[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.
|