one intersection with a low number of information points. Study results reveal that, for each pair of intersections, the one with high information points has a lower saturation flow rate than the one with low information points. A statistical analysis shows that the differences are significant. Based on the saturation flow data of the paired intersections, information point effect models are developed and presented in this paper.
