基于复杂网络的交通序列数据特性

doi:10.3976/j.issn.1002-4026.20230058

Abstract

Abstract:

To study the traffic flow characteristics, the traffic data is analyzed using a complex network method. A box plot-clustering algorithm model is proposed to identify and fill in missing values and outliers in the initial data. The one-dimensional data is reconstructed into network nodes using the phase space reconstruction method. Additionally, the connection threshold is selected to determine the connection relationship of network nodes to convert the traffic sequence data as a complex network and analyze the structure and quantitative indicators of the network. The result shows that the structure of the complex network of traffic data can reflect the traffic flow state of the road section to a certain extent. The research optimizes the data preprocessing method and extends the application of complex networks into traffic data research.

Key words: complex networks, data analysis, network building methods, phase space reconstruction, clustering algorithm

CLC Number:

U491

MENG Bo, KONG Xiangke, LI Shubin. The characteristics of traffic sequence data based on complex network[J].Shandong Science, 2024, 37(1): 107-117.

Figures/Tables 12

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References 20

[1]	武琼. 基于支持向量回归的短时交通流预测方法研究与应用[D]. 西安: 长安大学, 2016.
[2]	赵怀柏, 王逸凡, 宋晓鹏. 基于遗传算法优化BP神经网络的交通流预测[J]. 交通与运输(学术版), 2017(2): 32-36.
[3]	MA X L, TAO Z M, WANG Y H, et al. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data[J]. Transportation Research Part C: Emerging Technologies, 2015, 54: 187-197. DOI: 10.1016/j.trc.2015.03.014.
[4]	STRANO E, VIANA M, da FONTOURA COSTA L, et al. Urban street networks, a comparative analysis of ten European cities[J]. Environment and Planning B: Planning and Design, 2013, 40(6): 1071-1086. DOI: 10.1068/b38216.
[5]	叶彭姚. 城市道路网拓扑结构的复杂网络特性研究[J]. 交通运输工程与信息学报, 2012, 10(1): 13-19. DOI: 10.3969/j.issn.1672-4747.2012.01.003.
[6]	LU H P, SHI Y. Complexity of public transport networks[J]. Tsinghua Science & Technology, 2007, 12(2): 204-213. DOI: 10.1016/S1007-0214(07)70029-9.
[7]	许晴, 祖正虎, 徐致靖, 等. 330个中国城市P空间下公交复杂网络实证研究[J]. 交通运输系统工程与信息, 2013, 13(1): 193-198. DOI: 10.16097/j.cnki.1009-6744.2013.01.001.
[8]	YANG H H, AN S. Robustness evaluation for multi-subnet composited complex network of urban public transport[J]. Alexandria Engineering Journal, 2021, 60(2): 2065-2074. DOI: 10.1016/j.aej.2020.12.016.
[9]	WANG N, LI D, WANG Q W. Visibility graph analysis on quarterly macroeconomic series of China based on complex network theory[J]. Physica A: Statistical Mechanics and Its Applications, 2012, 391(24): 6543-6555. DOI: 10.1016/j.physa.2012.07.054.
[10]	GAO Z K, JIN N D. Complex network from time series based on phase space reconstruction[J]. Chaos: an Interdisciplinary Journal of Nonlinear Science, 2009, 19(3): 033137. DOI: 10.1063/1.3227736.
[11]	MAO S Z, XIAO F Y. Time series forecasting based on complex network analysis[J]. IEEE Access, 2019, 7: 40220-40229. DOI: 10.1109/ACCESS.2019.2906268.
[12]	邹晓芳. 城市快速路交通流故障数据修复方法研究[D]. 北京: 北京交通大学, 2014.
[13]	苗旭, 王忠宇, 邹亚杰, 等. 改进的固定交通检测器缺失数据综合修复方法[J]. 同济大学学报(自然科学版), 2019, 47 (10): 1477-1484. DOI: 10.11908/j.issn.0253-374x.2019.10.013.
[14]	姜桂艳, 冮龙晖, 张晓东, 等. 动态交通数据故障识别与修复方法[J]. 交通运输工程学报, 2004(01): 121-125. DOI: 10.3321/j.issn:1671-1637.2004.01.030.
[15]	孟力, 毕叶平. 相空间重构文献综述可视化分析[J]. 系统仿真学报, 2017, 29(12): 3167-3175. DOI: 10.16182/j.issn1004731x.joss.201712030.
[16]	李媛媛. 基于相空间重构和SVR的短时间交通流预测方法研究[D]. 北京: 北京交通大学, 2018.
[17]	CAO L Y. Practical method for determining the minimum embedding dimension of a scalar time series[J]. Physica D: Nonlinear Phenomena, 1997, 110(1/2): 43-50. DOI: 10.1016/S0167-2789(97)00118-8.
[18]	TANG J J, WANG Y H, LIU F. Characterizing traffic time series based on complex network theory[J]. Physica A: Statistical Mechanics and Its Applications, 2013, 392(18): 4192-4201. DOI: 10.1016/j.physa.2013.05.012.
[19]	TANG J J, WANG Y H, WANG H, et al. Dynamic analysis of traffic time series at different temporal scales: a complex networks approach[J]. Physica A: Statistical Mechanics and Its Applications, 2014, 405: 303-315. DOI: 10.1016/j.physa.2014.03.038.
[20]	KERNER B S. The physics of traffic[J]. Physics World, 1999, 12(8): 25-30. DOI: 10.1088/2058-7058/12/8/30.

道路及时长	宽运动阻塞流状态速度平均值	同步流状态速度平均值	自由流状态速度平均值
道路一,一日	22.7	36.8	45.0
道路一,一周	20.9	36.5	45.1
道路二,一日	28.3	44.9	52.2
道路二,一周	26.8	45.2	53.4

道路及时长	延迟时间	嵌入维数
道路一,一日	4	5
道路一,一周	6	5
道路二,一日	4	6
道路二,一周	8	8

道路及时长	平均度	聚类系数	介数
道路一,一日	17.379	0.842	0.235
道路一,一周	67.354	0.660	0.075
道路二,一日	8.073	0.770	0.244
道路二,一周	40.945	0.348	0.046

The characteristics of traffic sequence data based on complex network

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