• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Turn off MathJax
Article Contents
ZHAO Xueting, HU Liwei. Comprehensive Comparison of Inversion Performance of Urban Traffic Congestion Source Parameters[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230008
Citation: ZHAO Xueting, HU Liwei. Comprehensive Comparison of Inversion Performance of Urban Traffic Congestion Source Parameters[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230008

Comprehensive Comparison of Inversion Performance of Urban Traffic Congestion Source Parameters

doi: 10.3969/j.issn.0258-2724.20230008
  • Received Date: 03 Jan 2023
  • Rev Recd Date: 06 Jun 2023
  • Available Online: 13 Nov 2024
  • In order to accurately grasp the main parameters and diffusion and propagation laws inherent in urban traffic congestion sources and realize scientific control of traffic congestion sources, the Gaussian plume model for air pollutant dispersion was introduced and improved. The urban traffic congestion sources were divided into continuous traffic flow and a series of intermittent traffic flow, so as to realize the structural analysis of the Gaussian plume model. Then, three test functions, namely Griewank, Schaffer, and Rastrigin were used to test the “single point source” parameter inversion algorithm, and the seeker optimization algorithm was selected. Finally, the performance of the five typical objective functions with different numbers of parameters (one, two, and three) was evaluated in three dimensions based on the observed traffic congestion source data. The results show that in the one-parameter case, the stability of the objective function based on the sum of squared deviations of traffic density per unit area is better, and the confidence interval of the absolute value of the relative deviation of the inverse source strength is 38.38% ± 9.94%; the number of experiments less than 50% accounts for 84.52% of all experiments, and the stability of each objective function is better. In the two-parameter case of source strength inversion, the accuracy of the objective function based on the root-mean-square error of traffic density per unit area in the form of logarithmic transformation is the highest, and the confidence interval of the absolute value of the relative deviation of source strength inversion is 51.42% ± 9.84%; the number of experiments less than 50% accounts for 92.16% of all experiments. In terms of inversion location, the accuracy of the objective function based on the sum of squared deviations of traffic density per unit area is the best (The absolute value of position deviation inversion is 37.22 m ± 10.64 m), and the stability of the objective function based on correlation coefficient is the strongest (coefficient of variation is 0.022). In the three-parameter case, the accuracy inversion results are more consistent with those in the two-parameter case, and the source strength stability of each objective function is poor except for the objective function in the form of logarithmic transformation, but the position stability is better.

     

  • loading
  • [1]
    ZHANG Sen, YAO Yong, HU J, et al. Deep autoencoder neural networks for short-term traffic congestion prediction of transportation networks[J]. Sensors, 2019, 19(10): 2229.1-2229.19. doi: 10.3390/s19102229
    [2]
    PI Mingyu, YEON H, SON H, et al. Visual cause analytics for traffic congestion[J]. IEEE Transactions on Visualization and Computer Graphics, 2019, 27(3): 2186-2201. doi: 10.1109/TVCG.2019.2940580
    [3]
    CHEN Hengrui, ZHOU Ruiyu, CHEN Hong, et al. A resilience-oriented evaluation and identification of critical thresholds for traffic congestion diffusion[J]. Physica A: Statistical Mechanics and its Applications, 2022, 600(8): 127592.1-127592.15. doi: 10.1016/j.physa.2022.127592
    [4]
    ZHU Shixiang, DING Ruyi, ZHANG Minghe, et al. Spatio-temporal point processes with attention for traffic congestion event modeling[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(7): 7298-7309. doi: 10.1109/TITS.2021.3068139
    [5]
    CHEN Yuting, MAO Jiannan, ZHANG Zhao, et al. A quasi-contagion process modeling and characteristic analysis for real-world urban traffic network congestion patterns[J]. Physica A: Statistical Mechanics and its Applications, 2022, 603(10): 127729.1-127729.17. doi: /10.1016/j.physa.2022.127729
    [6]
    陈美林,郑治豪,郭宝,等. 基于因果关联的交通拥堵传播分析[J]. 中南大学学报 (自然科学版),2020,51(12): 3575-3583. doi: 10.11817/j.issn.1672-7207.2020.12.031

    CHEN Meilin, ZHENG Zhihao, GUO Bao, et al. Traffic congestion spreading analysis based on causal nexus[J]. Journal of Central South University (Science and Technology), 2020, 51(12): 3575-3583. doi: 10.11817/j.issn.1672-7207.2020.12.031
    [7]
    石敏,蔡少委,易清明. 基于空洞-稠密网络的交通拥堵预测模型[J]. 上海交通大学学报,2021,55(2): 124-130. doi: 10.16183/j.cnki.jsjtu.2020.99.009

    SHI Min, CAI Shaowei, YI Qingming, et al. A traffic congestion prediction model based on dilated-dense network[J]. Journal of Shanghai Jiao Tong University, 2021, 55(2): 124-130. doi: 10.16183/j.cnki.jsjtu.2020.99.009
    [8]
    曾筠程,邵敏华,孙立军,等. 基于有向图卷积神经网络的交通预测与拥堵管控[J]. 中国公路学报,2021,34(12): 239-248. doi: 10.3969/j.issn.1001-7372.2021.12.018

    ZENG Yuncheng, SHAO Minhua, SUN Lijun, et al. Traffic prediction and congestion control based on directed graph convolution neural network[J]. China Journal of Highway and Transport, 2021, 34(12): 239-248. doi: 10.3969/j.issn.1001-7372.2021.12.018
    [9]
    周辉宇,李瑞敏,黄安强,等. 基于时空关联规则挖掘的城市交通拥堵传导预测[J]. 系统工程理论与实践,2022,42(8): 2210-2223. doi: 10.12011/SETP2020-2752

    ZHOU Huiyu, LI Ruimin, HUANG Anqiang, et al. Forecasting urban traffic congestion conduction based on spatiotemporal association rule mining[J]. Systems Engineering-Theory & Practice, 2022, 42(8): 2210-2223. doi: 10.12011/SETP2020-2752
    [10]
    梁军,彭嘉恒. 考虑路网拓扑时变的交通拥堵自适应预测方法研究[J]. 中国公路学报,2022,35(9): 157-170. doi: 10.19721/j.cnki.1001-7372.2022.09.012

    LIANG Jun, PEN Jiaheng. Research on an adaptive traffic congestion prediction method considering a time-varying network topology[J]. China Journal of Highway and Transport, 2022, 35(9): 157-170. doi: 10.19721/j.cnki.1001-7372.2022.09.012
    [11]
    马庆禄,牛圣平,曾皓威,等. 网联环境下混合交通流偶发拥堵演化机理研究[J]. 交通运输系统工程与信息,2022,22(5): 97-106. doi: 10.16097/j.cnki.1009-6744.2022.05.010

    MA Qinglu, NIU Shengping, ZENG Haowei, et al. Mechanism of non-recurring congestion evolution under mixed traffic flow with connected and autonomous vehicles[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(5): 97-106. doi: 10.16097/j.cnki.1009-6744.2022.05.010
    [12]
    孙超,尹浩为,张玮,等. 基于有限理性的交通网络可靠性均衡模型[J]. 西南交通大学学报,2023,58(1): 83-90. doi: 10.3969/j.issn.0258-2724.20210679

    SUN Chao, YIN Haowei, ZHANG Wei, et al. Traffic equilibrium model of reliable network based on bounded rationality[J]. Journal of Southwest Jiaotong University, 2023, 58(1): 83-90. doi: 10.3969/j.issn.0258-2724.20210679
    [13]
    王丽,刘小明,任福田,等. 烟羽模型在交通影响分析中的应用[J]. 公路交通科技,2001,18(6): 82-85.

    WANG Li, LIU Xiaoming, REN Futian, et al. The application of cloud model in traffic impact analysis[J]. Journal of Highway and Transportation Research and Development, 2001, 18(6): 82-85.
    [14]
    胡立伟,杨锦青,何越人,等. 城市交通拥塞辐射模型及其对路网服务能力损伤研究[J]. 中国公路学报,2019,32(3): 145-154. doi: 10.19721/j.cnki.1001-7372.2019.03.0016

    HU Liwei, YANG Jinqing, HE Yueren, et al. Urban traffic congestion radiation model and damage caused to service capacity of road network[J]. China Journal of Highway and Transport, 2019, 32(3): 145-154. doi: 10.19721/j.cnki.1001-7372.2019.03.0016
    [15]
    刘立群,韩俊英,代永强,等. 果蝇优化算法优化性能对比研究[J]. 计算机技术与发展,2015,25(8): 94-98. doi: 10.3969/j.issn.1673-629X.2015.08.020

    LIU Liqun, HAN Junying, DAI Yongqiang, et al. Comparative study on optimization performance of fruit fly optimization algorithm[J]. Computer Technology and Development, 2015, 25(8): 94-98. doi: 10.3969/j.issn.1673-629X.2015.08.020
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(12)  / Tables(2)

    Article views(51) PDF downloads(5) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return