Twice clustering based hybrid model for short-term passenger flow forecasting
Abstract
Short-term metro passenger flow prediction plays a great role in traffic planning and management, and it is an important prerequisite for achieving intelligent transportation. So, a novel hybrid Support Vector Regression (SVR) model based on Twice Clustering (TC) is proposed for short-term metro passenger flow prediction. The training sets and test sets are generated by TC with respect to values of passenger flow in different time periods to improve the prediction accuracy. Furthermore, each obtained cluster is decomposed by using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm and the Ensemble Empirical Mode Decomposition (EEMD) algorithm, respectively. The volatility of each component obtained after decomposition is further reduced. Then, the SVR model optimized by the Grey Wolf Optimization (GWO) algorithm is used to predict the decomposed components. Moreover, forecast based on one-month data from Xi’an Metro Line 2 Library Station (China). By comparing the prediction results of the TC condition, the Once Clustering (OC) condition and the non-clustering condition, it shows that the TC approach can adequately model the volatility and effectively improve the prediction accuracy. At the same time, experimental results show that the novel hybrid TC–CEEMDAN–GWO–SVR model has superior performance than Genetic Algorithm (GA) optimized SVR (SVR–GA) model and hybrid Back Propagation Neural Network (BPNN) model.
Keyword : short-term passenger flow forecasting, twice clustering, support vector regression, grey wolf optimization, complete ensemble empirical mode decomposition, adaptive noise
How to Cite
Wang, S., & Yang, X. (2024). Twice clustering based hybrid model for short-term passenger flow forecasting. Transport, 39(3), 209–228. https://doi.org/10.3846/transport.2024.20538
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Sulaiman, M. H.; Mustaffa, Z.; Mohamed, M. R.; Aliman, O. 2015. Using the gray wolf optimizer for solving optimal reactive power dispatch problem, Applied Soft Computing 32: 286–292. https://doi.org/10.1016/j.asoc.2015.03.041
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Van Der Voort, M.; Dougherty, M.; Watson, S. 1996. Combining Kohonen maps with ARIMA time series models to forecast traffic flow, Transportation Research Part C: Emerging Technologies 4(5): 307–318. https://doi.org/10.1016/S0968-090X(97)82903-8
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Zhang, G. P. 2003. Time series forecasting using a hybrid ARIMA and neural network model, Neurocomputing 50: 159–175. https://doi.org/10.1016/S0925-2312(01)00702-0
Zhang, G.; Patuwo, B. E.; Hu, M. Y. 1998. Forecasting with artificial neural networks: the state of the art, International Journal of Forecasting 14(1): 35–62. https://doi.org/10.1016/S0169-2070(97)00044-7
Zhang, H.; Wang, X.; Cao, J.; Tang, M.; Guo, Y. 2018. A hybrid short-term traffic flow forecasting model based on time series multifractal characteristics, Applied Intelligence 48(8): 2429–2440. https://doi.org/10.1007/s10489-017-1095-9
Zhong, C.; Batty, M.; Manley, E.; Wang, J.; Wang, Z.; Chen, F.; Schmitt, G. 2016. Variability in regularity: mining temporal mobility patterns in London, Singapore and Beijing using smart-card data, PLoS ONE 11(2): e0149222. https://doi.org/10.1371/journal.pone.0149222
Zhu, S.; Qiu, X.; Yin, Y.; Fang, M.; Liu, X.; Zhao, X.; Shi, Y. 2019. Two-step-hybrid model based on data preprocessing and intelligent optimization algorithms (CS and GWO) for NO2 and SO2 forecasting, Atmospheric Pollution Research 10(4): 1326–1335. https://doi.org/10.1016/j.apr.2019.03.004
CURTA. 2019. Urban Rail Transit 2018 Statistics and Analysis Report. China Urban Rail Transit Association (CURTA), China. (in Chinese).
Cybenko, G. 1989. Approximation by superpositions of a sigmoidal function, Mathematics of Control, Signals and Systems 2(4): 303–314. https://doi.org/10.1007/BF02551274
Deng, Y.; Xiang, J.; Ou, Z. 2012. SVR with hybrid chaotic genetic algorithm for short-term traffic flow forecasting, in 2012 8th International Conference on Natural Computation, 29–31 May 2012, Chongqing, China, 708–712. https://doi.org/10.1109/ICNC.2012.6234768
Hamed, M. M.; Al-Masaeid, H. R.; Said, Z. M. B. 1995. Short-term prediction of traffic volume in urban arterials, Journal of Transportation Engineering 121(3): 249–254. https://doi.org/10.1061/(ASCE)0733-947X(1995)121:3(249)
Hao, S.; Lee, D.-H.; Zhao, D. 2019. Sequence to sequence learning with attention mechanism for short-term passenger flow prediction in large-scale metro system, Transportation Research Part C: Emerging Technologies 107: 287–300. https://doi.org/10.1016/j.trc.2019.08.005
Heydari, A.; Astiaso Garcia, D.; Keynia, F.; Bisegna, F.; De Santoli, L. 2019. Renewable energies generation and carbon dioxide emission forecasting in microgrids and national grids using GRNN-GWO methodology, Energy Procedia 159: 154–159. https://doi.org/10.1016/j.egypro.2018.12.044
Hochreiter, S.; Schmidhuber, J. 1997. Long short-term memory, Neural Computation 9(8): 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Hong, W.-C. 2011. Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm, Neurocomputing 74(12–13): 2096–2107. https://doi.org/10.1016/j.neucom.2010.12.032
Hu, Y.; Wu, C.; Liu, H. 2011. Prediction of passenger flow on the highway based on the least square support vector machine, Transport 26(2): 197–203. https://doi.org/10.3846/16484142.2011.593121
Huang, N. E.; Shen, Z.; Long, S. R.; Wu, M. C.; Shih, H. H.; Zheng, Q.; Yen, N.-C.; Tung, C. C.; Liu, H. H. 1998. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 454(1971): 903–995. https://doi.org/10.1098/rspa.1998.0193
Kamarianakis, Y, Prastacos, P. 2005. Space–time modeling of traffic flow, Computers & Geosciences 31(2): 119–133. https://doi.org/10.1016/j.cageo.2004.05.012
Kumar, S. V.; Vanajakshi, L. 2015. Short-term traffic flow prediction using seasonal ARIMA model with limited input data, European Transport Research Review 7: 21. https://doi.org/10.1007/s12544-015-0170-8
Lee, S.; Fambro, D. B. 1999. Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting, Transportation Research Record: Journal of the Transportation Research Board 1678: 179–188. https://doi.org/10.3141/1678-22
Lei, Y.; Lin, J.; He, Z; Zuo, M. J. 2013. A review on empirical mode decomposition in fault diagnosis of rotating machinery, Mechanical Systems and Signal Processing 35(1–2): 108–126. https://doi.org/10.1016/j.ymssp.2012.09.015
Li, H; Liu, T.; Wu, X.; Chen, Q. 2019a. Application of EEMD and improved frequency band entropy in bearing fault feature extraction, ISA Transactions 88: 170–185. https://doi.org/10.1016/j.isatra.2018.12.002
Li, H.; Wang, Y.; Xu, X.; Qin, L.; Zhang, H. 2019b. Short-term passenger flow prediction under passenger flow control using a dynamic radial basis function network, Applied Soft Computing 83: 105620. https://doi.org/10.1016/j.asoc.2019.105620
Li, M.-W.; Hong, W.-C.; Kang, H.-G. 2013. Urban traffic flow forecasting using Gauss–SVR with cat mapping, cloud model and PSO hybrid algorithm, Neurocomputing 99: 230–240. https://doi.org/10.1016/j.neucom.2012.08.002
Liu, L.; Chen, R.-C. 2017. A novel passenger flow prediction model using deep learning methods, Transportation Research Part C: Emerging Technologies 84: 74–91. https://doi.org/10.1016/j.trc.2017.08.001
Ma, X.; Tao, Z.; Wang, Yu.; Yu, H.; Wang, Yu. 2015. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data, Transportation Research Part C: Emerging Technologies 54: 187–197. https://doi.org/10.1016/j.trc.2015.03.014
Mirjalili, S.; Mirjalili, S. M.; Lewis, A. 2014. Grey wolf optimizer, Advances in Engineering Software 69: 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007
Mrówczyńska, B.; Łachacz, K.; Haniszewski, T.; Sładkowski, A. 2012. A comparison of forecasting the results of road transportation needs, Transport 27(1): 73–78. https://doi.org/10.3846/16484142.2012.666763
Niu, M.; Wang, Y.; Sun, S.; Li, Y. 2016. A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting, Atmospheric Environment 134: 168–180. https://doi.org/10.1016/j.atmosenv.2016.03.056
Plakandaras, V.; Papadimitriou, T.; Gogas, P. 2019. Forecasting transportation demand for the U.S. market, Transportation Research Part A: Policy and Practice 126: 195–214. https://doi.org/10.1016/j.tra.2019.06.008
Ramakrishnan, T.; Sankaragomathi, B. 2017. A professional estimate on the computed tomography brain tumor images using SVM-SMO for classification and MRG-GWO for segmentation, Pattern Recognition Letters 94: 163–171. https://doi.org/10.1016/j.patrec.2017.03.026
Ren, G.; Zhou, Z. 2011. Traffic safety forecasting method by particle swarm optimization and support vector machine, Expert Systems with Applications 38(8): 10420–10424. https://doi.org/10.1016/j.eswa.2011.02.066
Rumelhart, D. E.; Hinton, G. E.; Williams, R. J. 1986. Learning representations by back-propagating errors, Nature 323: 533–536. https://doi.org/10.1038/323533a0
Schölkopf, B.; Smola, A. J. 2001. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press. 644 p. https://doi.org/10.7551/mitpress/4175.001.0001
Sulaiman, M. H.; Mustaffa, Z.; Mohamed, M. R.; Aliman, O. 2015. Using the gray wolf optimizer for solving optimal reactive power dispatch problem, Applied Soft Computing 32: 286–292. https://doi.org/10.1016/j.asoc.2015.03.041
Šliupas, T. 2006. Annual average daily traffic forecasting using different techniques, Transport 21(1): 38–43. https://doi.org/10.3846/16484142.2006.9638039
Van Der Voort, M.; Dougherty, M.; Watson, S. 1996. Combining Kohonen maps with ARIMA time series models to forecast traffic flow, Transportation Research Part C: Emerging Technologies 4(5): 307–318. https://doi.org/10.1016/S0968-090X(97)82903-8
Vlahogianni, E. I.; Karlaftis, M. G.; Golias, J. C. 2014. Short-term traffic forecasting: where we are and where we’re going, Transportation Research Part C: Emerging Technologies 43: 3–19. https://doi.org/10.1016/j.trc.2014.01.005
Wang, J.; Shi, Q. 2013. Short-term traffic speed forecasting hybrid model based on chaos–wavelet analysis-support vector machine theory, Transportation Research Part C: Emerging Technologies 27: 219–232. https://doi.org/10.1016/j.trc.2012.08.004
Wang, K.; Qi, X.; Liu, H.; Song, J. 2018. Deep belief network based k-means cluster approach for short-term wind power forecasting, Energy 165: 840–852. https://doi.org/10.1016/j.energy.2018.09.118
Williams, B M. 2001. Multivariate vehicular traffic flow prediction: evaluation of ARIMAX modeling, Transportation Research Record: Journal of the Transportation Research Board 1776: 194–200. https://doi.org/10.3141/1776-25
Wu, Y.-X.; Wu, Q.-B.; Zhu, J.-Q. 2019. Improved EEMD-based crude oil price forecasting using LSTM networks, Physica A: Statistical Mechanics and its Applications 516: 114–124. https://doi.org/10.1016/j.physa.2018.09.120
Wu, Z.; Huang, N. E. 2009. Ensemble empirical mode decomposition: a noise-assisted data analysis method, Advances in Adaptive Data Analysis 1(1): 1–41. https://doi.org/10.1142/S1793536909000047
Yang, H.-F.; Dillon, T. S.; Chen, Y.-P. P. 2017. Optimized structure of the traffic flow forecasting model with a deep learning approach, IEEE Transactions on Neural Networks and Learning Systems 28(10): 2371–2381. https://doi.org/10.1109/TNNLS.2016.2574840
Yang, X.; Xue, Q.; Ding, M.; Wu, J.; Gao, Z. 2021a. Short-term prediction of passenger volume for urban rail systems: a deep learning approach based on smart-card data, International Journal of Production Economics 231: 107920. https://doi.org/10.1016/j.ijpe.2020.107920
Yang, X.; Xue, Q.; Yang, X.-X.; Yin, H.; Qu, Y.; Li, X.; Wu, J. 2021b. A novel prediction model for the inbound passenger flow of urban rail transit, Information Sciences 566: 347–363. https://doi.org/10.1016/j.ins.2021.02.036
Zhang, G. P. 2003. Time series forecasting using a hybrid ARIMA and neural network model, Neurocomputing 50: 159–175. https://doi.org/10.1016/S0925-2312(01)00702-0
Zhang, G.; Patuwo, B. E.; Hu, M. Y. 1998. Forecasting with artificial neural networks: the state of the art, International Journal of Forecasting 14(1): 35–62. https://doi.org/10.1016/S0169-2070(97)00044-7
Zhang, H.; Wang, X.; Cao, J.; Tang, M.; Guo, Y. 2018. A hybrid short-term traffic flow forecasting model based on time series multifractal characteristics, Applied Intelligence 48(8): 2429–2440. https://doi.org/10.1007/s10489-017-1095-9
Zhong, C.; Batty, M.; Manley, E.; Wang, J.; Wang, Z.; Chen, F.; Schmitt, G. 2016. Variability in regularity: mining temporal mobility patterns in London, Singapore and Beijing using smart-card data, PLoS ONE 11(2): e0149222. https://doi.org/10.1371/journal.pone.0149222
Zhu, S.; Qiu, X.; Yin, Y.; Fang, M.; Liu, X.; Zhao, X.; Shi, Y. 2019. Two-step-hybrid model based on data preprocessing and intelligent optimization algorithms (CS and GWO) for NO2 and SO2 forecasting, Atmospheric Pollution Research 10(4): 1326–1335. https://doi.org/10.1016/j.apr.2019.03.004