车头时距
理论(学习稳定性)
流量(计算机网络)
感知
计算机科学
模拟
流量(数学)
控制理论(社会学)
控制(管理)
数学
心理学
计算机安全
人工智能
几何学
机器学习
神经科学
作者
Shuke An,Liangjie Xu,Guojun Chen,Zeyu Shi
标识
DOI:10.1142/s0217984920501821
摘要
In order to explore the influence of driver’s characteristics in complex traffic flow, experienced, inexperienced attribution and the perception headway of the driver are introduced. Concurrently, an extended car-following model is established. The linear stability of the extended model is derived based on the control theory method, and obtains the stability conditions. This work verifies the impact of driver characteristics on traffic flow stability based on the open boundary simulation environment. The research results show that inexperienced driver will reduce the stability of traffic flow on complex roads, while experienced driver will improve the stability of traffic flow. Compared with the driver’s negative perception headway error, the positive perception headway error can improve the stability of traffic flow. More specifically, an experienced driver is good at predicting the state of the preceding vehicle, while the driver’s positive perception headway error tends to narrow the safe headway, and achieve the stability of traffic flow.
科研通智能强力驱动
Strongly Powered by AbleSci AI