公交与社会车辆混合的中观交通建模与仿真  被引量:4

Mesoscopic Modeling and Simulation of Mixed Traffic Flow of Buses and Vehicles

在线阅读下载全文

作  者:朱依婷 闫云 何兆成 Zhu Yiting;Yan Yun;He Zhaocheng(School of Intelligent Systems Engineering,Sun Yan-sen University,Guangzhou 510006,China;Guangdong Provincial Key Laboratory of Intelligent Transportation System,Guangzhou 510006,China)

机构地区:[1]中山大学智能工程学院,广东广州510006 [2]广东省智能交通系统重点实验室,广东广州510006

出  处:《系统仿真学报》2022年第9期2019-2027,共9页Journal of System Simulation

基  金:国家自然科学基金(U21B2090);南沙区营运车辆安全监管示范项目(2019SF03)。

摘  要:针对现有中观仿真模型仅将公交换算为标准社会车辆处理而忽略了公交与社会车辆差异性的问题,构建了公交与社会车辆混合交通流的中观仿真模型。在路段行驶过程,一方面考虑公交速度低于社会车辆速度的特征,建立公交速度折减函数;另一方面考虑站点溢出对邻近车道的影响,建立分车道的混合流速度模型,站点停靠与路口排队过程采用点排队模型描述。采用车辆身份检测的个体数据完成仿真标定,实验结果表明公交与社会车辆路段旅行时间的仿真误差不超过4.55%与8.20%,模型的仿真精度良好,并且可以有效刻画公交站点溢出的场景。Aiming at the problem that the existing mesoscopic simulation models only convert buses into several standard vehicles and ignore the movement difference between buses and vehicles,a mesoscopic simulation model of mixed traffic flow is proposed.In the process of road driving,on the one aspect,we consider the feature that bus speed is usually lower than vehicle speed,and correspondingly establish the reduction function of bus speed;on the other aspect,we consider the influences of bus-station queue overflow on the adjacent lanes,and correspondingly construct the lane-based speed model of mixed flow.Moreover,we use the point queuing model to describe the processes of station stop and intersection queuing.The individual-based data from vehicle identity recognition to calibrate the model parameters is introduced.The experimental results show that the mean absolute percentage errors of link travel time simulation of buses and vehicles are respectively less than 4.55%and 8.20%,the proposed model has good simulation accuracy and can effectively simulate the bus-station overflow scenarios.

关 键 词:城市交通 中观交通仿真 混合交通流 公交 社会车辆 粒子群算法 

分 类 号:TP391.9[自动化与计算机技术—计算机应用技术]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象