[1]

Ke J, Feng S, Zhu Z, Yang H, Ye J. 2021. Joint predictions of multi-modal ride-hailing demands: a deep multi-task multi-graph learning-based approach. Transportation Research Part C: Emerging Technologies 127:103063

doi: 10.1016/j.trc.2021.103063
[2]

Li W, Zhou M, Dong H. 2020. CPT model-based prediction of the temporal and spatial distributions of passenger flow for urban rail transit under emergency conditions. Journal of Advanced Transportation 2020:8850541

doi: 10.1155/2020/8850541
[3]

Wang Y, Zheng D, Luo SM, Zhan DN, Nie P. 2013. The research of railway passenger flow prediction model based on BP neural network. Advanced Materials Research 605–607:2366−69

doi: 10.4028/www.scientific.net/amr.605-607.2366
[4]

Chien SIJ, Kuchipudi CM. 2003. Dynamic travel time prediction with real-time and historic data. Journal of Transportation Engineering 129(6):608−16

doi: 10.1061/(ASCE)0733-947X(2003)129:6(608)
[5]

Ge SY, Zheng CJ, Hou MM. 2013. Forecast of bus passenger traffic based on exponential smoothing and trend moving average method. Applied Mechanics and Materials 433:1374−78

[6]

Gu Y, Han Y, Fang XL. 2011. Method of hub station passenger flow forecasting based on ARMA model. Journal of Transport Information and Safety 29(2):5−9

doi: 10.3963/j.ISSN1674-4861.2011.02.002
[7]

Zhang J, Chen Y, Panchamy K, Jin G, Wang C, et al. 2023. Attention-based multi-step short-term passenger flow spatial-temporal integrated prediction model in URT systems. Journal of Geo-information Science 25(4):698−713

doi: 10.12082/dqxxkx.2023.220817
[8]

Roos J, Bonnevay S, Gavin G. 2018. Dynamic Bayesian networks with Gaussian mixture models for short-term passenger flow forecasting. In: Proceedings of the 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), Nanjing, China, 24-26 November 2017. USA: IEEE. pp. 1–8. doi: 10.1109/ISKE.2017.8258756

[9]

Zarei N, Ghayour MA, Hashemi S. 2013. Road traffic prediction using context-aware random forest based on volatility nature of traffic flows. Intelligent Information and Database Systems. ACIIDS 2013. Lecture Notes in Computer Science, vol 7802. Berlin, Heidelberg: Springer. pp. 196–205 doi: 10.1007/978-3-642-36546-1_21

[10]

Xue F, Yao E. 2022. Adopting a random forest approach to model household residential relocation behavior. Cities 125:103625

doi: 10.1016/j.cities.2022.103625
[11]

Long XQ, Li J, Chen YR. 2019. Metro short-term traffic flow prediction with deep learning. Control and Decision 34:1589−600

doi: 10.13195/j.kzyjc.2018.1393
[12]

Yang F, Song X, Xu F, Tsui KL. 2019. State-of-charge estimation of lithium-ion batteries via long short-term memory network. IEEE Access 7:53792−99

doi: 10.1109/ACCESS.2019.2912803
[13]

Shao H, Soong BH. 2016. Traffic flow prediction with Long Short-Term Memory Networks (LSTMs). 2016 IEEE Region 10 Conference (TENCON), Singapore, 22–25 November 2016. pp. 2986–89 doi: 10.1109/TENCON.2016.7848593

[14]

Ma X, Dai Z, He Z, Ma J, Wang Y, et al. 2017. Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction. Sensors 17(4):818

doi: 10.3390/s17040818
[15]

Wu Y, Tan H. 2016. Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework. arXiv

doi: 10.48550/arXiv.1612.01022
[16]

Qi Q, Cheng R, Ge H. 2023. Short-term inbound rail transit passenger flow prediction based on BILSTM model and influence factor analysis. Digital Transportation and Safety 2(1):12−22

doi: 10.48130/dts-2023-0002
[17]

Yu B, Lee Y, Sohn K. 2020. Forecasting road traffic speeds by considering area-wide spatio-temporal dependencies based on a graph convolutional neural network (GCN). Transportation Research Part C: Emerging Technologies 114:189−204

doi: 10.1016/j.trc.2020.02.013
[18]

Bogaerts T, Masegosa AD, Angarita-Zapata JS, Onieva E, Hellinckx P. 2020. A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data. Transportation Research Part C: Emerging Technologies 112:62−77

doi: 10.1016/j.trc.2020.01.010
[19]

Zhao L, Song Y, Zhang C, Liu Y, Wang P, et al. 2020. T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Transactions on Intelligent Transportation Systems 21(9):3848−58

doi: 10.1109/TITS.2019.2935152
[20]

Cui Z, Henrickson KC, Ke R, Wang Y. 2020. Traffic graph convolutional recurrent neural network: a deep learning framework for network-scale traffic learning and forecasting. IEEE Transactions on Intelligent Transportation Systems 21:4883−94

doi: 10.1109/TITS.2019.2950416
[21]

Zhang J, Chen F, Cui Z, Guo Y, Zhu Y. 2021. Deep learning architecture for short-term passenger flow forecasting in urban rail transit. IEEE Transactions on Intelligent Transportation Systems 22(11):1502−10

doi: 10.1109/TITS.2020.3000761
[22]

Xie Y, Zhang Y, Ye Z. 2007. Short-term traffic volume forecasting using Kalman filter with discrete wavelet decomposition. Computer-Aided Civil and Infrastructure Engineering 22(5):326−34

doi: 10.1111/j.1467-8667.2007.00489.x
[23]

Shang P, Li X, Kamae S. 2005. Chaotic analysis of traffic time series. Chaos, Solitons & Fractals 25(1):121−28

doi: 10.1016/j.chaos.2004.09.104
[24]

Famourzadeh V, Sefidkhosh M. 2019. Straddling between determinism and randomness: chaos theory vis-a-vis Leibniz. arXiv 1909.13635v1

doi: 10.48550/arXiv.1909.13635
[25]

Hsieh Da. 1991. Chaos and nonlinear dynamics: application to financial markets. The Journal of Finance 46(5):1839−77

doi: 10.1111/j.1540-6261.1991.tb04646.x
[26]

Kang Y, Li X, Lu Y, Yang C. 2008. Application of chaotic phase space reconstruction into nonlinear time series prediction in deep rock mass. Proc. 5th International Symposium on Knowledge Discovery and Data Mining (FSKD), Jinan, China, 18-20 October 2008. USA: IEEE. pp. 593–97 doi: 10.1109/FSKD.2008.423

[27]

Takens F. 1981. Detecting strange attractors in turbulence. Dynamical Systems and Turbulence, Warwick 1980. Lecture Notes in Mathematics. vol 898. Berlin, Heidelberg: Springer. pp. 366−81 doi: 10.1007/BFb0091924

[28]

Kugiumtzis D. 1996. State space reconstruction parameters in the analysis of chaotic time series-the role of the time window length. Physica D: Nonlinear Phenomena 95(1):13−28

doi: 10.1016/0167-2789(96)00054-1
[29]

Shi Z, Zhang N, Schonfeld PM, Zhang J. 2020. Short-term metro passenger flow forecasting using ensemble-chaos support vector regression. Transportmetrica A: Transport Science 16(2):194−212

doi: 10.1080/23249935.2019.1692956
[30]

Jin J, Xu Z, Li C, Miao W, Xiao J, et al. 2022. Rolling bearing fault diagnosis based on deep learning and chaotic feature fusion. Control Theory & Applications 39(1):109−16

doi: 10.7641/CTA.2021.10177
[31]

Zhang WC, Tan SC, Gao PZ. 2013. Chaotic forecasting of natural circulation flow instabilities under rolling motion based on Lyapunov exponents. Acta Physica Sinica 62(6):060502

doi: 10.7498/aps.62.060502
[32]

Smale S. 1967. Differentiable dynamical systems. Bulletin of the American Mathematical Society 73(6):747−817

doi: 10.1090/s0002-9904-1967-11798-1
[33]

Sterman JD. 1988. Deterministic chaos in models of human behavior: methodological issues and experimental results. System Dynamics Review 4(1):148−78

doi: 10.1002/sdr.4260040109
[34]

Packard N, Crutchfield JP, Shaw R. 1980. Deterministic chaos in dynamical systems. Physical Review Letters 45:712

doi: 10.1103/PhysRevLett.45.712
[35]

Martinerie JM, Albano AM, Mees AI, Rapp PE. 1992. Chaos and dynamics of a time-delayed system. Physical Review A 45:7058

doi: 10.1103/PhysRevA.45.7058
[36]

Liangyue C. 1997. Nonlinear dynamics of a time-delay system. Physica D: Nonlinear Phenomena 110:43

doi: 10.1016/S0167-2789(97)00118-8
[37]

Mirjalili S, Mirjalili SM, Lewis A. 2014. Grey wolf optimizer. Advances in Engineering Software 69:46−61

doi: 10.1016/j.advengsoft.2013.12.007
[38]

Gu R, Chen J, Hong R, Wang H, Wu W. 2020. Incipient fault diagnosis of rolling bearings based on adaptive variational mode decomposition and Teager energy operator. Measurement 149:106941

doi: 10.1016/j.measurement.2019.106941
[39]

Xiong J, Sun Y, Sun J, Wan Y, Yu G. 2024. Sparse temporal data-driven SSA-CNN-LSTM-based fault prediction of electromechanical equipment in rail transit stations. Applied Sciences 14(18):8156

doi: 10.3390/app14188156
[40]

Gottam S, Nanda SJ, Maddila RK. 2021. A CNN-LSTM model trained with grey wolf optimizer for prediction of household power consumption. 2021 IEEE International Symposium on Smart Electronic Systems (iSES), Jaipur, India, 18−22 December 2021, Jaipur, India. pp. 355 doi: 10.1109/iSES52644.2021.00089