基于智能网联环境的信号交叉口排队长度估算模型研究
Research on Queue Length Estimation Method of Signalized Intersection Based on Intelligent Connected Environment
DOI: 10.12677/CSA.2023.1312252, PDF,    国家自然科学基金支持
作者: 寇子卿, 张萌萌*:山东交通学院交通与物流工程学院,山东 济南;山东省智慧交通重点实验室(筹),山东 济南;张 洁:山东正衢交通工程有限公司,山东 济南
关键词: 智能网联环境排队长度Kraus模型sumo仿真平台Intelligent Connected Environment Queue Length Kraus Model Sumo Simulation Platform
摘要: 为了能够计算信号交叉口的动态排队长度,基于排队论模拟车辆排队与消散,建立信号交叉口动态排队长度计算模型。首先,根据排队长度的定义来界定本文所计算的排队长度;其次,将车辆排队过程视为车辆在遇前方有固定物时的减速停车过程,以此来描述排队行为并建立车辆排队模型;然后,根据改进的Kraus模型及车辆位移公式,计算每辆车的到达率,并通过到达率计算排队车辆数及排队长度;最后,基于sumo仿真平台,进行模拟仿真实验。结果表明,本文基于改进的Kraus模型建立的排队长度模型,可以提高计算信号交叉口排队长度的准确度。
Abstract: In order to calculate the dynamic queue length of a signalized intersection, vehicle queuing and dis-sipation were simulated based on the queue theory, and the calculation model of the dynamic queue length of a signalized intersection was established. Firstly, the queue length calculated in this paper is defined according to the definition of queue length. Secondly, the queuing process is regarded as the process of slowing down and stopping when the vehicle meets the fixed object in front, which describes the queuing behavior and establishes the queuing model of the vehicle. Then, according to the improved Kraus model and vehicle displacement formula, the arrival rate of each vehicle is calculated, and the number and length of queuing vehicles are calculated through the arrival rate. Finally, simulation experiments are carried out based on the sumo simulation platform. The results show that the queue length model based on the improved Kraus model can accurately calculate the queue length of the signalized intersection.
文章引用:寇子卿, 张洁, 张萌萌. 基于智能网联环境的信号交叉口排队长度估算模型研究[J]. 计算机科学与应用, 2023, 13(12): 2528-2535. https://doi.org/10.12677/CSA.2023.1312252

参考文献

[1] Muck, J. (2002) Using Detectors near the Stop-Line to Estimate Traffic Flows. Traffic Engineering & Control, 43, 429-434.
[2] Cho, H.J., Tseng, M.T. and Hwang, M.C. (2014) Using Detection of Vehicular Presence to Estimate Shockwave Speed and Upstream Traffics for a Signalized Intersection. Applied Mathematics and Computation, 232, 1151-1165. [Google Scholar] [CrossRef
[3] Comert, G. and Cetin, M. (2009) Queue Length Estimation from Probe Vehicle Location and the Impacts of Sample Size. European Journal of Operational Research, 197, 196-202. [Google Scholar] [CrossRef
[4] Ban, X.G., Hao, P. and Sun, Z.B. (2011) Real Time Queue Length Estimation for Signalized Intersections Using Travel Times from Mobile Sensors. Transportation Research Part C, 19, 1133-1156. [Google Scholar] [CrossRef
[5] Zhan, X., Li, R. and Ukkusuri, S.V. (2015) Lane-Based Real-Time Queue Length Estimation Using License Plate Recognition Data. Transportation Research Part C: Emerging Technolo-gies, 57, 85-102. [Google Scholar] [CrossRef
[6] Li, B., Cheng, W. and Li, L.S. (2018) Real-Time Prediction of Lane-Based Queue Lengths for Signalized Intersections. Journal of Advanced Transportation, 2018, Article ID: 5020518. [Google Scholar] [CrossRef
[7] 周学农. 排队长度模型比较及动态方法研究[J]. 交通运输系统工程与信息, 2006(1): 91-95.
[8] 孔涛, 刘新, 张茂雷. 信号控制交叉口排队长度估算方法研究[J].中国公共安全(学术版), 2015(2): 64-67.
[9] 赵淑芝, 梁士栋, 马明辉, 刘华胜, 朱永刚. 信号交叉口实时排队长度估计[J]. 吉林大学学报(工学版), 2016, 46(1): 85-91.
[10] 贾利民, 陈娜, 李海舰. 基于单个地磁传感器的交叉口排队长度估计[J]. 吉林大学学报(工学版), 2016, 46(3): 756-763.
[11] 冯毅文. 基于大规模车辆轨迹数据的道路交叉口排队长度探测[D]: [硕士学位论文]. 深圳: 深圳大学, 2017.
[12] 杨良义, 谢飞, 陈涛. 基于视频的交叉路口车辆排队长度检测方法研究[J]. 重庆理工大学学报(自然科学), 2018, 32(6): 169-174.
[13] 龚方徽, 朱海峰, 温熙华, 刘彦斌. 基于车头时距与行程时间的排队长度算法[J]. 交通科技, 2020(1): 84-87.
[14] 王宇林, 任安虎, 李珊. 一种基于深度学习的道路交叉口车辆排队长度检测方法[J]. 中国科技信息, 2023(4): 105-109.
[15] 唐进, 于文雅. 车辆轨迹数据驱动的道路交叉口排队长度探测[J]. 湖南交通科技, 2022, 48(3): 208-214.
[16] 金晨辉. 基于轨迹数据的信号交叉口排队长度估计研究[D]: [硕士学位论文]. 北京: 北方工业大学, 2023.
[17] 高宽. 混合交通环境下基于卡尔曼滤波的交叉口排队长度实时估计[D]: [硕士学位论文]. 成都: 西南交通大学, 2022.