首页|考虑加减速行为不对称的终端区空中交通流跟驰模型

考虑加减速行为不对称的终端区空中交通流跟驰模型

扫码查看
为精细化描述空中交通流跟驰行为,考虑空中交通加减速跟驰行为的不对称性,本文提出一种基于改进智能驾驶员模型(Intelligent Driver Model,IDM)的空中交通流跟驰模型.采用成都终端区实际航迹数据,筛选出跟驰航迹数据;在此基础上,应用遗传算法对经典跟驰模型进行参数标定,选取出IDM模型的拟合精度最好;进而,考虑加速度、前后机间隔、平均速度差和机型等因素,分别构建加速和减速场景下的改进IDM跟驰模型;最后,采用奈奎斯特图分析改进模型的局部稳定性和渐近稳定性,并对加减速场景下的改进跟驰模型进行仿真实验.结果表明,改进IDM模型的稳定范围增大,且在加速和减速场景下,改进IDM模型所需稳定时间分别仅为原始IDM模型的13.9%和22.2%,头机为不同机型时,模型均表现较强稳定性,且头机为重型机时,所需稳定时间最长,这与实际运行中重型机产生的尾流对后机影响最为严重相一致.
An Aircraft-following Model for Air Traffic Flow in Terminal Airspace Considering Asymmetric Acceleration and Deceleration Behavior
To accurately describe the following behavior of air traffic flow,particularly considering the asymmetry of air traffic acceleration and deceleration,an air traffic flow following model based on an improved intelligent driver model(IDM)is proposed.Firstly,using actual flight trajectory data from the Chengdu terminal area,the following trajectory data is selected.The genetic algorithm is used to calibrate the parameters of the different classical following models,ultimately selecting the IDM model due to its superior fitting accuracy.Next,the improved IDM following models specifically for acceleration and deceleration scenarios are established considering factors such as acceleration,inter-vehicle spacing,average speed differences,and aircraft types.To assess the stability of the improved model,the Nyquist plot is used to analyze the local stability and asymptotic stability.Simulation experiments are conducted on the improved following model for acceleration and deceleration scenarios.The results show that the stable range of the improved IDM model is increased.For the acceleration and deceleration scenarios,the stability time of the improved IDM model is only 13.9%and 22.2%of the original IDM model,respectively.The model shows strong stability for different types of leading aircraft,and the leading aircraft of heavy requires the longest stable time,which is consistent with the practical observation that heavy aircraft generate the most severe wake turbulence in actual operations.

air transportationair traffic flowaircraft-following modelstabilityintelligent driver modelparameter calibration

李善梅、纪亚宏、王超、黄宝军、王远洲、赵末

展开 >

中国民航大学,空中交通管理学院,天津 300300

中国民用航空西南地区空中交通管理局,成都 610225

航空运输 空中交通流 跟驰模型 稳定性 智能驾驶员模型 参数标定

2024

交通运输系统工程与信息
中国系统工程学会

交通运输系统工程与信息

CSTPCD北大核心
影响因子:0.664
ISSN:1009-6744
年,卷(期):2024.24(6)